=LDR 03286nas a2200733 i 4500 =001 SSMS001 =003 IN-ChSCO =005 20170624061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 170624c20179999pau|||||o|||||||||||eng|| =022 \\$a2572-3928 =022 \\$z2520-6478 =030 \\$aSSMSCY =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA174 =082 04$a333.793$223 =245 00$aSmart and Sustainable Manufacturing Systems. =246 3\$aSSMS =246 3\$aASTM International Smart and Sustainable Manufacturing Systems =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2017- =310 \\$aAnnual =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =362 0\$aVol. 1, Issue 1 (January 2017)- =588 \\$aDescription based on: Volume 1, Issue 1; title from table of contents page (publisher's website, viewed June 24, 2017). =588 \\$aLatest issue consulted: Volume 1, Issue 1 (viewed June 24, 2017). =650 \0$aEngineering design. =650 \0$aInformation science. =650 \0$aSystems engineering. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/TOC/112017.htm =LDR 03286nas a2200733 i 4500 =001 SSMS002 =003 IN-ChSCO =005 20180405061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 180405c20179999pau|||||o|||||||||||eng|| =022 \\$a2572-3928 =022 \\$z2520-6478 =030 \\$aSSMSCY =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA174 =082 04$a333.793$223 =245 00$aSmart and Sustainable Manufacturing Systems. =246 3\$aSSMS =246 3\$aASTM International Smart and Sustainable Manufacturing Systems =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2017- =310 \\$aAnnual =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =362 0\$aVol. 1, Issue 1 (January 2017)- =588 \\$aDescription based on: Volume 2, Issue 1; title from table of contents page (publisher's website, viewed April 24, 2018). =588 \\$aLatest issue consulted: Volume 2, Issue 1 (viewed April 05, 2018). =650 \0$aEngineering design. =650 \0$aInformation science. =650 \0$aSystems engineering. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/TOC/212018.htm =LDR 03286nas a2200733 i 4500 =001 SSMS003 =003 IN-ChSCO =005 20190211061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190211c20179999pau|||||o|||||||||||eng|| =022 \\$a2572-3928 =022 \\$z2520-6478 =030 \\$aSSMSCY =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA174 =082 04$a333.793$223 =245 00$aSmart and Sustainable Manufacturing Systems. =246 3\$aSSMS =246 3\$aASTM International Smart and Sustainable Manufacturing Systems =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2017- =310 \\$aAnnual =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =362 0\$aVol. 1, Issue 1 (January 2017)- =588 \\$aDescription based on: Volume 1, Issue 1 (January 2017); title from table of contents page (publisher's website, viewed April 24, 2018). =588 \\$aLatest issue consulted: Volume 2, Issue 2 (viewed February 11, 2019). =650 \0$aEngineering design. =650 \0$aInformation science. =650 \0$aSystems engineering. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/TOC/222018.htm =LDR 03286nas a2200733 i 4500 =001 SSMS004 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125c20179999pau|||||o|||||||||||eng|| =022 \\$a2572-3928 =022 \\$z2520-6478 =030 \\$aSSMSCY =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA174 =082 04$a333.793$223 =245 00$aSmart and Sustainable Manufacturing Systems. =246 3\$aSSMS =246 3\$aASTM International Smart and Sustainable Manufacturing Systems =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2017- =310 \\$aAnnual =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =362 0\$aVol. 1, Issue 1 (January 2017)- =588 \\$aDescription based on: Volume 2, Issue 1; title from table of contents page (publisher's website, viewed April 24, 2018). =588 \\$aLatest issue consulted: Volume 3, Issue 1 (viewed January 25, 2020). =650 \0$aEngineering design. =650 \0$aInformation science. =650 \0$aSystems engineering. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/TOC/312019.htm =LDR 03286nas a2200733 i 4500 =001 SSMS005 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125c20179999pau|||||o|||||||||||eng|| =022 \\$a2572-3928 =022 \\$z2520-6478 =030 \\$aSSMSCY =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA174 =082 04$a333.793$223 =245 00$aSmart and Sustainable Manufacturing Systems. =246 3\$aSSMS =246 3\$aASTM International Smart and Sustainable Manufacturing Systems =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2017- =310 \\$aAnnual =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =362 0\$aVol. 1, Issue 1 (January 2017)- =588 \\$aDescription based on: Volume 2, Issue 1; title from table of contents page (publisher's website, viewed April 24, 2018). =588 \\$aLatest issue consulted: Volume 3, Issue 2 (viewed January 25, 2020). =650 \0$aEngineering design. =650 \0$aInformation science. =650 \0$aSystems engineering. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/TOC/322019.htm =LDR 03286nas a2200733 i 4500 =001 SSMS006 =003 IN-ChSCO =005 20201013061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 201013c20179999pau|||||o|||||||||||eng|| =022 \\$a2572-3928 =022 \\$z2520-6478 =030 \\$aSSMSCY =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA174 =082 04$a333.793$223 =245 00$aSmart and Sustainable Manufacturing Systems. =246 3\$aSSMS =246 3\$aASTM International Smart and Sustainable Manufacturing Systems =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2017- =310 \\$aAnnual =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =362 0\$aVol. 1, Issue 1 (January 2017)- =588 \\$aDescription based on: Volume 2, Issue 1; title from table of contents page (publisher's website, viewed October 13, 2020). =588 \\$aLatest issue consulted: 2020 Volume 4, Issue 2 (July 2020) (viewed October 13, 2020). =650 \0$aEngineering design. =650 \0$aInformation science. =650 \0$aSystems engineering. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/TOC/422020.htm =LDR 03286nas a2200733 i 4500 =001 SCOPEJ009 =003 IN-ChSCO =005 20200723061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200723c20179999pau|||||o||||||||||0eng|d =022 \\$a2572-3928 =022 \\$z2520-6478 =030 \\$aSSMSCY =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA174 =245 00$aSmart and Sustainable Manufacturing Systems. =246 3\$aSSMS =246 3\$aASTM International Smart and Sustainable Manufacturing Systems =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2017- =310 \\$aAnnual =362 0\$aVol. 1, Issue 1 (January 2017)- =588 \\$aDescription based on: Volume 2, Issue 1; title from table of contents page (publisher's website, viewed October 13, 2020). =588 \\$aLatest issue consulted: 2021 Volume 5, Issue 2 (May 2021) (viewed July 23, 2020). =650 \0$aEngineering design. =650 \0$aInformation science. =650 \0$aSystems engineering. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/TOC/522021.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20170016 =003 IN-ChSCO =005 20190211061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190211s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20170016$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20170016$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aQD553 =082 04$a546.2$223 =100 1\$aPichardo, Patricia A.,$eauthor. =245 10$aOn the Intensification of Natural Gas-Based Hydrogen Production Utilizing Hybrid Energy Resources /$cPatricia A. Pichardo, Vasilios I. Manousiouthakis. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (24 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aIn this work, parametric studies are carried out for natural gas-based hydrogen production systems utilizing hybrid energy sources, such as natural gas and concentrated solar power (CSP). The main technologies utilized in the considered networks consist of steam methane reforming, reverse water-gas shift, high-temperature shift, and low-temperature shift reactors; ideal hydrogen and carbon dioxide separators; water flash separators; pressure changing devices; and a heat exchange network (HEN). A broad search of the design space is carried out within the Infinite DimEnsionAl State-space conceptual framework, which allows for the simultaneous synthesis of the hydrogen production process and its associated HEN using linear programming. The identified designs minimize the total cost of three hot utilities and one cold utility, subject to bounding constraints on the work of separation and the HEN area. The level of exothermicity of the reforming operations, and the extent of CSP use, are shown to depend on the employed utility cost ratios and the aforementioned work and area bounds. =588 \\$aDescription based on publisher's website, viewed February 11, 2019. =650 \0$aHydrogen. =700 1\$aManousiouthakis, Vasilios I.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 2 Special Issue on Smart Manufacturing in Energy Intense Process Industries.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20170016.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20180021 =003 IN-ChSCO =005 20190211061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190211s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20180021$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20180021$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTK7872.D48 =082 04$a681/.2$223 =100 1\$aSen, Pallabi,$eauthor. =245 10$aStochastic Programming Approach versus Estimator-Based Approach for Sensor Network Design for Maximizing Efficiency /$cPallabi Sen, Urmila Diwekar, Debangsu Bhattacharyya. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (17 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThe measurement technology with sensors plays a key role in achieving efficient operation of the process plants, and optimal sensor placement is very important in this endeavor. The focus of the current work is on the development of sensor placement algorithms to obtain the numbers, locations, and types of sensors for a large-scale process with the estimator-based control system. Two sensor placement algorithms are developed and investigated. In one algorithm, dynamics in the process efficiency loss that are due to the estimator-based control system that receives measurements from a candidate sensor network are explicitly accounted for. For a large-scale process with a large number of candidate sensor locations, this approach leads to a computationally expensive mixed integer nonlinear programming problem. In another algorithm, the estimation error is accounted for in terms of probability distributions, and therefore, a stochastic programming approach is used to solve the sensor placement problem. A novel algorithm called BONUS is used to solve the problem. The developed sensor placement algorithms are implemented in an acid gas removal unit as part of an integrated gasification combined cycle power plant with precombustion carbon dioxide capture. In this article, we compare and contrast these two sensor placement algorithms by evaluating the efficiency loss of the optimal sensor network synthesized by each of these algorithms along with their computational performance. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed February 11, 2019. =650 \0$aSensor networks. =700 1\$aDiwekar, Urmila,$eauthor. =700 1\$aBhattacharyya, Debangsu,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 2 Special Issue on Smart Manufacturing in Energy Intense Process Industries.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20180021.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20180025 =003 IN-ChSCO =005 20190211061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190211s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20180025$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20180025$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ217.6 =082 04$a629.836$223 =100 1\$aGiuliani, Laura,$eauthor. =245 10$aData-Based Nonlinear Model Identification in Economic Model Predictive Control /$cLaura Giuliani, Helen Durand. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (49 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aMany chemical/petrochemical processes in industry are not completely modeled from a first-principles perspective because of the complexity of the underlying physico-chemical phenomena and the cost of obtaining more accurate, physically relevant models. System identification methods have been utilized successfully for developing empirical, though not necessarily physical, models for advanced model-based control designs such as model predictive control (MPC) for decades. However, a fairly recent development in MPC is economic model predictive control (EMPC), which is an MPC formulated with an economics-based objective function that may operate a process in a dynamic (i.e., off steady-state) fashion, in which case the details of the process model become important for obtaining sufficiently accurate state predictions away from the steady-state, and the physics and chemistry of the process become important for developing meaningful profit-based objective functions and safety-critical constraints. Therefore, methods must be developed for obtaining physically relevant models from data for EMPC design. While the literature regarding developing models from data has rapidly expanded in recent years, many new techniques require a model structure to be assumed a priori, to which the data is then fit. However, from the perspective of developing a physically meaningful model for a chemical process, it is often not obvious what structure to assume for the model, especially considering the often complex nonlinearities characteristic of chemical processes (e.g., in reaction rate laws). In this work, we suggest that the controller itself may facilitate the identification of physically relevant models online from process operating data by forcing the process state to nonroutine operating conditions for short periods of time to obtain data that can aid in selecting model structures believed to have physical significance for the process and, subsequently, identifying their parameters. Specifically, we develop EMPC designs for which the objective function and constraints can be changed for short periods of time to obtain data to aid in model structure selection. For one of the developed designs, we incorporate Lyapunov-based stability constraints that allow closed-loop stability and recursive feasibility to be proven even as the online “experiments” are performed. This new design is applied to a chemical process example to demonstrate its potential to facilitate physics-based model identification without loss of closed-loop stability. This work therefore reverses a question that has been of interest to the control community (i.e., how new techniques for developing models from data can be useful for control of chemical processes) to ask how control may be utilized to impact the use of these techniques for the identification of physically relevant process dynamic models that can aid in improving process operation and control for economic and safety purposes. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed February 11, 2019. =650 \0$aPredictive control. =700 1\$aDurand, Helen,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 2 Special Issue on Smart Manufacturing in Energy Intense Process Industries.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20180025.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20180020 =003 IN-ChSCO =005 20190211061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190211s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20180020$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20180020$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTK3226 =082 04$a621.31$223 =100 1\$aWestberg, B.,$eauthor. =245 10$aProactive Automation of a Batch Manufacturer in a Smart Grid Environment /$cB. Westberg, D. Machalek, S. Denton, D. Sellers, K. Powell. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (22 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aModern power companies are facing increasing technical challenges with resource management during peak demand intervals that stem from fluctuating demand and increased reliance on solar and wind generation. The peak power problem is partially addressed in some rate structures by applying a demand charge on users' bills, thus creating an incentive for users to reduce their peak demand. Solutions to the peak power issue are largely being addressed on the production side of the power grid (i.e., power plants) through use of fast-ramping peaking power plants. However, solutions are not common on the demand side of the grid, particularly in the manufacturing sector. Other studies have proposed that an ideal solution would involve a smart grid that utilizes automated response and prediction on both ends of the grid. This article analyzes how batch process facilities are well suited to respond to power grid changes as they function in a manner that allows for variable production scheduling. Additionally, the utilization of onsite energy storage is discussed for how it can be managed in order to reduce peak demand at necessary times. Data was analyzed from an industrial-scale bakery that has real-time electrical monitoring devices installed on major electrical systems in the factory. The simulation consisted of the glycol coolant system, the facility’s chiller, glycol storage tank, three bread dough mixers, and a fermenter room that includes product hold up. Through model simulation, combined with the implementation of the automation algorithms, a smart grid environment was simulated for the factory, and its results were analyzed. Among all operating schemes considered, the grid-coincident peak reduction, relative to normal operating conditions of the facility, was chosen for smart chilling, mixer staggering, and the combination of the two were 10, 29, and 36 %, respectively. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed February 11, 2019. =650 \0$aSmart power grids. =700 1\$aMachalek, D.,$eauthor. =700 1\$aDenton, S.,$eauthor. =700 1\$aSellers, D.,$eauthor. =700 1\$aPowell, K.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 2 Special Issue on Smart Manufacturing in Energy Intense Process Industries.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20180020.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20180022 =003 IN-ChSCO =005 20190211061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190211s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20180022$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20180022$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aQD305.H5 =082 04$a547.01$223 =100 1\$aMukherjee, Rajib,$eauthor. =245 10$aReliability of C-H-O Symbiosis Networks under Source Streams Uncertainty /$cRajib Mukherjee, Mahmoud M. El-Halwagi. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (22 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aAn ecoindustrial park (EIP) is a state-of-the-art concept in which a group of industries located in proximity share their products, byproducts, and waste in a symbiotic manner to conserve natural resources and enhance sustainability. To design a hydrocarbon-processing EIP, a carbon-hydrogen-oxygen symbiosis network (CHOSyN) that involves the use of multiscale atomic-based targeting, tracking, combination, separation, and allocation can be used. A major challenge in the multicompany implementation of an EIP is the uncertainty associated with the exchanged streams. The flow rate and characteristics of the streams are subject to fluctuations because of the variability in the performance of the participating plants. Concerns for reliable quantity and quality of streams exchanged among the different plants can largely impact the decision of a company to be integrated in an EIP. This article addresses the challenge of the fluctuating performances of participating plants by addressing the uncertainty of the exchanged streams in a CHOSyN. A stochastic approach is used for stream characterization and reliability assessment of an EIP. Performance functions are generated using response surface methodology. Next, a reliability analysis is performed with limit state function, or gradient-based methods, like the first-order reliability method, is used for analysis under relevant constraints. With the performance indexes, the variables are iteratively adjusted to achieve design and performance targets that meet sustainability criteria (e.g., economic, environmental). To illustrate the applicability of the proposed approach, a five-plant CHOSyN case study is solved. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed February 11, 2019. =650 \0$aHydrocarbons. =700 1\$aEl-Halwagi, Mahmoud M.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 2 Special Issue on Smart Manufacturing in Energy Intense Process Industries.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20180022.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20180029 =003 IN-ChSCO =005 20190211061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190211s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20180029$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20180029$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aHD9502.U62 =082 04$a333.790973$223 =100 1\$aAmini-Rankouhi, Aida,$eauthor. =245 10$aData-Driven Modeling and Analysis of Energy Efficiency of Geographically Distributed Manufacturing /$cAida Amini-Rankouhi, Sawyer Smith, Halit Akgun, Yinlun Huang. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (23 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aIndustries consume about one third of the total energy in the United States. In manufacturing sectors, significant energy loss occurs in various types of process systems and energy generation, conversion, and distribution steps. Over the past decades, various organizations have conducted a variety of analyses on national-level manufacturing activity and energy use, which have helped manufacturing sectors understand challenges in energy sustainability. It is recognized that integrated use of the openly accessible data may generate new information about energy efficiency and environmental impact in different manufacturing regions in the United States. In this work, we introduce a general data-driven modeling and analysis method to study energy consumption, energy loss, and carbon dioxide emission in the manufacturing sectors in geographically different regions. Case studies will illustrate methodological applicability and efficacy. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed February 11, 2019. =650 \0$aEnergy consumption. =700 1\$aSmith, Sawyer,$eauthor. =700 1\$aAkgun, Halit,$eauthor. =700 1\$aHuang, Yinlun,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 2 Special Issue on Smart Manufacturing in Energy Intense Process Industries.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20180029.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20180026 =003 IN-ChSCO =005 20190211061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190211s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20180026$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20180026$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTK7895.E43 =082 04$a384.3$223 =100 1\$aNguyen, Vinh,$eauthor. =245 10$aAn Internet of Things for Manufacturing (IoTfM) Enterprise Software Architecture /$cVinh Nguyen, Andrew Dugenske. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (13 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aAn Internet of Things for Manufacturing (IoTfM) has the potential to boost manufacturing by enabling the ability to monitor, analyze, and adjust production processes. However, manufacturers struggle with individually adhering to the wide variety of protocols utilized by Internet of Things (IoT) devices. This article proposes a low-cost architecture based upon the publish-and-subscribe standard for implementing IoTfM. The proposed system utilizes a message-queuing telemetry transport broker to handle data transfer in addition to a payload format designed for equipment monitoring. Low-cost gateways are used to adapt existing equipment and applications to the proposed architecture. Therefore, the architecture is a low-cost, nonintrusive, and flexible system for implementing IoT applications in production facilities. =588 \\$aDescription based on publisher's website, viewed February 11, 2019. =650 \0$aManufacturing processes. =650 \0$aInternet of things. =700 1\$aDugenske, Andrew,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 2 Special Issue on Smart Manufacturing in Energy Intense Process Industries.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20180026.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20180017 =003 IN-ChSCO =005 20190211061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190211s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20180017$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20180017$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aQD553 =082 04$a330$223 =100 1\$aOgumerem, Gerald S.,$eauthor. =245 10$aDynamic Modeling and Explicit Control of a PEM Water Electrolysis Process /$cGerald S. Ogumerem, Efstratios N. Pistikopoulos. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (19 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aHydrogen production from water electrolysis has the potential to mitigate the intermittency associated with power generated from variable renewable energy. In addition, proton exchange membrane water electrolyzer (PEMWE) has gained attention within the last decade because of its relative advantage over other water electrolysis technologies. However, the high cost of operation that is due to its energy intensity has been a major setback. In this work, we develop an optimal operating strategy for the PEMWE process using the parametric optimization and control framework. First, we present a dynamic mathematical model of the PEMWE that captures the detailed electrochemical interaction, transport phenomenon, bubble coverage, and other interactions associated with energy losses in the system. Secondly, we design a model predictive control (MPC), which is then reformulated into a multiparametric model predictive control (mp-MPC). Unlike the MPC, the mp-MPC avoids the online optimization procedure at every time step because the optimization is done once and offline. The control action is an explicit function of parameters that are realized during the process. The controller is tested on the original dynamic model in a closed loop validation scheme. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed February 11, 2019. =650 \0$aElectrolysis. =650 \0$aWater$xElectrolysis. =700 1\$aPistikopoulos, Efstratios N.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 2 Special Issue on Smart Manufacturing in Energy Intense Process Industries.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20180017.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20170003 =003 IN-ChSCO =005 20190529061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190529s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20170003$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20170003$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ1185 =082 04$a671.35$223 =100 1\$aMsaddek, El Bechir,$eauthor. =245 10$aQuantification and Compensation of CAM Errors in HSM /$cEl Bechir Msaddek, Zoubeir Bouaziz, Gilles Dessein. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (17 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aTo determine the numerical process imprecision of the complex shapes in high-speed machining, we must concentrate our study on computer-aided manufacturing (CAM) and computer-aided design (CAD) numerical models. At first, we define the theoretical prototype part in CAD. Then, the manufacturing software calculates the set of points and features of the tool orientations on the reference surface. However, we cannot always generate CAM machining trajectories that are identical to the theoretical contour. Depending on the complexity of the surface to be machined, CAM errors are generated between the theoretical contour and the machining trajectory. In this article, we proposed a quantification and a compensation tool of the CAM errors. In doing so, analytical models, which define the nodes of the tool-part contact, were developed. After that, a simulation tool based on a numerical calculation approach was structured. Finally, to compensate for the CAM errors in 2-D, the mirror method was used. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed May 29, 2019. =650 \0$aManufacturing processes. =650 \0$aHigh-speed machining. =650 \0$aProduction management. =700 1\$aBouaziz, Zoubeir,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20170003.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20170010 =003 IN-ChSCO =005 20190529061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190529s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20170010$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20170010$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ1077 =082 04$a621.8/9$223 =100 1\$aRevuru, Rukmini Srikant,$eauthor. =245 10$aMinimum Quantity Lubrication Using Nano-Graphite Cutting Fluids for Sustainable Machining of AISI 4140 under Different Cutting Conditions /$cRukmini Srikant Revuru, Nageswara Rao Posinasetti, M. Amrita, Venkata Ramana S. N. Vuppala. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (23 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aTraditionally, cutting fluids are applied in large quantities to reduce the cutting forces and temperatures in machining. However, in view of environmental and occupational health concerns with the conventional cutting fluids, industries are moving towards minimum quantity lubrication (MQL). MQL application is environment-friendly, provides good working conditions to employees, and is economical and hence sustainable. The present work studies the effect of MQL that utilizes cutting fluid with nano-graphite particles during the turning of AISI 4140 at varying ranges of cutting velocity, feed, and depth of cut. Taguchi’s design of experiments was used to limit the number of experiments. Cutting forces and surface roughness were measured and the performance of the formulated nano-graphite cutting fluid was compared to dry machining, flood lubrication, and MQL application of the conventional cutting fluid (without nanoparticles) at the considered range of cutting parameters. It was noticed that MQL with nanofluids offers a superior machining performance compared to other considered methods of lubrication. The optimum levels of the cutting parameters were determined using Taguchi analysis for all the lubricating conditions. Confirmation tests were carried out at the optimal conditions to establish the dependence of machining performance on lubricating methods. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed May 29, 2019. =650 \0$aMetal-working lubricants. =650 \0$aMetal-cutting tools. =700 1\$aPosinasetti, Nageswara Rao,$eauthor. =700 1\$aVuppala, Venkata Ramana S. N.,$eauthor. =700 1\$aAmrita, M.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20170010.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20170013 =003 IN-ChSCO =005 20190529061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190529s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20170013$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20170013$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTS23 =082 04$a670.42$223 =100 1\$aYang, Zhuo,$eauthor. =245 10$aDynamic Metamodeling for Predictive Analytics in Advanced Manufacturing /$cZhuo Yang, Douglas Eddy, Sundar Krishnamurty, Ian Grosse, Peter Denno, Paul William Witherell, Felipe Lopez. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (22 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aMetamodeling has been widely used in engineering for simplifying predictions of behavior in complex systems. The kriging method (Gaussian Process Regression) could be considered as a metamodeling technique that uses spatial correlations of sampling points to predict outcomes in complex and random processes. However, for large and nonideal data sets typical to those found in complex manufacturing scenarios, the kriging method is susceptible to losing its predictability and efficiency. To address these potential vulnerabilities, this article introduces a novel, dynamic metamodeling method that adapts kriging covariance matrices to improve predictability in contextualized, nonideal data sets. A key highlight of this approach is the optimal linking process, based on the location of prospective points, to alter the conventional stationary covariance matrices. This process reduces the size of resulting dynamic covariance matrices by retaining only the most critical elements necessary to maintain accuracy and reliability of new-point predictability. To further improve model fidelity, both the Gaussian parameters and design space attributes are optimized holistically within a problem space. Case studies with a representative test function show that the resulting Dynamic Variance-Covariance Matrix (DVCM) method is highly efficient without compromising accuracy. A second case study representative of an advanced manufacturing setting demonstrates the applicability and advantages of the DVCM method, including significantly increased model robustness. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed May 29, 2019. =650 \0$aManufactures$xTechnological innovations. =700 1\$aEddy, Douglas,$eauthor. =700 1\$aGrosse, Ian,$eauthor. =700 1\$aKrishnamurty, Sundar,$eauthor. =700 1\$aWitherell, Paul William,$eauthor. =700 1\$aDenno, Peter,$eauthor. =700 1\$aLopez, Felipe,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20170013.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20170015 =003 IN-ChSCO =005 20190529061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190529s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20170015$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20170015$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTN677 =082 04$a669.0282$223 =100 1\$aRogers, Austin P.,$eauthor. =245 10$aOptimization of the Cool-Down Process for a System of Sintering Furnaces /$cAustin P. Rogers, Bryan P. Rasmussen. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (23 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aSustainability in manufacturing systems involves the balance of energy, waste, and profit. This article presents a case study in which an industrial sintering process is optimized with respect to production, energy use, and material waste. The analysis proceeds by developing a physics-based furnace model, developing a cost function for optimization, and then considering the implementation costs associated with the solution. The furnace cycle time is reduced by optimally flowing the atmosphere gas through the furnace. A system of furnaces operating continuously is then considered, and the optimal control is modified in order to balance the profits with the implementation costs across a parallel set of furnaces. The implementation costs considered here include (1) the amount of gas storage required and (2) the size of the gas compressor required in order to implement the optimal control solution. The specific modeling and optimization methods used herein have broad application for thermal processes, and the general engineering approach should be used across the manufacturing sector to promote sustainability. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed May 29, 2019. =650 \0$aMetallurgical furnaces. =650 \0$aSteel$xHeat treatment. =650 \0$aFurnaces. =700 1\$aRasmussen, Bryan P.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20170015.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20180018 =003 IN-ChSCO =005 20190529061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190529s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20180018$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20180018$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aQA279.5 =082 04$a519.5/42$223 =100 1\$aNannapaneni, Saideep,$eauthor. =245 10$aPredictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing /$cSaideep Nannapaneni, Anantha Narayanan, Ronay Ak, David Lechevalier, Thurston Sexton, Sankaran Mahadevan, Yung-Tsun Tina Lee. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (27 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aBayesian networks (BNs) represent a promising approach for the aggregation of multiple uncertainty sources in manufacturing networks and other engineering systems for the purposes of uncertainty quantification, risk analysis, and quality control. A standardized representation for BN models will aid in their communication and exchange across the web. This article presents an extension to the predictive model markup language (PMML) standard for the representation of a BN, which may consist of discrete variables, continuous variables, or their combination. The PMML standard is based on extensible markup language (XML) and used for the representation of analytical models. The BN PMML representation is available in PMML v4.3 released by the Data Mining Group. We demonstrate the conversion of analytical models into the BN PMML representation, and the PMML representation of such models into analytical models, through a Python parser. The BNs obtained after parsing PMML representation can then be used to perform Bayesian inference. Finally, we illustrate the developed BN PMML schema for a welding process. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed May 29, 2019. =650 \0$aMachine learning. =650 \0$aBayesian statistical decision theory. =650 \0$aNeural networks (Computer science) =700 1\$aAk, Ronay,$eauthor. =700 1\$aNarayanan, Anantha,$eauthor. =700 1\$aLechevalier, David,$eauthor. =700 1\$aSexton, Thurston,$eauthor. =700 1\$aMahadevan, Sankaran,$eauthor. =700 1\$aLee, Yung-Tsun Tina,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20180018.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20180019 =003 IN-ChSCO =005 20190529061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190529s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20180019$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20180019$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ1225 =082 04$a621.9109$223 =100 1\$aFerguson, M.,$eauthor. =245 10$aA Data Processing Pipeline for Prediction of Milling Machine Tool Condition from Raw Sensor Data /$cM. Ferguson, R. Bhinge, J. Park, Y. T. Lee, K. H. Law. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (24 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aWith recent advances in sensor and computing technology, it is now possible to use real-time machine learning techniques to monitor the state of manufacturing machines. However, making accurate predictions from raw sensor data is still a difficult challenge. In this work, a data processing pipeline is developed to predict the condition of a milling machine tool using raw sensor data. Acceleration and audio time series sensor data are aggregated into blocks that correspond to the individual cutting operations of the Computer Numerical Control milling machine. Each block of data is preprocessed using well-known and computationally efficient signal processing techniques. A novel kernel function is proposed to approximate the covariance between preprocessed blocks of time series data. Several Gaussian process regression models are trained to predict tool condition, each with a different covariance kernel function. The model with the novel covariance function outperforms the models that use more common covariance functions. The trained models are expressed using the Predictive Model Markup Language where possible to demonstrate how the predictive model component of the pipeline can be represented in a standardized form. The tool condition model is shown to be accurate, especially when predicting the condition of lightly worn tools. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed May 29, 2019. =650 \0$aMilling-machines. =700 1\$aBhinge, R.,$eauthor. =700 1\$aLee, Y. T.,$eauthor. =700 1\$aLaw, K. H.,$eauthor. =700 1\$aPark, J.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20180019.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20180024 =003 IN-ChSCO =005 20190529061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190529s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20180024$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20180024$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTN677 =082 04$a669.0282$223 =100 1\$aKorambath, Prakashan,$eauthor. =245 10$aUse of On-Demand Cloud Services to Model the Optimization of an Austenitization Furnace /$cPrakashan Korambath, Hari S. Ganesh, Jianwu Wang, Michael Baldea, Jim Davis. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (15 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThis article describes a smart manufacturing framework, comprising an on-demand, cloud-based deployment of a modeling application in a manufacturing operation. A specific use case, the optimization of the austenitization of steel parts, is presented. The framework uses a Kepler workflow as a cloud service to orchestrate and manage the data and computations required to implement a run-time model-based control and optimization approach on Amazon Web Services (AWS) resources. Austenitization is an energy intensive heat treating process commonly employed to harden and strengthen ferrous metals, such as steel. Pre-finished steel parts are heated to a specific temperature in a continuously operating industrial austenitization furnace without oxidizing the surface. The steel parts are then rapidly cooled or quenched in an oil bath. There is significant potential to optimize energy productivity by managing the energy usage needed to achieve the properties of the metal part instead of managing to operational process settings. Models of this process, which predict the furnace energy consumption and temperatures of parts as a function of time and position in the furnace and map temperatures to properties, have been previously developed; however, for operational use, the data and models need to be orchestrated for run-time operation, access to infrastructure, scalability, security, and support. A cloud-based approach is an alternative to the on-premise approach, in which an infrastructure for data, computational, and security needs to be built and maintained to support the application. A workflow service makes it possible to combine and sequence simulation and optimization software applications developed in several distinct MATLAB (The Mathworks Inc., Natick, MA) model configurations that are needed for various data-based calculations. The final output of the computation is the optimal operating condition of the furnace that minimizes the fuel consumption without violating the part target specifications. The workflow can be triggered on demand by an operator of the furnace or run at periodic intervals. All the computational resources required are instantiated and run at the start of the workflow and shutdown at the end of the workflow. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed May 29, 2019. =650 \0$aMetallurgical furnaces. =650 \0$aSteel$xHeat treatment. =650 \0$aFurnaces. =700 1\$aGanesh, Hari S.,$eauthor. =700 1\$aWang, Jianwu,$eauthor. =700 1\$aBaldea, Michael,$eauthor. =700 1\$aDavis, Jim,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20180024.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20180031 =003 IN-ChSCO =005 20190529061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190529s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20180031$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20180031$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ213 =082 04$a628.9$223 =100 1\$aMontes-Dorantes, Pascual Noradino,$eauthor. =245 10$aModeling Type-1 Singleton Fuzzy Logic Systems Using Statistical Parameters in Foundry Temperature Control Application /$cPascual Noradino Montes-Dorantes, Adriana Mexicano Santoyo, Gerardo Maximiliano Méndez. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (24 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThis article presents a novel methodology to model a type-1 singleton fuzzy logic system (T1 SFLS) for temperature prediction in a secondary metallurgical process that takes place inside a ladle furnace. The proposal generates approximations using the energy consumed and the time elapsed within the casting process as input data, without using other instruments. It is known that the temperature cannot be verified all the time in the ladle furnace because it is sealed when it is in operation, and when temperature is measured, there is an uncertainty level in the sensor reading that generates predictions of the temperature in the order of 2.5 % out of the real value. The three proposed methodologies for the T1 SFLS forecaster provide a more accurate approximation of the temperature with less than 1 % of uncertainty. The predicted temperature is used in decision making to generate the required chemical composition of the steel and to mark the appropriate times to aggregate the additives in the alloy and achieve the required chemical balance. Compared with the model used by the industry, the results obtained show that the use of the proposed fuzzy model gives the opportunity to increase the quality of the steel by improving the adjustment of the quantities of additives that are lost by oxidation. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed May 29, 2019. =650 \0$aFuzzy systems. =650 \0$aAutomatic control. =700 1\$aSantoyo, Adriana Mexicano,$eauthor. =700 1\$aMéndez, Gerardo Maximiliano,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20180031.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20180033 =003 IN-ChSCO =005 20190529061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190529s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20180033$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20180033$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aQA76.87 =082 04$a006.32$223 =100 1\$aFerguson, Max,$eauthor. =245 10$aDetection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning /$cMax Ferguson, Ronay Ak, Yung-Tsun Tina Lee, Kincho H. Law. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (28 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aQuality control is a fundamental component of many manufacturing processes, especially those involving casting or welding. However, manual quality control procedures are often time-consuming and error-prone. In order to meet the growing demand for high-quality products, the use of intelligent visual inspection systems is becoming essential in production lines. Recently, Convolutional Neural Networks (CNNs) have shown outstanding performance in both image classification and localization tasks. In this article, a system is proposed for the identification of casting defects in X-ray images, based on the Mask Region-based CNN architecture. The proposed defect detection system simultaneously performs defect detection and segmentation on input images, making it suitable for a range of defect detection tasks. It is shown that training the network to simultaneously perform defect detection and defect instance segmentation results in a higher defect detection accuracy than training on defect detection alone. Transfer learning is leveraged to reduce the training data demands and increase the prediction accuracy of the trained model. More specifically, the model is first trained with two large, openly available image datasets before fine-tuning on a relatively small metal casting X-ray dataset. The accuracy of the trained model exceeds the state-of-the art performance of the Grupo de Inteligencia de Máquina database of X-ray images (GDXray) Castings dataset and is fast enough to be used in a production setting. The system also performs well on the GDXray Welds dataset. A number of in-depth studies are conducted to explore how transfer learning, multitask learning, and multi-class learning influence the performance of the trained system. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed May 29, 2019. =650 \0$aAtmosphere$xRemote sensing. =650 \0$aNeural networks (Computer science) =700 1\$aAk, Ronay,$eauthor. =700 1\$aLee, Yung-Tsun Tina,$eauthor. =700 1\$aLaw, Kincho H.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20180033.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20180035 =003 IN-ChSCO =005 20190529061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190529s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20180035$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20180035$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aQC176.8.A3 =082 04$a530.4/12$223 =100 1\$aWilliams, Jacob,$eauthor. =245 10$aDefect Detection and Monitoring in Metal Additive Manufactured Parts through Deep Learning of Spatially Resolved Acoustic Spectroscopy Signals /$cJacob Williams, Paul Dryburgh, Adam Clare, Prahalada Rao, Ashok Samal. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (23 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aLaser powder bed fusion (LPBF) is an additive manufacturing (AM) process that promises to herald a new age in manufacturing by removing many of the design and material-related constraints of traditional subtractive and formative manufacturing processes. However, the level and severity of defects observed in parts produced by the current class of LPBF systems will not be tolerated in safety-critical applications. Hence, there is a need to introduce information-rich process monitoring to assess part integrity simultaneously with fabrication so that opportune corrective action can be taken to minimize part defects. Spatially Resolved Acoustic Spectroscopy (SRAS) is a uniquely positioned nondestructive acoustic microscopy sensing approach that has been successfully used to probe the mechanical properties and assess the presence of defects in LPBF parts. However, the technique is sensitive to extraneous phenomena, such as surface reflectivity, which occur within the LPBF system and may occlude identification of surface breaking and subsurface defects. With a view to applying the SRAS technique for in-process monitoring in a production-scale LPBF environment and to overcome the foregoing challenge, this study proposes the use of a deep learning convolutional neural network that is termed Densely connected Convolutional Block Architecture for Multimodal Image Regression (DCB-MIR), which invokes SRAS-derived acoustic velocity maps of the part as input data and translates them to an output resembling an optical micrograph. Through this approach, we demonstrate that defects, such as porosity and surface imperfections in titanium alloy and nickel alloy specimens made using LPBF, which were not clearly discernable in the as-measured SRAS acoustic map and were obscured by artifacts in the optical image, are accurately identified. To quantify the accuracy of the approach, the cosine similarity between the predicted output images and target optical images was used as the objective function of DCB-MIR. The cosine similarity between the acquired SRAS signatures and the corresponding as-measured optical micrographs of samples typically ranged between –0.15 and 0.15. In contrast, when the optical micrograph-like images derived from DCB-MIR proposed in this work were compared with the optical signatures, the cosine similarity improved in the range of 0.25 to 0.60. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed May 29, 2019. =650 \0$aAcoustic surface waves. =650 \0$aSolids$xAcoustic properties. =700 1\$aDryburgh, Paul,$eauthor. =700 1\$aClare, Adam,$eauthor. =700 1\$aRao, Prahalada,$eauthor. =700 1\$aSamal, Ashok,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20180035.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20180036 =003 IN-ChSCO =005 20190529061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190529s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20180036$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20180036$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aPS183 =082 04$a670.4$223 =100 1\$aZhang, Heng,$eauthor. =245 10$aA Knowledge-Enriched Computational Model to Support Lifecycle Activities of Computational Models in Smart Manufacturing /$cHeng Zhang, Utpal Roy. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (23 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aBecause of the needs in developing industrial community modeling and simulation platforms for smart manufacturing, a Knowledge-Enriched Computational Model (KECM) is proposed in this article to formally capture domain knowledge and integrate that knowledge with standardized computational models. To support model development in a distributed environment, the KECM can be used as the medium for formal information sharing between model developers. A case study has been developed to demonstrate the utilization of the KECM in supporting the construction of a Bayesian Network model between a domain expert and data analyst. To support model deployment, a general method to support the integration of computational models into a manufacturing data system has been presented. A case study has been developed to show the data integration of an optimization model into a Business To Manufacturing Markup Language-based manufacturing system. Finally, a general model to support the combination of computational models have been presented. A case study has been developed to show the combination of an Agent-based model and a Decision Tree model using the KECM. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed May 29, 2019. =650 \0$aManufacturing processes$xAutomation. =650 \0$aManufacturing industries$xTechnological innovations. =700 1\$aRoy, Utpal,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20180036.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20180039 =003 IN-ChSCO =005 20190529061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 190529s2018\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20180039$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20180039$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA658.8 =082 04$a624.1/7713$223 =100 1\$aWei, Qi,$eauthor. =245 10$aLearn to Learn: Application to Topology Optimization /$cQi Wei, Ioannis Akrotirianakis, Arindam Dasgupta, Amit Chakraborty. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2018. =300 \\$a1 online resource (11 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThe objective of this article is to propose a new algorithm for topology optimization (TO), specifically in the context of additive manufacturing (AM). TO as a part design mechanism is particularly synergestic with AM. We propose to solve the TO problem using a pretrained deep neural network (DNN). We develop a variation of DNN architecture that has been used successfully in image processing, and its adaptation to TO problems constitutes the main focus of our work. We use a deep convolutional neural network to learn end-to-end mapping from the initial designs obtained by running solid isotropic material with penalization (SIMP) for a few iterations to the final optimal designs obtained when SIMP runs to convergence. The iterative updates from the initial designs to the converged ones is replaced by forward propagation through the trained DNN. Our approach can be thought of as a way to the develop a trained DNN that can imitate the gradient descent method used in the standard SIMP method. We present computational results that demonstrate that our approach can compete favorably with SIMP. =541 \\$aASTM International$3PDF$cPurchase price$hUSD25. =588 \\$aDescription based on publisher's website, viewed May 29, 2019. =650 \0$aStructural optimization. =650 \0$aTopology. =700 1\$aAkrotirianakis, Ioannis,$eauthor. =700 1\$aDasgupta, Arindam,$eauthor. =700 1\$aChakraborty, Amit,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 2, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2018$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20180039.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190049 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190049$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTS183 =082 04$a670.285223 =100 1\$aAhuett-Garza, Horacio,$eauthor. =245 10$aA Reference Model for Evolving Digital Twins and Its Application to Cases in the Manufacturing Floor /$cHoracio Ahuett-Garza, Pedro Daniel Urbina Coronado. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (13 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aCyberphysical systems (CPSs) represent the major contribution of Industry 4.0 to modern manufacturing systems. Once implemented, CPS will increase the efficiency of operations, improve product quality, reduce waste, and optimize the use of resources and assets. A critical element for the realization of CPSs is the Digital Twin (DT), a concept that bridges the gap between the physical and digital realms. Although significant strides have been made to establish the nature of DT, there is still a need for comprehensive reference models and test cases that can help guide the design and deployment of DT. This work presents a reference model for the development of DT. The model considers factors such as stage in the life cycle of a product, the purpose of the digital representation, and the use of the information that the digital representation provides. Cases are presented and analyzed in terms of the proposed model. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aManufacturing processes$xAutomation. =700 1\$aUrbina Coronado, Pedro Daniel,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 2 Special Issue on Cyber-Physical Systems and Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190049.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190017 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190017$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ1185 =082 04$a621.9023223 =100 1\$aNguyen, Vinh,$eauthor. =245 10$aAn IoT Architecture for Automated Machining Process Control: A Case Study of Tool Life Enhancement in Turning Operations /$cVinh Nguyen, Thomas Malchodi, Mahmoud Dinar, Shreyes N. Melkote, Anant Mishra, Sudhir Rajagopalan. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (13 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aWith the advent of the Internet of Things (IoT) in manufacturing applications, current research is aimed at utilization of IoT data for process control. This article presents a novel approach to performing automated process feedback using a cloud-based IoT architecture. Specifically, a case study of automated spindle speed adjustment to enhance tool life in a machining operation is used to evaluate the proposed architecture. A data-driven model of tool flank wear evolution in a longitudinal turning operation is created on a cloud-based platform through measurements of a polyvinylidene fluoride thin film sensor voltage data and machine tool parameters monitored via MTConnect. The data are used to develop a Gaussian process regression (GPR) model to predict the average tool flank wear as a function of the measured quantities, which is then used to predict the remaining tool life. The performance of the GPR model is evaluated using 10-fold cross-validation and is shown to be sufficiently accurate for predicting the average flank wear with the coefficient of determination ( R 2 ) and the root mean square error values of 0.96 and 13.45 Μm, respectively. A web application running the GPR model on the cloud platform is used to forecast the remaining tool life during a turning operation and when the predicted remaining tool life is less than desired, the web application commands a spindle speed override to automatically extend tool life. The architecture is demonstrated through longitudinal turning experiments on stainless steel 316L to extend tool life by 82 %. In addition, the latency of the architecture is evaluated and shown to be acceptable for the tool life enhancement application considered in this study. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aMachining$xAutomation. =650 \0$aMachine-tools. =700 1\$aDinar, Mahmoud,$eauthor. =700 1\$aMalchodi, Thomas,$eauthor. =700 1\$aMelkote, Shreyes N.,$eauthor. =700 1\$aMishra, Anant,$eauthor. =700 1\$aRajagopalan, Sudhir,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 2 Special Issue on Cyber-Physical Systems and Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190017.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190023 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190023$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aQ325.5 =082 04$a 006.31223 =100 1\$aSumba, Jorge Chuya,$eauthor. =245 10$aIntelligent Fault Diagnosis for Rotating Machines Using Deep Learning /$cJorge Chuya Sumba, Israel Ruiz Quinde, Luis Escajeda Ochoa, Juan Carlos Tudón Martínez, Antonio J. Vallejo Guevara, Ruben Morales-Menendez. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (14 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThe diagnosis of failures in high-speed machining centers and other rotary machines is critical in manufacturing systems, because early detection can save a representative amount of time and cost. Fault diagnosis systems generally have two blocks: feature extraction and classification. Feature extraction affects the performance of the prediction model, and essential information is extracted by identifying high-level abstract and representative characteristics. Deep learning (DL) provides an effective way to extract the characteristics of raw data without prior knowledge, compared with traditional machine learning (ML) methods. A feature learning approach was applied using one-dimensional (1-D) convolutional neural networks (CNN) that works directly with raw vibration signals. The network structure consists of small convolutional kernels to perform a nonlinear mapping and extract features; the classifier is a softmax layer. The method has achieved satisfactory performance in terms of prediction accuracy that reaches ∼99 % and ∼97 % using a standard bearings database: the processing time is suitable for real-time applications with ∼8 ms per signal, and the repeatability has a low standard deviation <2 % and achieves an acceptable network generalization capability. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aMachine learning. =700 1\$aMartínez, Juan Carlos Tudón,$eauthor. =700 1\$aMorales-Menendez, Ruben,$eauthor. =700 1\$aOchoa, Luis Escajeda,$eauthor. =700 1\$aQuinde, Israel Ruiz,$eauthor. =700 1\$aVallejo Guevara, Antonio J.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 2 Special Issue on Cyber-Physical Systems and Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190023.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190020 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190020$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ1185.S555 =082 04$a621.8223 =100 1\$aHassan, M.,$eauthor. =245 10$aA Generalized Multisensor Real-Time Tool Condition–Monitoring Approach Using Deep Recurrent Neural Network /$cM. Hassan, A. Sadek, M. H. Attia. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (12 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aTool condition monitoring (TCM) is crucial for manufacturing systems to maximize productivity, maintain part quality, and reduce waste and cost. Available TCM systems mainly depend on data-driven classical machine learning methods to analyze different sensors‘ feedback signals for tool condition prediction. Despite their applicability for high process variability and part complexity, they require long development lead time and extensive expert efforts for signal feature definition, extraction, and fusion to accurately detect the tool condition. Additionally, they substantially depend on sensors whose nature is intrusive to the cutting process. Therefore, this research presents a generalized, nonintrusive multisignal fusion approach for real-time tool wear detection in milling that redefines process learning directly from raw signals. In this two-stage approach, the signals‘ intrinsic mode functions (IMFs) are extracted, optimized, and directly fused in a deep long short-term memory (LSTM) recurrent neural network (RNN) for tool condition prediction. The IMF extraction and optimization mask the effect of the cutting conditions to accentuate the tool condition effect. Therefore, it generalizes and minimizes the learning process to cover a wider range of unlearned process parameters. Embedded feature architecting of the LSTM-RNN is applied to the optimized IMFs for signal fusion and tool condition prediction to standardize the learning process and significantly minimize the lead time. Spindle motor current, voltage, and power signals are used to avoid process intrusion. A systematic study is carried out to define the optimum LSTM-RNN architecture. Extensive experimental validation results have demonstrated tool wear detection accuracy >95 % at different ranges of unlearned cutting conditions. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aMachine-tools$xMonitoring. =650 \0$aAcoustic emission testing. =700 1\$aAttia, M. H.,$eauthor. =700 1\$aSadek, A.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 2 Special Issue on Cyber-Physical Systems and Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190020.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190018 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190018$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ163.3 =082 04$a630223 =100 1\$aBrimley, Paige,$eauthor. =245 10$aSmart Scheduling of a Batch Manufacturer’s Operations by Utilization of a Genetic Algorithm to Minimize Electrical Demand /$cPaige Brimley, Derek Machalek, Kody M. Powell. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (15 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aPower utilities currently manage unpredictable electrical demand through the use of fast-ramping plants and punitive demand fees. Unpredictable demand makes it more difficult to onboard variable renewable energy sources, such as solar and wind, because of their intermittency, which can contribute to grid instability. Optimal utilization of variable renewable energy sources requires a flexible, resilient electrical grid and thus a transformation of the current system to a proposed “smart grid” that would adapt in real time to grid signals. Batch manufacturers are well suited to reduce the burden of intermittency by considering demand side management techniques when scheduling process operations. The goal of such participation is a reduction in electrical demand and associated costs for both the facility and the utility. This study investigates how a novel application of the genetic algorithm could be used to schedule a batch manufacturer‘s operations in a manner that reduces overall peak demand and is compatible with process constraints. The genetic algorithm is chosen because it is highly efficient at finding minima and handling complex constraints and large data sets. A major highlight of this article is the use of measured demand profiles to log real-time energy consumption of process equipment. The scenarios investigated use a genetic algorithm to determine the optimum scheduling when given a fixed set of interdependent operations that must be executed within the day. All investigated scenarios have a reduction in peak demand after rescheduling via the genetic algorithm. Baseline operations demonstrated reductions between 13 % and 28 %, with an average reduction of 21 %. The results of this study demonstrate that automated load shifting can easily be applied to an industrial facility and paves the way for future work to investigate the integration of scheduling algorithms with process control systems. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aElectric power$xSupply and demand. =700 1\$aMachalek, Derek,$eauthor. =700 1\$aPowell, Kody M.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 2 Special Issue on Cyber-Physical Systems and Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190018.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190038 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190038$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA335 =082 04$a670.427223 =100 1\$aNewman, Daniel,$eauthor. =245 10$aDevelopment of a Digital Architecture for Distributed CNC Machine Health Monitoring /$cDaniel Newman, Mahmoud Parto, Kyle Saleeby, Thomas Kurfess, Andrew Dugenske. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (15 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aWith the advancement of the Internet of Things, machines are expected to become more connected by transmitting operational data in ways that can readily be consumed by analytics engines. In recent years, a substantial amount of research has been conducted to create techniques of acquiring and utilizing machine data to inform supply chain decisions and monitor machine health. In modern manufacturing equipment, standard interfaces such as MTConnect and Open Platform Communications–Unified Architecture facilitate the data acquisition process by providing a means of easily capturing machine controller data. With data from these interfaces, monitoring quantities such as machine utilization are possible by monitoring the MTConnect interface and performing analysis on the machine state. In a distributed manufacturing environment, a means of remotely accessing this data must be developed. The infrastructure responsible for this data transmission must be secure, scalable, and standardized. If detailed machine health metrics are to be analyzed, the infrastructure should be able to accommodate data from sources beyond the machine controller itself. Supplementing the controller data with that from additional sensors, such as accelerometers, temperature, humidity, and current sensors, a more complete picture of machine health status can be created. This article will present a digital architecture that meets these criteria and demonstrate the use cases in machine utilization and health monitoring. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aMachinery$xMonitoring. =700 1\$aDugenske, Andrew,$eauthor. =700 1\$aKurfess, Thomas,$eauthor. =700 1\$aParto, Mahmoud,$eauthor. =700 1\$aSaleeby, Kyle,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 2 Special Issue on Cyber-Physical Systems and Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190038.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190019 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190019$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ1185 =082 04$a621.902223 =100 1\$aParto, Mahmoud,$eauthor. =245 10$aA Cloud-Based Machine Vision Approach for Utilization Prediction of Manual Machine Tools /$cMahmoud Parto, Dongmin Han, Pierrick Rauby, Chong Ye, Yuanlai Zhou, Duen Horng Chau, Thomas Kurfess. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (12 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aSince the last decades of the 20th century, the manufacturing industry has been moving toward the development of fully automated equipment; however, a large number of machine tools are still manual and require the operators to stay close by while operating. This allows solutions to be developed that measure machine utilization by tracking and correlating the location of personnel and manual machines. The knowledge of machine utilization and the prediction of machine availability can be extremely advantageous in efficiently scheduling the work that needs to be done with the machines. The lack of this knowledge, on the other hand, can cause long wait times and inefficiencies. In this article, we have proposed and studied a cost affordable cloud-based machine vision approach to capture and predict the utilization of manual machine tools. A case study was performed in Georgia Tech‘s ME2110 lab, where approximately 400 students design and develop their projects every semester. Because this lab is open to all of these students with no predefined schedules, the statistical analysis on the historical equipment utilization could be one of the only methods of predicting machine availability. By analyzing the data of a security camera mounted in this lab, the location of students was tracked and correlated with the location of the machine tools to find out the utilization time of the machines. The autoregressive–moving-average (ARMA) method was then used to predict the machine utilization for days after. The evaluation results of this framework show that the error between the actual and predicted utilization was less than 20 %. Although the accuracy of this framework with the data collected in 27 days is high and can be used to increase the efficiency of the lab, the accuracy is expected to increase by capturing more data in a longer time period. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aMachine-tools. =650 \0$aMachine-tools$xDesign and construction. =700 1\$aChau, Duen Horng,$eauthor. =700 1\$aHan, Dongmin,$eauthor. =700 1\$aKurfess, Thomas,$eauthor. =700 1\$aRauby, Pierrick,$eauthor. =700 1\$aYe, Chong,$eauthor. =700 1\$aZhou, Yuanlai,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 2 Special Issue on Cyber-Physical Systems and Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190019.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190025 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190025$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTS155.65 =082 04$a670.427223 =100 1\$aWen, Rong,$eauthor. =245 10$aSupply–Demand Prediction for Agile Manufacturing with Deep Neural Network /$cRong Wen, Wenjing Yan. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (11 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aAgile manufacturing represents modern production systems that rapidly respond to a fast-moving marketplace and connect customers to production. The success of an agile manufacturing system relies on multisource data analytics, which provide smart data-driven decision-making strategies to guide manufacturing and the supply chain system. In order to implement rapid manufacturing processes to respond to customer orders, supply–demand gap prediction plays a critical role. In this article, we study the problem of predicting supply–demand gap with respect to product types, categories, and spatiotemporal attributes. To this end, we propose and develop an end-to-end framework using an extendable deep neural network architecture for supply–demand gap reduction. The framework can process multiple customized input factors and automatically discover spatiotemporal supply–demand patterns from historical transaction data. A set of customized features are employed to build a general training model to predict future demand. Embedding layers are used to map high dimensional features into a smaller subspace for input data consolidation. Fully connected layers with activation functions are used to build the training architecture of the model. Customized data attributes can be concatenated from different layers in the deep learning neural network. In this way, multisource input data can be integrated with outputs of internal layers for a comprehensive demand prediction. Experiments were conducted to evaluate the network with real supply and demand data, which were acquired from warehouses of a manufacturing company. The experimental results demonstrated that the proposed network was able to reduce supply–demand gap and provide a practical solution to long-term customer demand prediction. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aSystem design. =650 \0$aFlexible manufacturing systems. =700 1\$aYan, Wenjing,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 2 Special Issue on Cyber-Physical Systems and Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190025.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190028 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190028$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTS191 =082 04$a629.049223 =100 1\$aNorton, Adam,$eauthor. =245 10$aA Standard Test Method for Evaluating Navigation and Obstacle Avoidance Capabilities of AGVs and AMRs /$cAdam Norton, Peter Gavriel, Holly Yanco. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (21 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aAutomatic guided vehicles (AGVs) and autonomous mobile robots (AMRs) are now ubiquitous in industrial manufacturing environments. These systems all must possess a similar set of core capabilities, including navigation, obstacle avoidance, and localization. However, there are few standard methods to evaluate the capabilities and limitations of these systems in a way that is comparable. In this article, a standard test method is presented that can be used to evaluate these capabilities and can be easily scaled and augmented according to the characteristics of the system under test. The test method can be configured in a variety of ways to exercise different capabilities, all using a common test apparatus to ease test setup and increase versatility. For each test configuration, conditions are specified with respect to the a priori knowledge provided to the system (e.g., boundary or obstacle locations) and the obstacles in the environment. Robustness of system capabilities is evaluated by purposefully introducing misalignment between the characteristics of the physical and virtual environments (e.g., providing representations of obstacles in the system‘s map when they are not physically present). Example test performance data from an AMR are provided. The goal of this work is to provide a common method to characterize the performance of mobile systems in industrial environments that is easily comparable and communicated for both commercial and developmental purposes. This work is driven by existing standards and those in development by the ASTM F45 Committee on Driverless Automatic Guided Industrial Vehicles and will influence the development of new standards within the committee. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aAutomated guided vehicle systems. =650 \0$aAutonomous robots. =700 1\$aGavriel, Peter,$eauthor. =700 1\$aYanco, Holly,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 2 Special Issue on Cyber-Physical Systems and Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190028.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190029 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190029$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTS183 =082 04$a658.26223 =100 1\$aLe, Dy D.,$eauthor. =245 10$aVisualization and Explainable Machine Learning for Efficient Manufacturing and System Operations /$cDy D. Le, Vung Pham, Huyen N. Nguyen, Tommy Dang. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (21 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aTo enable Industry 4.0 successfully, there is a need to build a resilient automation system that can quickly recover after having been attacked or robustly sustain continued operations while being threatened, enable an automated monitoring evolution via various sensor channels in real time, and use advanced machine learning and data analytics to formulate strategies to mitigate and eliminate faults, threats, and malicious attacks. It is envisioned that if we can develop an intelligent model that (a) represents a meaningful, realistic environment and complex entity containing manufacturing Internet of Things interdependent and independent properties that are stepping-stones of the cyber kill chain or precursors of the onset of cyberattacks; (b) can learn and predict potential errors and formulate offense/defense strategies and healing solutions; (c) can enable cognitive ability and human-in-the-loop analytics in real time; and (d) can facilitate system behavior changes to disrupt the attack cascade, then the hosting system can learn how to neutralize threats and attacks and self-repair infected or damaged links autonomously. In this article, our preliminary work presents a visual analytics framework and technique for situational awareness, including autonomously monitoring, diagnosing, and prognosticating the state of cyber-physical systems. Our approach, presented in this article, relies on visual characterizations of multivariate time series and real-time predictive analytics to highlight potential faults, threats, and malicious attacks. To validate the usefulness of our approach, we demonstrate the developed technique using various aviation datasets obtained from the Prognostics Center of Excellence at the National Aeronautics and Space Administration Ames. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aManufacturing processes$xEnergy conservation. =650 \0$aProcess control. =700 1\$aDang, Tommy,$eauthor. =700 1\$aNguyen, Huyen N.,$eauthor. =700 1\$aPham, Vung,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 2 Special Issue on Cyber-Physical Systems and Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190029.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190021 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190021$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aQA76.9.B56 =082 04$a005.758 223 =100 1\$aGopalakrishnan, Praveen Kumare,$eauthor. =245 10$aA Decision-Making Framework for Blockchain Technology Selection /$cPraveen Kumare Gopalakrishnan, Varsha Ananth, Kemper Lewis, Sara Behdad. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (21 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aAs the concept of the visible product lifecycle and product tracking becomes important to consumers, emerging technologies such as Internet of Things (IoT)-based networks and Blockchain platforms are considered promising solutions by Original Equipment Manufacturers (OEMs) to increase their brand reputation, improve their consumers‘ experiences, and reduce their operational costs. However, costly implementation, data verification issues, and the risk of uploading inaccurate information on those platforms have limited the adoption rate and capabilities offered by the aforementioned technologies. Companies often face the question of what type of distributed ledger technology and verification protocol is more efficient in terms of operational costs as well as security. The purpose of this study is to develop an optimization framework to make a trade-off between the cost of implementation of the technology and the security level. The framework is developed based on utility theory principles. The main focus is to discuss the cost associated with various aspects of technology implementation. Finally, this general cost framework is shown as a demo for an example of the food supply chain. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aPublic administration$xData processing. =650 \0$aReal estate business$xData processing. =700 1\$aAnanth, Varsha,$eauthor. =700 1\$aBehdad, Sara,$eauthor. =700 1\$aLewis, Kemper,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 2 Special Issue on Cyber-Physical Systems and Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190021.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190026 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190026$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA403.6 =082 04$a620.11223 =100 1\$aNagarajan, Hari P. N.,$eauthor. =245 10$aGraph-Based Metamodeling for Characterizing Cold Metal Transfer Process Performance /$cHari P. N. Nagarajan, Suraj Panicker, Hossein Mokhtarian, Théo Remy-Lorit, Eric Coatanéa, Romaric Prod’hon, Hesam Jafarian, Karl R. Haapala, Ananda Chakraborti. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (21 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aAchieving predictable, reliable, and cost-effective operations in wire and arc additive manufacturing is a key concern during production of complex-shaped functional metallic components for demanding applications, such as those found in aerospace and automotive industries. A metamodel combining localized submodels of the different physical phenomena during welding can ensure stable material deposition. Such a metamodel would necessarily combine submodels from multiple domains, such as materials science, thermomechanical engineering, and process planning, and it would provide a holistic systems perspective of the modeled process. An approach using causal graph-based modeling and Bayesian networks is proposed to develop a metamodel for a test case using wire and arc additive manufacturing with cold metal transfer. The developed modeling approach is used to characterize the effect of manufacturing variables on product dimensional quality in the form of a causal graph. A quantitative simulation using Bayesian networks is applied to the causal graph to enable process parameter tuning. The Bayesian network inference mechanism predicts the effects of the parameters on results, whereas, conversely, with known targets, it can predict the required parameter values. Validation of the developed Bayesian network model is performed using experimental tests. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aManufacturing processes. =650 \0$aEngineering. =700 1\$aChakraborti, Ananda,$eauthor. =700 1\$aCoatanéa, Eric,$eauthor. =700 1\$aHaapala, Karl R.,$eauthor. =700 1\$aJafarian, Hesam,$eauthor. =700 1\$aMokhtarian, Hossein,$eauthor. =700 1\$aPanicker, Suraj,$eauthor. =700 1\$aProd’hon, Romaric,$eauthor. =700 1\$aRemy-Lorit, Théo,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 2 Special Issue on Cyber-Physical Systems and Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190026.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190024 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190024$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA403.6 =082 04$a620.11223 =100 1\$aAljarrah, Osama,$eauthor. =245 10$aA Self-Organizing Evolutionary Method to Model and Optimize Correlated Multiresponse Metrics for Additive Manufacturing Processes /$cOsama Aljarrah, Jun Li, Wenzhen Huang, Alfa Heryudono, Jing Bi. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (25 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThe use of robust multiresponse constrained optimization techniques in which multiple-objective responses are involved is becoming a crucial part in additive manufacturing (AM) processes. Common and popular techniques, in most cases, rely on the assumption of independent responses. In practice, however, many of the desired quality characteristics can be correlated. In this work, we propose a technique based on three ingredients: hybrid self-organizing (HSO) method, desirability function (DF), and evolutionary algorithms to analyze, model, and optimize the multiple correlated responses for the fused deposition modeling (FDM) process, one of the most popular AM technologies. The multiobjective functions are formulated by employing the HSO method and DF, where structural integrity and process efficiency metrics are considered for the data-driven correlated multiresponse models. Subsequently, layer thickness, nozzle temperature, printing speed, and raster angles are taken as process parameters (decision variables). The operational settings and capabilities for the FDM machine are defined as boundary constraints. Different EA algorithms, the nondominated sorting genetic algorithm, and the multiobjective particle swarm optimization method, are then deployed to model the AM criteria accordingly to extract the Pareto-front curve for the correlated multiresponse functions. FDM experimental design and data collection for the proposed method are provided and used to validate our approach. This study sheds light on formulating robust and efficient data-driven modeling and optimizations for AM processes. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aManufacturing processes. =650 \0$aEngineering. =700 1\$aBi, Jing,$eauthor. =700 1\$aHeryudono, Alfa,$eauthor. =700 1\$aHuang, Wenzhen,$eauthor. =700 1\$aLi, Jun,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 2 Special Issue on Cyber-Physical Systems and Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190024.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20180042 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20180042$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTD892 =082 04$a690.2223 =100 1\$aTawfik, Ahmed,$eauthor. =245 10$aUtilizing Detector Filters for Noise Reduction in X-Ray Computer Tomography Scanning for the Inspection of the Structural Integrity of Additive Manufactured Metal Parts /$cAhmed Tawfik, Samuel Otto Nicholson, Radu Racasan, Liam Blunt, Paul Bills. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (13 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThe recent development of industrial computer tomography (CT) has enabled inspection of the integrity of mechanical parts without physical sectioning. Using X-ray CT (XCT) presents many challenges prohibiting industry from widely implementing the technology. The existence of several variables such as filament current, filter material, and filter thickness is directly related to the presence of noise and influences the accuracy of the inspection process. Noise in the resultant reconstructed image is the result of low-energy X rays being absorbed by the detector causing large variations of brightness on the computer image; other noise sources such as detector variations and electronic noise are further sources of error. Additionally, the reconstruction method, which also has a response to the noise scatter, can contribute to the resulting image noise. The presence of noise can skew the resulting image and create the illusion of pores/defects that are not actually present, thus vastly compromising the results of the analysis. Noise reduction is vital in improving the reliability of CT imaging of additive manufactured components. This article investigates the possibility of reducing noise by using detector filters. The study will investigate the impact of source and detector filters (shown in fig. 1) on image quality. Two filter types of 100-Μm-thick aluminum and 100-um-thick copper filters were used, and the results were compared to a conventional tube filter. The workpiece used in this study consisted of a 6-mm Ti6Al4V round bar with designed internal features ranging from 50 to 1,400 Μm containing a mixture of voids, two filled with unfused powder and two unfilled. The diameter and depth of defects were characterized using focus variation microscopy and then scanned with a Nikon XTH225 industrial CT to compare the measured internal features. The analysis was carried out using VGStudio Max 3.0 (Volume Graphics, Germany) software package to evaluate surface determination and defects/porosity. Preliminary results indicate that using the aluminum detector filter can reduce the noise by 20 % when scanning the part under certain conditions. Also, for a low-magnification scan using the aluminum detector filter, no difference in noise was evident, but it was noted that the filament current could be reduced, potentially further reducing noise. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aNoise control. =650 \0$aIndustrial noise. =700 1\$aBills, Paul,$eauthor. =700 1\$aBlunt, Liam,$eauthor. =700 1\$aNicholson, Samuel Otto,$eauthor. =700 1\$aRacasan, Radu,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20180042.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190001 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190001$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTS840 =082 04$a628.5223 =100 1\$aPrasetyo, Vendy E.,$eauthor. =245 10$aA Wood Recovery Assessment Method Comparison between Batch and Cellular Production Systems in the Furniture Industry /$cVendy E. Prasetyo, Benoit Belleville, Barbara Ozarska, John P. T. Mo. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (17 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aEnhanced wood recovery mirrors a successful wood manufacturing operation. Studies of wood recovery in secondary wood processing, however, are scarce, particularly in furniture manufacturing. Although recovery rates are under the continuous surveillance of sophisticated technology, this attempt to monitor wood recovery would be especially challenging for small- to medium-sized furniture enterprises, as the capital investment in such technology would be substantial. This would hinder the possibility for improvements in production efficiency of the furniture industry. A methodology of wood recovery assessment in the furniture industry has been developed and proposed but has not been validated with a cellular production system, a different layout process and distinctive machinery, species, and other customer requirements. The objective of this study is to assess the wood recovery protocol individually used in batch and cellular production systems, followed by examining the wood recovery of furniture manufacturing in these distinct production systems. Two Indonesian medium-sized furniture companies that individually operate batch and cellular production systems were employed, and two methods, mass and volume, were used to assess wood recovery at each furniture-making station. There was a significant difference in cumulative wood recovery rates between batch and cellular production systems. Based on species and product dimensions, the average individual and cumulative wood recovery rates of furniture manufacturing resulted in a significant difference at the resawing and edging station. Large-dimension product recorded higher wood recovery level than small-dimension product. The wood recovery rates at the resawing and edging, surface planing, thickness planing, and trimming stations were mostly influenced by species, the quality of sawn timber, and cutting bills. Meanwhile, wood recovery at other stations was affected by product dimension and design. The mass method was the most acceptable method according to the measurement systems analysis. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aFurniture. =650 \0$aFurniture industry and trade. =700 1\$aBelleville, Benoit,$eauthor. =700 1\$aMo, John P. T.,$eauthor. =700 1\$aOzarska, Barbara,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190001.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190022 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190022$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTD794.5 =082 04$a658.5223 =100 1\$aFerrero, Vincenzo,$eauthor. =245 10$aValidating the Sustainability of Eco-Labeled Products Using a Triple-Bottom-Line Analysis /$cVincenzo Ferrero, Arvind Shankar Raman, Karl R. Haapala, Bryony DuPont. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (22 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aSustainability considerations are becoming an intrinsic part of product design and manufacturing. Today‘s consumers rely on package labeling to relay useful information about the environmental, social, and economic impacts of a given product. As such, eco-labeling has become an important influence on how consumers interpret the sustainability of products. Three categories of eco-labels are theorized: Type I labels are certified by a reputable third party; Type II are eco-labels that are self-declared, potentially lacking scientific merit; and Type III eco-labels indicate the public availability of product Life Cycle Assessment (LCA) data. Regardless of the type of eco-label used, it is uncertain if eco-labeling directly reflects improved product sustainability. This research focuses on exploring if eco-labeled products are veritably more sustainable. To do this, we perform a comparative study of eco-labeled and comparable conventional products using a triple-bottom-line sustainability assessment, including environmental, economic, and social impacts. Here we show that for a selected set of products, eco-labeling does, in fact, have a positive correlation with improved sustainability. On average, eco-labeled products have a 47.7 % reduced environmental impact, reduce product lifespan costs by 48.4 %, and are subject to positive social perception. However, Type II eco-labeling shows a slight negative correlation with product sustainability and economic cost. We found only one eco-labeled product (with Type II labeling) that had an increased environmental impact over the conventional alternative. In general, the results confirm that most eco-labels are indicative of improved product sustainability. However, there is evidence that suggests that eco-labeling, though accurate, can omit truths with intention to improve marketability. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aManufactures$xLabeling. =650 \0$aProduct management$xEnvironmental aspects. =700 1\$aDuPont, Bryony,$eauthor. =700 1\$aHaapala, Karl R.,$eauthor. =700 1\$aRaman, Arvind Shankar,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190022.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190027 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190027$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA1677 =082 04$a672.37223 =100 1\$aGaikwad, Aniruddha,$eauthor. =245 10$aIn Situ Monitoring of Thin-Wall Build Quality in Laser Powder Bed Fusion Using Deep Learning /$cAniruddha Gaikwad, Farhad Imani, Hui Yang, Edward Reutzel, Prahalada Rao. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (24 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThe goal of this work is to mitigate flaws in metal parts produced from the laser powder bed fusion (LPBF) additive manufacturing (AM) process. As a step toward this goal, the objective of this work is to predict the build quality of a part as it is being printed via deep learning of in situ layer-wise images acquired using an optical camera instrumented in the LPBF machine. To realize this objective, we designed a set of thin-wall features (fins) from titanium alloy (Ti-6Al-4V) material with a varying length-to-thickness ratio. These thin-wall test parts were printed under three different build orientations, and in situ images of their top surface were acquired during the process. The parts were examined offline using X-ray computed tomography (XCT), and their build quality was quantified in terms of statistical features, such as the thickness and consistency of its edges. Subsequently, a deep learning convolutional neural network (CNN) was trained to predict the XCT-derived statistical quality features using the layer-wise optical images of the thin-wall part as inputs. The statistical correlation between CNN-based predictions and XCT-observed quality measurements exceeds 85 %. This work has two outcomes consequential to the sustainability of AM: (1) it provides practitioners with a guideline for building thin-wall features with minimal defects, and (2) the high correlation between the offline XCT measurements and in situ sensor-based quality metrics substantiates the potential for applying deep learning approaches for the real-time prediction of build flaws in LPBF. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aManufacturing processes. =650 \0$aAerospace industries$xMaterials$xStandards. =700 1\$aImani, Farhad,$eauthor. =700 1\$aRao, Prahalada,$eauthor. =700 1\$aReutzel, Edward,$eauthor. =700 1\$aYang, Hui,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190027.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190032 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190032$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA1634 =082 04$a006.32223 =100 1\$aFerguson, Max,$eauthor. =245 10$aA Standardized PMML Format for Representing Convolutional Neural Networks with Application to Defect Detection /$cMax Ferguson, Yung-Tsun Tina Lee, Anantha Narayanan, Kincho H. Law. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (19 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aConvolutional neural networks are becoming a popular tool for image processing in the engineering and manufacturing sectors. However, managing the storage and distribution of trained models is still a difficult task that is partially due to the lack of standardized methods for deep neural network representation. Additionally, the interoperability between different machine learning frameworks remains poor. This article seeks to address this issue by proposing a standardized format for convolutional neural networks based on the Predictive Model Markup Language (PMML). A new standardized schema is proposed to represent a range of convolutional neural networks, including classification, regression, and semantic segmentation systems. To demonstrate the practical application of this standard, a semantic segmentation model, which is trained to detect casting defects in X-ray images, is represented in the proposed PMML format. A high-performance scoring engine is developed to evaluate images and videos against the PMML model. The utility of the proposed format and the scoring engine is evaluated by benchmarking the performance of the defect detection models on a range of different computational platforms. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aNeural networks (Computer science) =650 \0$aEngineering. =700 1\$aLaw, Kincho H.,$eauthor. =700 1\$aLee, Yung-Tsun Tina,$eauthor. =700 1\$aNarayanan, Anantha,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190032.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190048 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190048$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aHD38.5 =082 04$a658.503223 =100 1\$aZafarzadeh, Masoud,$eauthor. =245 10$aData-Driven Production Logistics – An Industrial Case Study on Potential and Challenges /$cMasoud Zafarzadeh, Magnus Wiktorsson, Jannicke Baalsrud Hauge, Yongkuk Jeong. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (26 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aProduction logistics is typically considered a nonvalue-adding activity with a low level of automation and digitalization. However, recent advancements in technology infrastructure for capturing real-time data are key enablers of smart production logistics and are expected to empower companies to adopt data-driven strategies for more responsive, efficient, and sustainable intrasite logistic systems. Still, empirical evidence is lacking on potential and challenges in industrial transitions toward such systems. The objective of this article is to analyze the potential and challenges of adopting data-driven production logistics based on an industrial case study at an international manufacturing company in the pharmaceutical industry. The industrial application is analyzed in relation to established frameworks for data-driven manufacturing, and key technology infrastructures are identified. The potential of adopting a data-driven solution for the industrial case is quantified through simulating a future scenario and relating the results to the five SCOR performance attributes: reliability, responsiveness, agility, cost, and asset management efficiency. The findings show that deploying a data-driven approach can improve the overall performance of the system. The improvements especially concern lead-time, utilization of resources and space, streamlining logistics processes, and synchronization between production and logistics. On the other hand, challenges in adopting this data-driven strategy include a lack of relevant competence, difficulties of creating technological infrastructure and indistinct vision, and issues with integrity. Key contributions of the article include the analysis of a real industrial case for identification of potential and challenges while adopting a smart and data-driven production logistics. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aProduction planning. =650 \0$aIndustrial engineering. =700 1\$aHauge, Jannicke Baalsrud,$eauthor. =700 1\$aJeong, Yongkuk,$eauthor. =700 1\$aWiktorsson, Magnus,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190048.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190051 =003 IN-ChSCO =005 20200125061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200125s2019\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190051$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190049$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA455.P58 =082 04$a620.1920287223 =100 1\$aMiller, Arielle,$eauthor. =245 10$aGuidance on the Use of Existing ASTM Polymer Testing Standards for ABS Parts Fabricated Using FFF /$cArielle Miller, Celeste Brown, Grant Warner. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2019. =300 \\$a1 online resource (17 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aWhen evaluating the static mechanical performance of fused filament fabrication (FFF) polymers, researchers have conducted tensile testing using ASTM D3039, Standard Test Method for Tensile Properties of Polymer Matrix Composite Materials , and ASTM D638, Standard Test Method for Tensile Properties of Plastics . The choice of ASTM D638 versus ASTM D3039 test specimen geometry is usually based on the perceived susceptibility of the ASTM D638 test specimens to failure within the fillet radius. ASTM polymer and plastic tensile test standards define criteria for acceptably tested specimens as requiring failure inside the narrow length (i.e., ASTM D638) or outside of the grips (i.e., ASTM D3039) for the results to be considered acceptable. There has been limited published research regarding the selection of an ASTM test specimen geometry for FFF polymer materials. This study provides evidence-based guidance through the comparison of the mechanical performance and failure acceptance rates of ASTM D3039 and ASTM D638 test specimen geometries, fabricated using FFF acrylonitrile butadiene styrene. The purpose of this study is to provide guidance on the use of existing ASTM polymer testing standards for additively manufactured polymers. Results indicate there is an inherent benefit to using ASTM D3039 over ASTM D638 Type I and Type IV test specimens for tensile testing. ASTM D3039 test specimens provide the most consistent failure within the test specimen‘s gage length, which is attributed to the rectangular design of the test specimen. Results also indicated that, like traditionally manufactured polymer composites, there will be differences in the tensile test results (e.g., ultimate tensile strength, elastic modulus) based on the different cross-sectional areas of the test specimen geometries. =588 \\$aDescription based on publisher's website, viewed January 25, 2020. =650 \0$aPolymers$xTesting. =700 1\$aBrown, Celeste,$eauthor. =700 1\$aWarner, Grant,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 3, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2019$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190051.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190050 =003 IN-ChSCO =005 20200706061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200706s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190050$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190050$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTN693.T5 =082 04$a620.189322$223 =100 1\$aPasam, Vamsi Krishna,$eauthor. =245 10$aEffect of Vegetable Oil–Based Hybrid Nano-Cutting Fluids on Surface Integrity of Titanium Alloy in Machining Process /$cVamsi Krishna Pasam, Parimala Neelam. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (18 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aIn the present work, an attempt is made to examine the machining performance in turning titanium alloy using vegetable oil–based hybrid nano-cutting fluids. The sesame oil–based hybrid nanofluids with carbon nanotubes (CNT)/molybdenum disulphide (MoS 2 ) in the ratio of 1:2 are used to test the effect of surface integrity in the machining of titanium alloy. The cutting forces, cutting temperatures, surface roughness, and microhardness of the machined surface were measured. The machined surface is analyzed for machining-induced residual stresses and changes in the surface topography. Machining performance improved with vegetable oil–based hybrid nano-cutting fluids in reducing cutting forces and cutting temperatures. The percentage reduction of temperatures in the cutting zone under 0.5, 1, 1.5, and 2 wt. % and conventional cutting fluids (CCFs) are 70.2, 58.2, 63.1, 67.3, and 56.7, respectively, compared with dry machining. The percentage reduction in cutting forces are found to be 39.4, 70.9, 42.7, 73.2, and 72.3 for CCF 0.5, 1, 1.5, and 2 wt. % nanoparticle inclusions (NPI) compared with dry cutting, respectively. The percentage reduction in surface roughness ( Ra ) under CCF is 9.6, 24.4, 23.2, 22.4, and 26.7 for 0.5, 1, 1.5, and 2 wt. % NPI respectively, compared with dry cutting. The microhardness of the machined surface increased, and residual stresses are encouraging with hybrid nano-cutting fluids. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed July 06, 2020. =650 \0$aTitanium alloys. =700 1\$aNeelam, Parimala,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 4, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2020$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190050.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20200001 =003 IN-ChSCO =005 20200706061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 200706s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200001$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200001$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ1077 =082 04$a665.5385$223 =100 1\$aGanesh Kumar, Poongavanam,$eauthor. =245 10$aThermal Stability, Density, Rheology, and Electrical Conductivity of Two Different Ionic Liquids for Solar Thermal Applications /$cPoongavanam Ganesh Kumar, Kumar Balaji, Duraisamy Sakthivadivel, Murugesan Renuka. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (14 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThis paper presents the thermophysical properties of the 1-butyl-3-methylimidazolium–based ionic liquids (ILs) with different oxidative anions such as hexafluorophosphate and tetrafluoroborate. Differential scanning calorimetry (DSC), thermogravimetric, and rheology studies were carried out for the properties‘ measurement. Heating and cooling rates of 1°C/min, 3°C/min, and 5°C/min were used for the DSC analysis. The specific heat capacity of the ILs was measured in the temperature range of -40 to 40°C. The experimental result shows that the specific heat increases with increasing the temperature and the viscosity decreases with increasing the temperature. From the thermogravimetric analysis, the onset temperature of the [BF 4 ] tetrafluoroborate and [PF 6 ] hexafluorophosphate was 362°C and 381°C. Dynamic viscosities of the ILs samples diminished exponentially with an increase in temperature. The density of the ILs samples diminished exponentially with the rising temperature. The electrical conductivity of tetrafluoroborate has the highest electrical conductivity compared to the hexafluorophosphate. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed July 06, 2020. =650 \0$aRheology. =700 1\$aBalaji, Kumar,$eauthor. =700 1\$aSakthivadivel, Duraisamy,$eauthor. =700 1\$aRenuka, Murugesan,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 4, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2020$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20200001.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190047 =003 IN-ChSCO =005 20201014061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 201014s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190047$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190047$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA170 =082 04$a338.927$223 =100 1\$aRaman, Arvind Shankar,$eauthor. =245 10$aDefining Near-Term to Long-Term Research Opportunities to Advance Metrics, Models, and Methods for Smart and Sustainable Manufacturing /$cArvind Shankar Raman, Karl R. Haapala, Kamyar Raoufi, Barbara S. Linke, William Z. Bernstein, K. C. Morris. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (24 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aOver the past century, research has focused on continuously improving the performance of manufacturing processes and systemsoften measured in terms of cost, quality, productivity, and material and energy efficiency. With the advent of smart manufacturing technologies better production equipment, sensing technologies, computational methods, and data analytics applied from the process to enterprise levels the potential for sustainability performance improvement is tremendous. Sustainable manufacturing seeks the best balance of a variety of performance measures to satisfy and optimize the goals of all stakeholders. Accurate measures of performance are the foundation on which sustainability objectives can be pursued. Historically, operational and information technologies have undergone disparate development, with little convergence across the domains. To focus future research efforts in advanced manufacturing, the authors organized a one-day workshop, sponsored by the U.S. National Science Foundation, at the joint manufacturing research conferences of the American Society of Mechanical Engineers and Society of Manufacturing Engineers. Research needs were identified to help harmonize disparate manufacturing metrics, models, and methods from across conventional manufacturing, nanomanufacturing, and additive/hybrid manufacturing processes and systems. Experts from academia and government labs presented invited lightning talks to discuss their perspectives on current advanced manufacturing research challenges. Workshop participants also provided their perspectives in facilitated brainstorming breakouts and a reflection activity. The aim was to define advanced manufacturing research and educational needs for improving manufacturing process performance through improved sustainability metrics, modeling approaches, and decision support methods. In addition to these workshop outcomes, a review of the recent literature is presented, which identifies research opportunities across several advanced manufacturing domains. Recommendations for future research describe the short-, mid-, and long-term needs of the advanced manufacturing community for enabling smart and sustainable manufacturing. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed October 14, 2020. =650 \0$aManufacturing industries$xEnvironmental aspects. =650 \0$aSustainable engineering. =700 1\$aHaapala, Karl R.,$eauthor. =700 1\$aRaoufi, Kamyar,$eauthor. =700 1\$aLinke, Barbara S.,$eauthor. =700 1\$aBernstein, William Z.,$eauthor. =700 1\$aMorris, K. C.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 4, Issue 2 Special Issue on Technology Infrastructure for Enabling Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2020$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190047.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190046 =003 IN-ChSCO =005 20201014061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 201014s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190046$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190046$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ1189 =082 04$a621.9023$223 =100 1\$aLynn, Roby,$eauthor. =245 14$aThe State of Integrated Computer-Aided Manufacturing/Computer Numerical Control: Prior Development and the Path Toward a Smarter Computer Numerical Controller /$cRoby Lynn, Moneer Helu, Mukul Sati, Tommy Tucker, Thomas Kurfess. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (18 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aCurrent industrial practice in automated manufacturing operations relies on low fidelity data transmission methods between computer numerical control (CNC) machine tools and the computer-aided manufacturing (CAM) systems used to program them. The typical language used to program CNC machines, known as G-Code, has been in existence for nearly sixty years and offers limited resolution for command data. In addition, the proprietary nature of industrial CNC systems hampers the ability of manufacturers to expand and improve upon the capability of existing machine tools. G-Code was not designed to support transmission of feedback data, and thus both the CAM system and higher level organizational control systems are frequently blind to the state of the production process. In response, separate standards that enable data exchange with machine tools have been used by industry, such as MTConnect and Open Platform Communications Unified Architecture. However, these standards enable data pathways that are independent of the G-Code command data pathway, and thus they provide practically no means to affect the state of a process on receipt of feedback data. As a result, control and data acquisition exist in separate realms, which makes the implementation of self-optimizing smart CNC systems challenging. This state-of-the-art review surveys existing methods for data transmission to and from machine tools and explores the current state of so-called integrated CAM/CNC systems that enable more thorough control of the machining process using intelligence built into the CAM system. The literature survey reveals that integrated CAM/CNC systems are impeded both by the data exchange methods used to interface with CNC systems in addition to the proprietary and closed architecture of the CNC systems themselves. Future directions in integrated CAM/CNC research are identified based on the requirements identified for such systems. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed October 14, 2020. =650 \0$aMachine-tools$xNumerical control. =650 \0$aComputer-aided design. =700 1\$aHelu, Moneer,$eauthor. =700 1\$aSati, Mukul,$eauthor. =700 1\$aTucker, Tommy,$eauthor. =700 1\$aKurfess, Thomas,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 4, Issue 2 Special Issue on Technology Infrastructure for Enabling Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2020$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190046.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190041 =003 IN-ChSCO =005 20201014061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 201014s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190041$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190041$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTS155 =082 04$a670.427$223 =100 1\$aMorris, K. C.,$eauthor. =245 10$aFoundations of Information Governance for Smart Manufacturing /$cK. C. Morris, Yan Lu, Simon Frechette. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (19 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThe manufacturing systems of the future will be even more dependent on data than they are today. More and more data and information are being collected and communicated throughout product development life cycles and across manufacturing value chains. To enable smarter manufacturing operations, new equipment often includes built-in data collection capabilities. Older equipment can be retrofitted inexpensively with sensors to collect a wide variety of data. Many manufacturers are in a quandary as to what to do with increasing quantities of data. Much hype currently surrounds the use of artificial intelligence (AI) to process large data sets, but manufacturers struggle to understand how AI can be applied to improve manufacturing system performance. The gap lies in the lack of good information governance practices for manufacturing. This paper defines information governance in the manufacturing context as the set of principles that allows for consistent, repeatable, and trustworthy processing and use of data. The paper identifies three foundations for good information governance that are needed in the manufacturing environment data quality, semantic context, and system context and reviews the surrounding and evolving body of work. The work includes a broad base of standard methods that combine to create reusable information from raw data formats. An example from an additive manufacturing case study is used to show how those detailed specifications create the governance needed to build trust in the systems. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed October 14, 2020. =650 \0$aArtificial intelligence$xIndustrial applications. =650 \0$aBlockchains (Databases) =700 1\$aLu, Yan,$eauthor. =700 1\$aFrechette, Simon,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 4, Issue 2 Special Issue on Technology Infrastructure for Enabling Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2020$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190041.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190034 =003 IN-ChSCO =005 20201014061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 201014s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190034$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190034$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTS183 =082 04$a670$223 =100 1\$aBarring, Maja,$eauthor. =245 10$aDigital Technologies Enabling Data of Production Systems for Decision Support /$cMaja Barring, Bjorn Johansson, Johan Stahre. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (18 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aUsing digital technologies can support the flow and use of digital information in a timelier manner both within and between production systems. Smart manufacturing systems use these enabling technologies to maximize the capabilities cost, delivery, flexibility, and quality. The focus in industry so far has mainly been on the technologies and systems, but to succeed in digitalization, it is vital to understand how the technologies can be put into practice to meet the needs of an industrial organization. The focus of this article is to demonstrate how digital technologies can be used to enable data from production systems to inform decisions and generate value for an organization. This article showcases this with examples from real-world industrial cases, and the technologies used are 3-D laser scanning and 5G communication. The 3-D laser scanning is used to collect spatial data to build a virtual representation of a factory, and 5G is utilized to collect machine data from various data sources, e.g., the machine computer and external sensors. The results show that available data can support multiple roles in the organization and throughout the different phases of a production system. The status of the machine can be monitored in real time, a design can be evaluated against a system's behavior, an organization can learn from the data transformed into information, and the virtual representation provides a very accurate and photorealistic representation of a system as-is. Digital technologies can enable more data representing the system and support the organization in knowing more about their processes when used in the right way. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed October 14, 2020. =650 \0$aManufacturing processes. =650 \0$aProduction management$xData processing. =700 1\$aJohansson, Bjorn,$eauthor. =700 1\$aStahre, Johan,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 4, Issue 2 Special Issue on Technology Infrastructure for Enabling Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2020$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190034.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190030 =003 IN-ChSCO =005 20201014061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 201014s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190030$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190030$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTS183 =082 04$a670$223 =100 1\$aDuffy, Annie,$eauthor. =245 10$aMethodology for Digitally Logging and Analyzing Manufacturing Issues Encountered on a Factory Floor /$cAnnie Duffy, Ken Bruton, Richard Harrington, Alexander Brem, Dominic O'Sullivan. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (19 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aIn recent years, manufacturing industries have moved toward smart manufacturing to improve efficiency and production levels. Part of this innovation of current processes includes digitizing information and improving access to machine information, which can be achieved through the introduction of new technology to assist with this transition. In order to maintain smooth processes, a variety of information sources must be available on the factory floor. This project aims to provide a proof of concept tool for digitization and access to production information, available on the factory floor during Pulse Walks. Additionally, this project will introduce a method of digitally storing issues discussed during the Pulse Walk, aiding identification of recurrent issues and problematic areas. The research in this article considers the development of an application operating as a digital information hub to carry out these functions. The methodology used to develop this tool is discussed, including observations of the Pulse Walk and a survey to determine the most useful information sources to include. The use cases for this tool are deliberated, and benefits are identified. The tool can assist with tracking recurring problems by using previously logged issues to create a historical database. The issue logging dashboard can be used for investigating reasons for machine downtime. Furthermore, this tool aims to improve production efficiency for a manufacturing line in a factory through issue tracking using the digitized issue log. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed October 14, 2020. =650 \0$aManufacturing processes. =700 1\$aBruton, Ken,$eauthor. =700 1\$aHarrington, Richard,$eauthor. =700 1\$aBrem, Alexander,$eauthor. =700 1\$aO'Sullivan, Dominic,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 4, Issue 2 Special Issue on Technology Infrastructure for Enabling Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2020$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190030.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190043 =003 IN-ChSCO =005 20201014061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 201014s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190043$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190043$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTS155 =082 04$a670.427$223 =100 1\$aHu, Qianyu,$eauthor. =245 10$aPrivacy-Preserving Data Mining for Smart Manufacturing /$cQianyu Hu, Ruimin Chen, Hui Yang, Soundar Kumara. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (22 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aInternet of Things (IoT) and data mining techniques have laid the foundation for the next generation of smart and secure manufacturing systems where big data are leveraged to extract useful information about the manufacturing processes and further help optimize decisions. The threat of data breach exists especially for nonpersonal, yet sensitive data, which are pertinent to every aspect of manufacturing. Data breach and privacy leakages can significantly impede the manufacturer's business and lead to damaging a company's reputation. With a comprehensive case study in the manufacturing setting, we show that adversaries can utilize accessible shop floor predictive models and other available background information to make inferences about sensitive attributes that were used as inputs to the original model and use that information for their own purposes. From this view, this article presents a privacy-preserving data mining framework to build a smart and secure manufacturing system. First, we introduce differential privacy (DP), an emerging approach to preserve the individual/s privacy in the data mining process. Second, we present a privacy-preserving system where DP mechanisms and queries are enforced to obtain differentially private results. Third, we propose to optimize the selection of DP mechanisms and privacy parameters by balancing the model utility and the robustness to attack. Further, we evaluate and validate the proposed privacy-preserving data mining framework with a real-world case study on the modeling of cutting power consumption in computer numerical control turning processes. Experimental results show that the performance gain, i.e., the trade-off between model utility and the robustness to attack, is improved from the nonprivate model by 5.6, 9.4, and 13.1 % for privacy-preserving Laplace, Gaussian, and sensitive mechanisms, respectively. This article is among the first to investigate and present a privacy-preserving data mining framework for smart manufacturing. The proposed methodology shows great potential to be generally applicable in industry for data-enabled smart and sustainable manufacturing. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed October 14, 2020. =650 \0$aArtificial intelligence$xIndustrial applications. =650 \0$aBlockchains (Databases) =700 1\$aChen, Ruimin,$eauthor. =700 1\$aYang, Hui,$eauthor. =700 1\$aKumara, Soundar,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 4, Issue 2 Special Issue on Technology Infrastructure for Enabling Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2020$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190043.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190039 =003 IN-ChSCO =005 20201014061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 201014s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190039$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190039$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aQ334 =082 04$a006.3$223 =100 1\$aPuranik, Tejas,$eauthor. =245 10$aBenchmarking Deep Neural Network Architectures for Machining Tool Anomaly Detection /$cTejas Puranik, Aroua Gharbi, Burak Bagdatli, Olivia Pinon Fischer, Dimitri N. Mavris. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (25 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aWith the democratization of cyber-physical systems, edge computing, and large-scale data infrastructure, the volume of operational data available is continuously increasing. One of the significant challenges in current industrial research is defining a robust and scalable approach for machine health monitoring and anomaly detection. The methods that exist for such purposes rely extensively on feature engineering and are strongly dependent on the expertise of the operator, hence limiting their generalization. Deep learning techniques, on the other hand, are known to automate feature engineering and allow complex abstractions to be learned, making them particularly suitable for machine health monitoring. This paper presents a benchmarking of deep neural network architectures for the identification of machining tool anomalies on a lathe machine. The features are generated using indirect metrics such as sensor data and process variables from the machine controller, but without direct metrics like surface roughness or finished part quality. The ability of different architectures to identify incipient anomalies in tool quality is compared. A detailed treatment of various subsets of features is provided along with their relative importance to identify the minimum required parameters for accurately identifying the tool anomaly for each architecture considered. Finally, a recommendation is provided based on the results obtained on the type of architecture that is appropriate for the identification of machining tool anomalies. It is expected that the embeddings of the training data learned by the chosen network can be used for other learning tasks, such as transfer learning to another machine or anomaly type. The methodology described in this paper lends itself well to continuous monitoring through the use of scalable robust models and appropriate units of analysis for each model. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed October 14, 2020. =650 \0$aNeural computers. =650 \0$aComputer architecture. =700 1\$aGharbi, Aroua,$eauthor. =700 1\$aBagdatli, Burak,$eauthor. =700 1\$aFischer, Olivia Pinon,$eauthor. =700 1\$aMavris, Dimitri N.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 4, Issue 2 Special Issue on Technology Infrastructure for Enabling Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2020$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190039.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190036 =003 IN-ChSCO =005 20201014061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 201014s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190036$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190036$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aQA76.76.Q35 =082 04$a005.14$223 =100 1\$aAdrita, Mumtahina Mahajabin,$eauthor. =245 10$aDevelopment of a Decision Support System to Enable Adaptive Manufacturing /$cMumtahina Mahajabin Adrita, Alexander Brem, Patrick O'Neill, Eymard Gorman, Dominic O'Sullivan, Ken Bruton. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (17 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThe drive for Industry 4.0 has allowed manufacturing companies to stay at the forefront of business competition by making vast improvements over the years. Adaptive manufacturing is a key area for industries to explore where production processes can be fully automated using advanced technologies. However, such processes can generate a large collection of data, and there are difficulties with analyzing the data to derive useful information that will be used to assist the end users in making decisions for any disturbance in the machine during production. An expert system was developed in this study that sends offset feedback from the coordinate measuring machine to the computer numerical control (CNC) machine and aligns the tools appropriately within the CNC, thus extending tool life. Critical-to-quality (CTQ) dimensions were identified from scrap history, and decision trees were developed for each CTQ by using heuristic knowledge of the end users. Thus, this novel approach, which uses a rule-based method because of a lack of training data, includes a set of IF THEN statements codified from the decision trees, describing the alterations required on the critical tool if the measurements are not within specification. The ruleset was tested by force-failing the tools associated with the rules where the triggered responses were checked against the stated responses. Although the rule-based expert system proved to be successful at making offset changes for correcting tool positioning, further improvements to the ruleset are required to tackle any uncertainty, and operator interaction needs to be assessed to rule out the non value-adding steps and to codify useful tacit knowledge. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed October 14, 2020. =650 \0$aAdaptive control systems. =650 \0$aControl theory. =700 1\$aBrem, Alexander,$eauthor. =700 1\$aO'Neill, Patrick,$eauthor. =700 1\$aGorman, Eymard,$eauthor. =700 1\$aO'Sullivan, Dominic,$eauthor. =700 1\$aBruton, Ken,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 4, Issue 2 Special Issue on Technology Infrastructure for Enabling Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2020$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190036.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190044 =003 IN-ChSCO =005 20201014061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 201014s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190044$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190044$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA418.84 =082 04$a620.11294$223 =100 1\$aNakkina, Tapan Ganatma,$eauthor. =245 10$aIdentification of Microstructures in 3-D Printed Ti-6Al-4V Using Acoustic Emission Cepstrum /$cTapan Ganatma Nakkina, Ashif Sikandar Iquebal, Rama Krishna Sai S. Gorthi, Satish Bukkapatnam. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (16 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aRecent advances in smart hybrid machine tools allow the manufacturing of components with materials discovered on demand from certain common material precursors. Imperative to on-demand material discovery is the ability to probe and characterize the microstructure and salient properties of the materials as they are created. The article focuses on harnessing the complex spectral characteristics of high-resolution acoustic emission (AE) sensor signal generated during a nanoindentation-based scanning probe lithography process to classify the different surface microstructure types of additively manufactured Ti-6Al-4V components. We demonstrate that the low-frequency mel frequency cepstral coefficients (MFCCs) provide highly informative signatures of the AE processes to make inferences about the microstructures. We also show that unlike the well-known time-frequency features of AE, including those gathered via spectrograms, the MFCC compactly capture the variation of the energies of different frequency bands and enable classification of different microstructure types with as simple classifier as logistic regression. Via extensive nanoindentation experiments and analysis of the AE signals, we identify the specific MFCCs that are most important for discriminating between two different microstructure types of Ti-6Al-4V with accuracies estimated via extensive cross-validation close to 100 %. The proposed approach of using MFCCs offers a fast and efficient way of identifying different microstructure types of a given material system compared with conventional approaches, such as X-ray diffraction and scanning electron microscopy. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed October 14, 2020. =650 \0$aAcoustic emission. =650 \0$aAcoustic emission testing. =700 1\$aIquebal, Ashif Sikandar,$eauthor. =700 1\$aGorthi, Rama Krishna Sai S.,$eauthor. =700 1\$aBukkapatnam, Satish,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 4, Issue 2 Special Issue on Technology Infrastructure for Enabling Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2020$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190044.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190042 =003 IN-ChSCO =005 20201014061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 201014s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190042$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190042$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA418.84 =082 04$a620.11294$223 =100 1\$aWang, Zimo,$eauthor. =245 10$aBidirectional Gated Recurrent Deep Learning Neural Networks for Smart Acoustic Emission Sensing of Natural Fiber-Reinforced Polymer Composite Machining Process /$cZimo Wang, Pawan Dixit, Faissal Chegdani, Behrouz Takabi, Bruce L. Tai, Mohamed El Mansori, Satish Bukkapatnam. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (20 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aNatural fiber reinforced polymer (NFRP) composites are increasingly considered in the industry for creating environmentally benign product alternatives. The complex structure of the fibers and their random distribution within the matrix basis impede the machinability of NFRP composites as well as the resulting product quality. This article investigates a smart process monitoring approach that employs acoustic emission (AE) elastic waves sourced from various plastic deformation and fracture mechanisms to characterize the variations in the NFRP machining process. The state-of-the-art analytic tools are incapable of handling the transient dynamic patterns with long-term correlations and bursts in AE and how process conditions and the underlying material removal mechanisms affect these patterns. To address this gap, we investigated two types of the bidirectional gated recurrent deep learning neural network (BD-GRNN) models, viz., bidirectional long short-term memory and bidirectional gated recurrent unit to predict the process conditions based on dynamic AE patterns. The models are tested on the AE signals gathered from orthogonal cutting experiments on NFRP samples performed at six different cutting speeds and three fiber orientations. The results from the experimental study suggest that BD-GRNNs can correctly predict (around 87 % accuracy) the cutting conditions based on the extracted temporal-spectral features of AE signals. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed October 14, 2020. =650 \0$aAcoustic emission. =650 \0$aAcoustic emission testing. =700 1\$aDixit, Pawan,$eauthor. =700 1\$aChegdani, Faissal,$eauthor. =700 1\$aTakabi, Behrouz,$eauthor. =700 1\$aTai, Bruce L.,$eauthor. =700 1\$aEl Mansori, Mohamed,$eauthor. =700 1\$aBukkapatnam, Satish,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 4, Issue 2 Special Issue on Technology Infrastructure for Enabling Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2020$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190042.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190040 =003 IN-ChSCO =005 20201014061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 201014s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190040$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190040$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ233 =082 04$a621.8150285$223 =100 1\$aWang, Zhigang,$eauthor. =245 10$aSmart Machining Process Monitoring Enabled by Contextualized Process Profiles for Synchronization /$cZhigang Wang, Timothy C. Wagner, Changsheng Guo. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (11 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aRepeated machine downtime leads to lost productivity, late deliveries, and dissatisfied customers. Plants often struggle with meeting delivery commitment because they lack advanced predictive technologies. Because of limited information from current production practice, it is very challenging to control product quality, meet the tight tolerance, and eliminate scrap parts, which is especially critical for expensive aeroengine components. With the recent development of Industrial Internet of Things and Operation Technologies, it is now feasible to digitize the production process and take machine reliability and performance to a new level. In this study, the machining process is simulated in the virtual environment, and the output response is used as a reference digital thread of the process. In real-time machining, the computer numerical control (CNC) control signals together with existing machinery sensors and historical data are monitored and fused together to observe the machining conditions. Comparing the reference signature with supervised machine learning analytics, any subtle deviations in operating behavior, which are often the early warning signs of problems, can be identified. Also, with the look-ahead function, it gives the operators better visibility into the machining process. With the proposed method, without adding any more hardware to existing production machineries, it is feasible to capture data in real time, conduct automated analysis of the information, and create visualizations for team members. Plus, the real-time monitoring of any deviation in operating behavior during production can protect machine and cutting tools from excessive load and damage, thereby reducing operating costs and increasing effective manufacturing capacity. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed October 14, 2020. =650 \0$aMachine design$xData processing. =700 1\$aWagner, Timothy C.,$eauthor. =700 1\$aGuo, Changsheng,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 4, Issue 2 Special Issue on Technology Infrastructure for Enabling Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2020$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190040.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190045 =003 IN-ChSCO =005 20201014061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 201014s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190045$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190045$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ1280 =082 04$a621.92$223 =100 1\$aKamath, Akshay Katapadi,$eauthor. =245 10$aEnabling Advanced Process Control for Manual Grinding Operations /$cAkshay Katapadi Kamath, Barbara S. Linke, Chih-Hsing Chu. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (21 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aManual grinding is an abrasive manufacturing process commonly employed in the automotive, aerospace, and medical industries for deburring, finishing, and engraving operations. Unlike other manufacturing processes in which automation drives constant improvement, the operator's skill set continues to play a pivotal role in manual grinding. Process parameters such as grinding force and feed rate are dependent on the pressure and manual feed rate provided by the operator as well as the operator's tool movement during the process. Therefore, it is essential to quantify the manual skills involved in the process in order to develop a real-time feedback system, which can assist the operator for in-process corrective action. Manual operations such as manual grinding have not fully utilized the Industrial Internet of Things yet. This article focuses on developing a robust experimental setup to effectively monitor operator-controlled variables (tool feed rate and tool circumferential speed) and process information variables (grinding force, workpiece acceleration, and grinding power). Experiments are carried out to understand the relationships between the variables and their impacts on process outcomes (surface roughness and material removal rate). In addition, grinding energy is evaluated to improve grinding efficiency and sustainability. The developed test setup consists of a power tool, a piezoelectric force sensor, a motion-tracking-based feed rate sensor, and additional sensors. An alumina sanding band is used to grind aluminum 6061-T6 and hardened steel AISI 416 workpieces. Profilometer and confocal surface measurements are carried out for the test specimens to assess various two-dimensional and three-dimensional surface roughness parameters. Findings derived from the experimental results may lay a foundation for understanding and controlling manual grinding operations and enable their integration in smart and sustainable manufacturing systems. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed October 14, 2020. =650 \0$aGrinding and polishing. =700 1\$aLinke, Barbara S.,$eauthor. =700 1\$aChu, Chih-Hsing,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 4, Issue 2 Special Issue on Technology Infrastructure for Enabling Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2020$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190045.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20190033 =003 IN-ChSCO =005 20201014061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 201014s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20190033$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20190033$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aF403.3 =082 04$a658.5036$223 =100 1\$aNafors, Daniel,$eauthor. =245 10$aApplication of a Hybrid Digital Twin Concept for Factory Layout Planning /$cDaniel Nafors, Jonatan Berglund, Liang Gong, Bjorn Johansson, Thomas Sandberg, Jesper Birberg. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (14 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aAs the modern society is in the middle of its fourth industrial revolution, many enabling technologies are now viable to use in an engineering context. Several of these technologies are mature and available off the shelf; however, in the industrial setting they are rather novel. Two of these are virtual reality (VR), which has grown immensely in the gaming sector, and 3-D imaging, which is commonly used in archeology and construction. This study presents lessons learned from combining these two technologies in an industrial context with the digital twin concept. Three industrial case studies have been performed, and several observations have been identified in all three aspects of sustainability. For example, improved solution fidelity at an early stage can be achieved by externalizing tacit knowledge, and multiple issues during planning and installation phases have been avoided by utilizing the hybrid digital twin models. This type of digital twin enables highly detailed production system access, enabling engineering abilities from anywhere, anytime. Furthermore, the model becomes a powerful communication tool, which has reduced the resistance to workplace changes, as stakeholders lacking computer-aided design (CAD) knowledge can be involved in the change process. The highly detailed models have also allowed more focus to be put on safety and regulations, as these aspects naturally are more suited to experience in immersive VR. In conclusion, the hybrid digital twin concept developed in this study is a promising tool for decision makers and stakeholders alike, bound to benefit those who use it in all three aspects of sustainability. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed October 14, 2020. =650 \0$aEngineering$xManagement. =700 1\$aBerglund, Jonatan,$eauthor. =700 1\$aGong, Liang,$eauthor. =700 1\$aJohansson, Bjorn,$eauthor. =700 1\$aSandberg, Thomas,$eauthor. =700 1\$aBirberg, Jesper,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 4, Issue 2 Special Issue on Technology Infrastructure for Enabling Smart Manufacturing.$dWest Conshohocken, Pa. :$bASTM International, 2020$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190033.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20210999 =003 IN-ChSCO =005 20210722061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 210722s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20210999$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20210999$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA170 =082 04$a333.793$223 =100 1\$aRickli, Jeremy L.,$eauthor. =245 10$aEditorial: Special Issue on Education and Curriculum for Smart and Sustainable Manufacturing /$cJeremy L. Rickli, Yinlun Huang. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (3 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aSmart and sustainable manufacturing are future strategies for global competitiveness by manufacturing industries. Smart manufacturing intersects operational technologies and information technologies to develop sensor networks, autonomous controls, and high level enterprise management software to enhance manufacturing operations. Implementing smart manufacturing strategies is predicted to result in step changes in efficiency and productivity, offering a competitive advantage for smart manufacturing adopters. Sustainable manufacturing incorporates environmental, social, and economic aspects into manufacturing design, operation, and decision making in order to establish a sustained competitive advantaged locally and globally. Research into technical challenges has been ongoing for numerous years, but adoption by industries requires not only technical achievements in smart and sustainable manufacturing methods, but also advancements in education and curriculums for smart and sustainable manufacturing. When combined, educational and technical advancements in smart and sustainable manufacturing will contribute to an increase in adoption of smart and sustainable manufacturing methods. The papers in this special issue of Smart and Sustainable Manufacturing Systems focus on advances and outcomes of traditional and non-traditional education initiatives, learning approaches, and curricula in smart and sustainable manufacturing systems. Theoretical and practical knowledge in smart and sustainable manufacturing will be critical in the future manufacturing workforce. New approaches to teaching, training, and designing programs around smart and sustainable manufacturing systems, which can have complex and multi-scale interactions, are necessary to developing these skills in the next generation of engineers. The issue welcomed submissions across a spectrum of smart and sustainable manufacturing learning approaches and engineering disciplines, including but not limited to research experiences for undergraduates and teachers, new teaching methods for smart and sustainable manufacturing, community engaged teaching elements, and new programs or curriculum development to close the smart and sustainable manufacturing skill gap. This special issue provides an avenue to disseminate and share educational results and experiences in designing, operating, and evaluating smart and sustainable manufacturing based research experiences for undergraduate, graduate, and teacher education initiatives, learning approaches, and curriculum. In total, the special issue contains 7 papers that focus on new education methods and curriculum designs that foster knowledge and skills in smart and sustainable manufacturing. Topics within these 7 papers range from household decision making to course-based learning approaches and curriculum innovations. Research experiences in smart and sustainable manufacturing aim to provide learning experiences that are able to incorporate the multi-disciplinary and complex system aspects of smart and sustainable manufacturing systems. Research projects can span a broad array of research topics due to the wide scope of smart and sustainable manufacturing system research, thus, these experiences can involve multiple faculty members along with discussions that aim to connect sub-topics to broader smart and sustainable manufacturing concepts. Kovalenko et al. present the design and outcomes of a Secure Cloud Manufacturing Multidisciplinary Design Program where students learn smart manufacturing through multi-semester long projects advised by multiple faculty members, graduate students, and research scientists. Upon completion, the program expects that students understand the needs and capabilities of future manufacturing systems. Course level education approaches that aim to implement teaching pedagogies can support smart and sustainable manufacturing learning. This issue contains 3 papers that highlight advancements in course level pedagogies for smart and sustainable manufacturing system education. Amini-Rankouhi and Huang develop team-based learning approaches into a senior chemical engineering design course in order to incorporate and assess sustainability learning gains. Implementation in a senior design course allowed them to assess sustainability learning of students that already have foundational knowledge in chemical engineering and how students integrated sustainability concepts into high level senior projects required near the completion of students‘ chemical engineering degree. Lou et al. developed a problem-based learning approach for an advanced analysis course in chemical engineering that aims to teach advanced data analytics skills to chemical engineers using problems based on real-world scenarios. Student perceptions of the problem-based approach were evaluated, and follow-up was assessed to determine if student work benefited from the approach. Menandro and Arnab reviewed game-based teaching and learning approaches, specifically in mechanical engineering programs. More than 200 papers were reviewed and evaluated based on the gamification approach, education objectives, and fit in a mechanical engineering curriculum. Two curricula level papers are included in this issue. These papers go beyond a single course and aim to transform entire curricula for smart and sustainable manufacturing education. Raoufi et al. developed and implemented an adaptive undergraduate curriculum in manufacturing. The curriculum contains a set of foundational courses that are supported by courses in manufacturing systems and product development. The adaptive aspect enables educators to address needs for manufacturing systems, product development, smart manufacturing, and sustainable manufacturing learning while being able to support local industry needs. Li and Lin emphasize curriculum innovations that enable the integration of manufacturing based materials and quality control standards. This NIST-supported project incorporates development for in-class and online formats. Smart and sustainable manufacturing systems take into consideration life cycle stages beyond the scope of the factory, such as use phase and end-of-use or end-of-life phases. Browne and Moloney evaluated recycling and landfill sorting initiatives in the end-of-use stage in order to understand the effectiveness waste education interventions that aim to decrease contamination in recyclable material collection. The impact of recycling and landfill waste stream education interventions was evaluated and compared. Smart and sustainable manufacturing education and curriculum research is an active area both nationally and globally. Developments in this area are being undertaken by a group of researchers who recognize that technical research efforts must be combined with education efforts in order to implement and integrate smart and sustainable manufacturing systems into current industry practices. The selection of papers in this issue provides a look into the state-of-the-art of smart and sustainable manufacturing education programs that are currently being developed or executed. We anticipate that academic researchers, educators, and industry organizations or professionals will be attracted to this issue as universities aim to offer cutting edge manufacturing education programs and industries aim to attract top-tier talent with next-generation manufacturing skillsets. We acknowledge the financial support from the U.S. National Science Foundation (Award No. 1461031 and 1950192), and we appreciate all the authors and the editorial office of the journal for their efforts in the development of the special issue. =541 \\$aASTM International$3PDF$cPurchase price$hFREE =588 \\$aDescription based on publisher's website, viewed July 22, 2021. =650 \0$aManufacturing industries. =700 1\$aHuang, Yinlun,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 2.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20210999.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20200009 =003 IN-ChSCO =005 20210722061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 210722s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200009$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200009$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA170 =082 04$a333.793$223 =100 1\$aKovalenko, Ilya,$eauthor. =245 10$aDeveloping the Workforce for Next-Generation Smart Manufacturing Systems: A Multidisciplinary Research Team Approach /$cIlya Kovalenko, Efe C. Balta, Yassine Qamsane, Patricia D. Koman, Xiao Zhu, Yikai Lin, Dawn M. Tilbury, Z. Morley Mao, Kira Barton. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (21 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aAs real-world systems become more complex and connected, the industrial sector requires engineers who can solve problems across multiple disciplines and work with people across various educational backgrounds. This is particularly apparent in the manufacturing industry, as the integration of new manufacturing system technology requires knowledge in a diverse set of fields, such as physics, computer science, and engineering, to name a few. To properly educate the next workforce in manufacturing, engineering education needs to incorporate cross-disciplinary, project-driven learning that provides students with ample opportunities to work with cutting-edge manufacturing technology. At the University of Michigan, the Secure Cloud Manufacturing Multidisciplinary Design Program team focuses on developing the next generation of manufacturing engineers through research-driven multidisciplinary projects. A group of 7–22 students work on several multisemester-long projects that focus on providing hands-on, student-driven learning. Each semester, these students work closely with several faculty members, research scientists, postdocs, and graduate students to propose, develop, and conduct industry-relevant research projects on multiple manufacturing testbeds. Example projects have included the implementation of a smart quality-control camera, the development of digital twins for manufacturing processes, and the integration of secure cloud-based infrastructures for industrial controllers. In these highly collaborative and multidisciplinary project groups, students learn from each other, take on leadership roles, and disseminate their work through technical reports and presentations to academic and industry experts. Students leave the group with an understanding of the capabilities and needs of future manufacturing systems, ready to become, and lead, the next set of manufacturing engineers. =541 \\$aASTM International$3PDF$cPurchase price$hFREE =588 \\$aDescription based on publisher's website, viewed July 22, 2021. =650 \0$aManufacturing industries. =700 1\$aBalta, Efe C.,$eauthor. =700 1\$aQamsane, Yassine,$eauthor. =700 1\$aKoman, Patricia D.,$eauthor. =700 1\$aZhu, Xiao,$eauthor. =700 1\$aLin, Yikai,$eauthor. =700 1\$aTilbury, Dawn M.,$eauthor. =700 1\$aMorley Mao, Z.,$eauthor. =700 1\$aBarton, Kira,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 2.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20200009.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20200011 =003 IN-ChSCO =005 20210722061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 210722s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200011$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200011$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTP168 =082 04$a660.076$223 =100 1\$aAmini-Rankouhi, Aida,$eauthor. =245 10$aTeam–based Learning of Sustainability: Incorporation of Sustainability Concept and Assessment into Chemical Engineering Senior Design Course /$cAida Amini-Rankouhi, Yinlun Huang. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (11 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aDesign for Sustainability (DfS) becomes as an increasing important component in engineering education. One of the most effective educational strategies for sustainability integration is through undergraduate senior capstone courses. Chemical engineering as a main engineering discipline aims to prepare next-generation engineers with adequate knowledge and skills for pursing sustainable engineering in the near future. In this paper, we introduce our approach for introducing sustainability concepts and sustainability assessment methods into our undergraduate capstone design course. In the course, we guided students to conduct a sophisticated team project: Process Design, Modification, and Sustainability Assessment for Distributed Biodiesel Manufacturing. Students in teams developed and evaluated all design options from the sustainability point of view using different sustainability metrics systems and discussed for identification of most desirable solutions. Educational experience with the team-based approach was summarized through classroom evaluation. =541 \\$aASTM International$3PDF$cPurchase price$hFREE =588 \\$aDescription based on publisher's website, viewed July 22, 2021. =650 \0$aChemical engineering. =700 1\$aHuang, Yinlun,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 2.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20200011.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20200027 =003 IN-ChSCO =005 20210722061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 210722s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200027$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200027$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTP168 =082 04$a660.076$223 =100 1\$aLou, Helen H.,$eauthor. =245 10$aProblem-Based Learning on Incorporation of Data Analysis Skills into Chemical Engineering Senior Advanced Analysis Course /$cHelen H. Lou, Yifan Chen, Ravinder Singh. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (9 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThrough years of production, the chemical process industry has accumulated rich data assets. In the age of information and artificial intelligence, processing facilities are using data in new ways to improve efficiency, reliability, and safety. However, a skilled workforce is in shortage. To embrace the digital transformation, chemical engineering students need to get in-depth training for data analytics skills. In a senior Advanced Analysis class, a problem-based learning approach was utilized to incorporate data analysis skills into traditional curriculum using real-world problems and real plant data. Students diagnosed process operation data and planned maintenance for a series of heat exchangers based on condition rather than time. This approach was well received by the students, some of whom eventually became process engineers or maintenance engineers who are using this skill in their work. =541 \\$aASTM International$3PDF$cPurchase price$hFREE =588 \\$aDescription based on publisher's website, viewed July 22, 2021. =650 \0$aChemical engineering. =700 1\$aChen, Yifan,$eauthor. =700 1\$aSingh, Ravinder,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 2.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20200027.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20200003 =003 IN-ChSCO =005 20210722061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 210722s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200003$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200003$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ1 =082 04$a621$223 =100 1\$aMenandro, Fernando César Meira,$eauthor. =245 10$aGame-Based Mechanical Engineering Teaching and Learning - A Review /$cFernando César Meira Menandro, Sylvester Arnab. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (15 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThe usual approach to engineering education, though appropriate for some students, in general lacks adaptability for different learning needs. Game-based learning is an attempt to provide adaptable learner-centered education. This paper presents a review of the published works on game-based applications for Mechanical Engineering teaching and learning from the past five decades. A comprehensive review was performed, and more than 200 papers were screened, evaluating the gamification approach, educational objectives, and Mechanical Engineering curriculum. Bloom‘s taxonomy was used to identify cognitive learning outcomes for each of the games studied. There was also an attempt to establish Mechanical Engineering topics for an efficient curriculum and a correspondence of each game analyzed with the specific topic. The references found are presented according to Mechanical Engineering knowledge topic and cognitive learning outcome. Suggestions for further research on the field are made. The main ones include the need to formalize the educational objectives and development goals of the games, since most of the games studied did no such formalization, as well as the development and design strategies adopted to achieve such goals, a recently growing field of study. =541 \\$aASTM International$3PDF$cPurchase price$hFREE =588 \\$aDescription based on publisher's website, viewed July 22, 2021. =650 \0$aMechanical engineering. =700 1\$aArnab, Sylvester,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 2.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20200003.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20200008 =003 IN-ChSCO =005 20210722061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 210722s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200008$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200008$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ1180.A1 =082 04$a670.5$223 =100 1\$aRaoufi, Kamyar,$eauthor. =245 10$aDevelopment and Implementation of a Framework for Adaptive Undergraduate Curricula in Manufacturing Engineering /$cKamyar Raoufi, Brian K. Paul, Karl R. Haapala. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (20 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aAdvanced manufacturing, through the application of science and technology, compels an expanded view of the role of engineers in driving innovation. Advanced manufacturing requires engineers to imagine new ways of making products for smart, rapid, flexible, sustainable, and competitive production. Such manufacturing innovation is driving operational, tactical, and strategic advantages for industry while also creating a demand for a dynamic global workforce and market. The work herein supports the preparation of next-generation engineers for careers in academia and industry by developing and implementing a framework for adaptive manufacturing engineering curricula. The framework is founded upon a benchmarking study that applied the Society of Manufacturing Engineers Four Pillars of Manufacturing Knowledge Model to examine Accreditation Board for Engineering and Technology, Inc.–accredited undergraduate manufacturing engineering programs in the United States. Results of this work will enable universities, along with their industry partners, to identify topics that have garnered the attention of other curriculum developers and define opportunities for improvement. Thus, the adaptive framework can serve as a basis for defining how individual undergraduate programs can best meet the human resource needs of affiliated advanced manufacturing industry. To illustrate, a resulting revision to the manufacturing engineering curriculum at Oregon State University is described. The curriculum consists of a set of foundational courses and supporting thrusts in manufacturing systems and product development. The framework enables keystone options addressing needs for educating students in manufacturing systems, product development, smart manufacturing, and sustainable manufacturing. The framework supports local industry needs while taking advantage of faculty expertise. Initial implementation has demonstrated a positive student reception of the revised program, which also facilitates dual majors with industrial and mechanical engineering. =541 \\$aASTM International$3PDF$cPurchase price$hFREE =588 \\$aDescription based on publisher's website, viewed July 22, 2021. =650 \0$aProduction engineering. =700 1\$aPaul, Brian K.,$eauthor. =700 1\$aHaapala, Karl R.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 2.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20200008.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20200012 =003 IN-ChSCO =005 20210722061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 210722s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200012$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200012$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aT56.23 =082 04$a658.5$223 =100 1\$aLi, Hua,$eauthor. =245 10$aAn Innovation Framework to Integrate Engineering Standards into Industrial Engineering Graduate Curriculum /$cHua Li, Kai Jin. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (8 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThe importance of standards is that they provide product manufacturers with clear material, mechanical, and dimensional requirements coupled with specific quality assurance and test methods used to ensure their quality and functionality. Manufacturing-related material standards provide a consensus regarding desired properties between both manufacturers and customers. Moreover, these standards provide analysis methods to measure the properties and leads to standard manufacturing procedures that ensure the quality. To fulfill the need of real-world systems, a curriculum innovation was planned and implemented into the industrial engineering graduate program at Texas A&M University-Kingsville with the aim to integrate manufacturing-related materials and quality management standards into graduate engineering education through innovative course modules and a certificate program. There are four major components in the framework, including (1) course module development on manufacturing-related material standards, (2) course module development on manufacturing-related quality management standards, (3) graduate-level certificate program development, and (4) industrial experience sharing through webinars. ASTM and ISO standards were introduced to graduate students with hands-on experiences on applying standards to real-world case studies. It creates a systematic framework to strengthen graduate students‘ education and learning about manufacturing-related materials and quality management standards and standardization. Student surveys were used to collect their feedback, which show very positive impact on the students‘ knowledge and interests in standards and standardization. =541 \\$aASTM International$3PDF$cPurchase price$hFREE =588 \\$aDescription based on publisher's website, viewed July 22, 2021. =650 \0$aIndustrial engineering. =700 1\$aJin, Kai,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 2.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20200012.htm =LDR 03762nab 2200553 i 4500 =001 SSMS20200007 =003 IN-ChSCO =005 20210722061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 210722s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200007$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200007$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aHD4482 =082 04$a363.7282$223 =100 1\$aBrowne, C. A.,$eauthor. =245 10$aRecycling or Rubbish? Understanding Decision-Making in Household Recycling Programs /$cC. A. Browne, B. Moloney. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (12 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aMaterial recycling is an increasingly complex global problem. Unprecedented levels of material consumption across developed economies and continuous innovation around materials and manufacturing within the economy have made the practical challenge of closing the loop in recycling increasingly difficult. At the same time, political decisions at the global scale, such as exported recyclables not meeting standards for processing, are forcing local municipalities and councils to reconsider their approach to curbside recycling systems. In recent decades, local councils in Australia have tried various methods, such as education and awareness training, for decreasing contamination of recyclable material in curbside recycling bins. These efforts have been shown to have had a limited effect. To investigate this phenomenon at a local level, we conducted a practical workshop and knowledge experiment with undergraduate engineering students. Students were given a worksheet where they were required to sort household items into curbside recycling, drop-off recycling, or landfill. In this experiment, there were two intervention groups: a picture-based intervention and slogan-based intervention. We found that students in both groups were able to more reliably sort items intended for the Recycling waste stream than the Landfill waste stream. Further, we found that waste education interventions did little to improve correct sorting of items in the Recycling waste stream but did make a difference for Landfill items. This shows that methods for improving communication about material waste streams are needed to improve end-of-life disposal efforts, even within a cohort of young adults who have a background in engineering materials. =541 \\$aASTM International$3PDF$cPurchase price$hFREE =588 \\$aDescription based on publisher's website, viewed July 22, 2021. =650 \0$aRecycling (Waste, etc)$xEconomic aspects. =700 1\$aMoloney, B.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 2.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20200007.htm =LDR 03286nas a2200733 i 4500 =001 SSMS0501 =003 IN-ChSCO =005 20220129061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220129c20179999pau|||||o|||||||||||eng|| =022 \\$a2572-3928 =022 \\$z2520-6478 =030 \\$aSSMSCY =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA174 =082 04$a333.793$223 =245 00$aSmart and Sustainable Manufacturing Systems. =246 3\$aSSMS =246 3\$aASTM International Smart and Sustainable Manufacturing Systems =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2017- =310 \\$aAnnual =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =362 0\$aVol. 1, Issue 1 (January 2017)- =588 \\$aDescription based on: Volume 5, Issue 1; title from table of contents page (publisher's website, viewed Jan. 29, 2022). =588 \\$aLatest issue consulted: Volume 5, Issue 1 (viewed January 29, 2022). =650 \0$aEngineering design. =650 \0$aInformation science. =650 \0$aSystems engineering. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =856 40$uhttps://www.astm.org/journals/volume/listing/coden/SSMSCY/issue/1/volume/5/online-issue-date/2021-12-21+00%3A00%3A00/ =LDR 03286nas a2200733 i 4500 =001 SSMS0502 =003 IN-ChSCO =005 20220129061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220129c20179999pau|||||o|||||||||||eng|| =022 \\$a2572-3928 =022 \\$z2520-6478 =030 \\$aSSMSCY =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA174 =082 04$a333.793$223 =245 00$aSmart and Sustainable Manufacturing Systems. =246 3\$aSSMS =246 3\$aASTM International Smart and Sustainable Manufacturing Systems =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2017- =310 \\$aAnnual =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =362 0\$aVol. 1, Issue 1 (January 2017)- =588 \\$aDescription based on: Volume 5, Issue 2; title from table of contents page (publisher's website, viewed Jan. 29, 2022). =588 \\$aLatest issue consulted: Volume 5, Issue 2 (viewed January 29, 2022). =650 \0$aEngineering design. =650 \0$aInformation science. =650 \0$aSystems engineering. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aAmerican Society for Testing and Materials. =856 40$uhttps://www.astm.org/journals/volume/listing/coden/SSMSCY/issue/2/volume/5/online-issue-date/2021-05-27+00%3A00%3A00 =LDR 03762nab 2200553 i 4500 =001 SSMS20200040 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200040$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200040$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aQC754.2.M3 =082 04$a538.7$223 =100 1\$aHuang, Gang,$eauthor. =245 10$aResearch on Magnetic-Vibration Combination Treatment Based on Multifield Coupled Finite Element Analysis /$cGang Huang, Qingdong Zhang, Shuo Li. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (17 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aIn this paper, the three-dimensional simulation modeling of the magnetic-vibration combination residual stress reduction device was established based on the multifield coupled finite element analysis, and the residual stress reduction method of magnetic-vibration combination treatment was researched. The placement mode of the electromagnetic device and the factors affecting the effect of the magnetic-vibration combination reduction were comprehensively studied. The results showed that when the electromagnetic device was placed vertically, it was more beneficial to reduce the residual stress. The electromagnetic frequency was the maximal factor to affect the magnetic-vibration combination, and the factors that followed were voltage, material performance, exciting force, and exciting frequency. The greater the electrical conductivity of the material was, the lower the magnetic induction intensity was. The thicker the steel plate was, the easier it was to magnetize, but it was not conducive to vibration, and there was an optimal thickness under a certain set of parameters. Applying tension to the steel plate was beneficial to increase magnetization to reduce stress, and the aforementioned results of the simulation were consistent with the experimental results. The simulation study in this paper provided theoretical support for the process parameter setting of magnetic-vibration combination treatment to reduce residual stress. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aMagnetic fields$xResearch. =650 \0$aMagnetic resonance imaging. =650 \0$aNuclear magnetic resonance. =700 1\$aZhang, Qingdong,$eauthor. =700 1\$aLi, Shuo,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200040.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200030 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200030$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200030$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aHA29 =082 04$a519.535$223 =100 1\$aEddy, Douglas,$eauthor. =245 10$aRealization of System Robustness by Clustering to Predict New Product Performance Levels /$cDouglas Eddy, Sundar Krishnamurty, Ian Grosse. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (13 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aFinal test metrics that evaluate product system performance usually depend upon numerous variables, such as dimensions or other characteristics of parts and assemblies. Many product systems are expensively comprised of numerous parts. Therefore, during new product system development, the challenge becomes how to rapidly learn estimated system results from combinations of many variables at the smallest possible sample size to minimize cost and improve product quality. In this work, we introduce a fundamental Vector-Based Clustering technique to predict a cluster range of system test results for comparison to other machine learning techniques in a commercial software tool. This work expands to include two additional techniques that account for significance among many variables. All three of these techniques were tested and compared to the machine learning algorithm from a commercial tool best suited for each training set from a high dimensional open-source data set representative of manufacturing system data. These case study results show improvement in predictive accuracy over many prevalent machine learning techniques at small sample sizes. Furthermore, since a best-suited machine learning technique is selected by trial and error for each training set, the computational time is significantly improved as well. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aCluster analysis. =650 \0$aMathematical statistics. =650 \0$aDiscriminant analysis. =700 1\$aKrishnamurty, Sundar,$eauthor. =700 1\$aGrosse, Ian,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200030.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200041 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200041$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200041$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aQC318.J6 =082 04$a536.56$223 =100 1\$aMahapatro, Khirod,$eauthor. =245 10$aDesign of CO 2 -Based Cooling System for Machining of Ti-6Al-4V Using Joule–Thomson Effect /$cKhirod Mahapatro, Gaurav Mahendra, Abhishek Markandeya, P. Vamsi Krishna. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (18 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aHigh cutting temperatures are reported in the machining process because of a large amount of generated heat, which reduces the dimensional accuracy and quality of the machined surface. This paper presents a new methodology to design a carbon dioxide (CO2)-based cooling system in which the cooling effect is produced by using the Joule–Thomson effect during machining of Ti-6Al-4V. The finite element method and computational fluid dynamics are used to predict the tooltip temperature in machining, supply conditions of the coolant, and design parameters. The theoretical heat transfer rate of the tool and workpiece is compared with the simulated value to validate the model. After the validation, the turning experiments of dry machining and CO2-cooled machining are performed under constant cutting parameters. In this experimentation, the coolant supply conditions used are taken from the simulation. From the experimental results, it is observed that the CO2 cooling system provides a reduction in cutting temperature (46.66 %), flank wear (10 %), and surface roughness (46 %) compared with dry machining. However, cutting force is increased (about 33.33 %) because of the pressurized CO2 gas focused on the cutting tool. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aJoule-Thomson effect. =650 \0$aThermodynamics. =650 \0$aPhysics. =700 1\$aMahendra, Gaurav,$eauthor. =700 1\$aMarkandeya, Abhishek,$eauthor. =700 1\$aVamsi Krishna, P.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200041.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200026 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200026$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200026$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTK6553 =082 04$a621.384$223 =100 1\$aEslami, Mohammad,$eauthor. =245 10$aNonlinear Modeling for Low Frequency Oscillations Damping Using the Collective Intelligence Algorithms /$cMohammad Eslami. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (23 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThis paper presents a design for a fuzzy controller for low frequency oscillations using the improved virus colony search. In this way, the parameters and fuzzy members are considered variably, which ultimately has turned into an optimization issue. Therefore, the proper coordination between these variables leads to the best operating conditions and vice versa: inappropriate adjustment of the parameters may lead to a persistent exacerbation in applying controlling signals. Because of the complexity of the system and to avoid a large amount of computing, in this paper, optimization of control parameters has been proposed with the help of an improved virus colony search algorithm. The proposed controller has been studied under different operating conditions considering the interregional fluctuation and low frequencies. It is shown that the proposed fuzzy controller has a better performance for stabilizing damaging system disturbances in bad operating conditions. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aMathematical physics. =650 \0$aFrequencies of oscillating systems. =650 \0$aRadio frequency modulation. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200026.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200029 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200029$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200029$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA418.9.S62 =082 04$a620.11$223 =100 1\$ada Silveira Dib, Mario Alberto,$eauthor. =245 10$aFederated Learning as a Privacy-Providing Machine Learning for Defect Predictions in Smart Manufacturing /$cMario Alberto da Silveira Dib, Bernardete Ribeiro, Pedro Prates. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (17 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aIn this work, the federated learning methodology is applied to predict defects in sheet metal forming processes exposed to sources of scatter in the material properties and process parameters. Numerical simulations of the U-channel forming process were performed to analyze springback for three types of sheet steel materials. The datasets of different clients are used to train a single machine learning model. With this approach, multiple parties would simultaneously train a machine learning model on their combined data by training the models locally on the client nodes and progressively improving the learning model through interaction with the central server. This way the industrial peers have no access to the others local data in a centralized server. The predictive performance achieved is similar to a standard centralized learning method, offering competitive results of collaborative machine learning in industrial environment. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aSmart materials. =650 \0$aSmart structures. =650 \0$aSmart materials$xIndustrial applications. =700 1\$aRibeiro, Bernardete,$eauthor. =700 1\$aPrates, Pedro,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200029.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200057 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200057$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200057$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA174 =082 04$a620.0042$223 =100 1\$aDzyura, Volodymyr,$eauthor. =245 10$aDetermining Optimal Parameters of Grooves of Partially Regular Microrelief Formed on End Faces of Rotary Bodies /$cVolodymyr Dzyura, Pavlo Maruschak, Halyna Kozbur, Petro Kryvyi, Olegas Prentkovskis. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (12 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThe article considers the schemes of groove arrangement on a partially regular microrelief of type I with coaxial grooves formed on end faces of rotary bodies because of vibration. An analytical dependence was obtained to determine the relative area of a partially regular microrelief depending on the geometrical parameters of the V-shaped grooves. The parameters of the grooves formed at different distances from the center of rotation, with which the relative area of the partially regular microrelief will be the same, are determined. It is found that without adjusting the vibration amplitude of the deforming element, the relative area of the grooves will decrease significantly. The imprint radius of the deforming element and its influence on changes in the relative area of the vibrorolling are also found. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aEngineering design$vCongresses. =650 \0$aManufacturing processes$vCongresses. =650 \0$aTechnology$vCongresses. =700 1\$aMaruschak, Pavlo,$eauthor. =700 1\$aKozbur, Halyna,$eauthor. =700 1\$aKryvyi, Petro,$eauthor. =700 1\$aPrentkovskis, Olegas,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200057.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200076 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200076$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200076$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTP156.S45 =082 04$a660$223 =100 1\$aEl Din Mohamad, Hagar Alm,$eauthor. =245 10$aA Portable Active Photovoltaic Solar Energy System in Treating Wastewaters by Electrocoagulation /$cHagar Alm El Din Mohamad, M. H. A. Amr, A. A. Bastawissi, A. E. Bastawissi, Hitesh Panchal, Kishor Kumar Sadasivuni. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (9 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThis study presents the effects of adding some provisions to a portable photovoltaic (PV) solar energy system to obtain both DC and ac sources. An efficient ac high-speed fan was powered from this system to cool the PV module for reducing its power loss because of a temperature rise. A DC electrocoagulation (EC) cell was powered from the active PV system to treat real dairy wastewater. The results revealed that about 5 % loss in maximum power output of the PV module at 25°C was avoided by the added provisions while the EC cell efficiently treated the tested wastewater. For this wastewater, the maximum removal efficiencies of turbidity, suspended pollutants, and chemical oxygen demand were 97, 62, and 72.5 %, respectively. The cell effluent complied with the Egyptian requirements for discharge on public sewer system. This work indicates the technical feasibility of raising the efficiency of PV modules while producing both ac and DC sources. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aElectrocoagulation. =650 \0$aParticles. =650 \0$aSeparation (Technology) =700 1\$aAmr, M. H. A.,$eauthor. =700 1\$aBastawissi, A. A.,$eauthor. =700 1\$aBastawissi, A. E.,$eauthor. =700 1\$aPanchal, Hitesh,$eauthor. =700 1\$aSadasivuni, Kishor Kumar,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200076.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200082 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200082$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200082$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTS178.4 =082 04$a658.5$223 =100 1\$aMedini, Khaled,$eauthor. =245 10$aDeveloping a Multi-Agent System to Support Multi-Variant Production Ramp-Up Management /$cKhaled Medini, David Romero, Thorsten Wuest. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (19 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aIn the Industry 4.0 era, with increased demands for customization, numerous companies struggle to accommodate changes in their businesses and to keep up with the pace of digital transformation. It follows that introducing new products or services based on an existing configuration or a new design is becoming more frequent. Managing the production ramp-up phase carefully is therefore emerging as one of the most critical steps in the product lifecycle. Although several general strategies and high-level frameworks are available in recent ramp-up management literature, data-backed tools and frameworks that deal with this question are scarce. This article aims to bridge this gap by developing a multi-agent system (MAS) supporting ramp-up management strategies assessment in multi-variant production contexts. An MAS relies on the concept of an agent, which is an autonomous entity operating in a society of agents in order to contribute to a general goal. This article studies the method used to develop an MAS and illustrates its applicability through a case study in the furniture sector. The article highlights the reusability and flexibility of the development method and the relevance of the MAS to decision-makers during ramp-up planning. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aOperations Management. =650 \0$aAutomation$vCongresses. =650 \0$aProduction Ramp-Up. =650 \0$aMachining$xAutomatic control$vCongresses. =700 1\$aRomero, David,$eauthor. =700 1\$aWuest, Thorsten,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200082.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200084 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200084$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200084$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTS176 =082 04$a670$223 =100 1\$aRomero, David,$eauthor. =245 10$aSmart Wearable and Collaborative Technologies for the Operator 4.0 in the Present and Post-COVID Digital Manufacturing Worlds /$cDavid Romero, Thorsten Wuest, Makenzie Keepers, Lora A. Cavuoto, Fadel M. Megahed. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (19 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThis paper addresses the potential of smart wearable and collaborative technologies in support of healthier, safer, and more productive shop floor environments during the present and post– coronavirus 2019 pandemic emerging digital manufacturing worlds. It highlights the urgent need to “digitally transform” many high-touch shop floor operations into low-touch or no-touch ones, aiming not only at a safer but also more productive return to work as well as a healthier continuity of production operations in more socially sustainable working environments. Furthermore, it discusses the interrelated roles of people, data, and technology to develop smart and sustainable shop floor environments. Lastly, it provides relevant recommendations to the key business units in a manufacturing enterprise in regard to the adoption and leverage of smart wearable and collaborative technologies on the shop floor in order to ensure the short- and long-term operation of a factory amid the coronavirus 2019 pandemic and the future of production and work in the Industry 4.0 era. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aManufacturing processes. =650 \0$aProduction engineering. =650 \0$aManufacturing industries$xTechnological innovations. =700 1\$aWuest, Thorsten,$eauthor. =700 1\$aKeepers, Makenzie,$eauthor. =700 1\$aCavuoto, Lora A.,$eauthor. =700 1\$aMegahed, Fadel M.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200084.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200081 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200081$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200081$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTN693.T5 =082 04$a620.189322$223 =100 1\$aSivareddy, D. Venkata,$eauthor. =245 10$aExperimental Investigation on Flank Wear of the Tool in Ultrasonic Vibration-Assisted Turning of Ti6Al4V Alloy /$cD. Venkata Sivareddy, P. Vamsi Krishna, A. Venu Gopal. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (12 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aHigh cutting temperatures developed during machining of Ti6Al4V alloy diminish the tool life by rapid increase in rate of tool wear. The high cutting temperatures increase the chemical reactivity of the workpiece with tool material and form the localized temperature zones at the vicinity of cutting tool edge. Ultrasonic vibration-assisted turning (UVAT) is an effective cutting technique in which tangential vibrations are provided to the cutting tool. The reduced cutting force and temperature improve the tool life in the UVAT process. In the present study, flank wear of the uncoated carbide cutting tool has been studied in UVAT processes at various cutting speeds (90, 120, and 150 m/min) and ultrasonic powers (80, 90, and 100 %). The flank wear of the tool is measured with a scanning electron microscope, and it is observed that tool wear is low in UVAT compared with the conventional turning (CT) process. Fracture of the cutting edge of the tool is observed in CT because of high compressive stresses with the high cutting temperature, which did not occur in the UVAT process. The growth of average and maximum flank wear with cutting velocity is low in UVAT, whereas rapid growth is observed in the CT process. From the experimental results, tool life improvement is observed in UVAT compared with CT at an average flank wear of 300 µm; however, this improvement is decreased with an increase in cutting speed in the order of 62, 53.2, and 32 % for 90, 120, and 150 m/min, respectively, at 100 % ultrasonic power. Similarly, tool life is improved with an increase in ultrasonic power in the order of 18, 55, and 62 % for 80, 90, and 100 % ultrasonic power, respectively, at 90 m/min cutting speed. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aTitanium alloys. =650 \0$aHard materials$xMachining. =650 \0$aUltrasonic waves$xSafety measures. =700 1\$aKrishna, P. Vamsi,$eauthor. =700 1\$aGopal, A. Venu,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200081.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200070 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200070$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200070$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aHC79.E5 =082 04$a658.4/083$223 =100 1\$aVaidya, Uday,$eauthor. =245 10$aIACMI–The Composites Institute Efforts in Current and Post–COVID-19 Manufacturing Era—Innovations and Sustainability /$cUday Vaidya, John Hopkins. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (7 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aInstitute for Advanced Composites Manufacturing Innovation (IACMI)–The Composites Institute comprises 150 industry members making up the supply chain from material suppliers; Tiers 1, 2, and 3 original equipment manufacturer (OEM)s and fabricators; academia; and national laboratories. The institute is addressing the needs for medical systems support in response to the coronavirus 2019 (COVID-19) crisis and the recovery of the U.S. composites industry and connected manufacturing supply chains. The impact of the current and post-COVID response by IACMI and its partners extends across health care, automotive, transportation, construction, infrastructure, aerospace, sports, marine, and industrial sectors. Sustainability and circular economy of advanced materials and manufacturing play a key role in overall reduced embodied energy. This technical note provides a summary of IACMI efforts in response to COVID-19 and outlook for the post-COVID era with a focus on sustainable advanced materials and manufacturing. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aSustainable development. =650 \0$aSustainability. =650 \0$aBusiness & Economics$xEnvironmental Economics. =700 1\$aHopkins, John,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200070.html =LDR 03762nab 2200553 i 4500 =001 SSMS20210007 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20210007$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20210007$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTL718 =082 04$a614.78$223 =100 1\$aLu, Lance,$eauthor. =245 10$aEffects of Extrinsic Noise Factors on Machine Learning–Based Chatter Detection in Machining /$cLance Lu, Thomas Kurfess, Christopher Saldana. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (14 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aUnmitigated chatter can result in poor part quality, accelerated tool wear, and possible damage to spindle and machine. While various methods have been shown to effectively detect chatter, implementation of these methods in noisy environments, such as factory floors, has not been well studied. The present study seeks to explore the effects of extrinsic noise sources on threshold-based and machine learning–based chatter detection methods using audio signals of the machining process. To accomplish this, stable and unstable cuts were made on a milling machine and the audio signal was collected. Data augmentation using Gaussian white noise and periodic noise was conducted to simulate a range of noise levels and types. The performance of these techniques were then compared with respect to the increasing levels of noise. It was found that machine learning–based approaches achieved satisfactory accuracies up to 98.6 % under the presence of extrinsic noise. Conventional static threshold techniques, however, failed under most noise conditions and resulted in false positives depending on the threshold values used. Furthermore, support vector machine approaches demonstrated an ability to classify noisy data despite limited training. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aGround-effect machines$xNoise. =650 \0$aGround-effect machines$xTesting. =650 \0$aNoise control. =700 1\$aKurfess, Thomas,$eauthor. =700 1\$aSaldana, Christopher,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20210007.html =LDR 03762nab 2200553 i 4500 =001 SSMS20210010 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20210010$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20210010$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aT55.45 =082 04$a670.42$223 =100 1\$aGil, Santiago,$eauthor. =245 10$aAn Ontological Model to Integrate and Assist Virtualization of Automation Systems for Industry 4.0 /$cSantiago Gil, Germán D. Zapata-Madrigal, Gloria L. Giraldo-Gómez. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (27 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aAutomation engineering faces some challenges to properly address the development of automation systems and cyber-physical systems (CPSs) according to the concepts and principles of Industry 4.0. Currently, the design of these systems requires multidisciplinary knowledge to achieve integrated solutions, such as models, techniques, and technologies, commonly characterized by their complexity. The representation of CPSs, specifically the cyber-physical production systems, depends on describing dynamics behavior but also on relations, knowledge, and information. This is possible using formal engineering methods with other modeling approaches like ontologies. In this work, a semantic model is proposed to integrate automation systems and their associated components into a formal representation by employing an ontological model. The proposed model, the Automation I4.0 Ontology, is a modular approach that can represent several components of automation systems with an extended feature for the implementation in real environments through software applications and semantic automation networks. The ontology is intended to enable the representation and implementation of distributed service-based automation systems providing interoperability and cognition via ontology-based instances in the code, enabling the same format between applications and ontology files. Two case studies are proposed to show how the Automation I4.0 Ontology is easily integrated with Open Platform Communications Unified Architecture and how it can be used for model- and ontology-based developments of automation systems as a modeling, deployment, or virtualization method, obtaining extended features, such as online semantic reasoning and querying, assisted resource virtualization, and technological convergence with standardization, which currently are not provided by previous approaches. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aOntology$xResearch. =650 \0$aInterdisciplinary research. =650 \0$aOntology$xMethodology. =700 1\$aZapata-Madrigal, Germán D.,$eauthor. =700 1\$aGiraldo-Gómez, Gloria L.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20210010.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200074 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200074$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200074$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTS183 =082 04$a620.0042$223 =100 1\$aXiao, Heye,$eauthor. =245 10$aMethodology for Design Process of Internal Supported Cylindrical Thin Shell Made by Additive Manufacturing /$cHeye Xiao, Ruobing Wang, Xuefeng Li, Qi Zhang, Xudong Zhang, Junqiang Bai. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (11 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aTo improve specific stiffness and strength of cylinder type shell, it is essential to make use of free space inside the cylinder to generate internal supported structure for reinforcement. In this article, an available method is introduced to design this complex structure based on the advantages of additive manufacturing, which include the subprocess of design domain generation, topology optimization, reconstruction, and analysis. Firstly, the design domain inside cylinder is created by removing swept volume from cylindrical container space. The initial structure is obtained by topology optimization and reconstructed by considering the constraints of manufacturing. Then, mechanical properties of the refined structure are checked by finite element method to give a final model. A cylindrical thin shell with slider is chosen as an example to show the detailed work process of the proposed design method, and the designed structure is generated by selective laser melting technology finally. It is proved that the proposed method is suitable for designing additive manufacturing cylinder shell with internal support. Furthermore, a traditional cylindrical thin shell is selected as a benchmark to be compared with the designed model by mechanical properties. Through the comparisons, it is concluded that internal supported cylindrical thin shell has greater bearing capacity and less weight cost than the classical cylindrical structure. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aComposite materials$xFracture. =650 \0$aProduction control$xAutomation. =650 \0$aEngineering design. =700 1\$aWang, Ruobing,$eauthor. =700 1\$aLi, Xuefeng,$eauthor. =700 1\$aZhang, Qi,$eauthor. =700 1\$aZhang, Xudong,$eauthor. =700 1\$aBai, Junqiang,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200074.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200036 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200036$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200036$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA485 =082 04$a620.16$223 =100 1\$aAmrita, M.,$eauthor. =245 10$aSustainability Analysis of Machining Inconel 718 Using Graphene-Based Nanofluids and Self-Lubricating Tools /$cM. Amrita, Rukmini Srikant Revuru, B. Siva, B. Kamesh. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (23 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aInconel 718 has wide application due to its excellent strength at high temperatures and corrosion resistance. But because of the problems associated with machining, it is categorized as "hard-to-machine" material. The present work aims at identifying a sustainable method to machine Inconel 718 with the application of graphene. Sustainability evaluation consists of evaluating a product or process for the satisfaction of three E’s: employee, environment, and economy. In the present work, the effect of graphene-based cutting fluid and graphene-based self-lubricating tools on cutting forces and tool wear is evaluated while machining Inconel 718. In addition, economic analysis and carbon footprint analysis are carried out to verify the advantage and feasibility of using the formulated cutting fluids and self-lubricating tools. Furthermore, minimum quantity application of conventional cutting fluid and graphene-based nanofluids and dry machining using graphene-based self-lubricating tools are compared to estimate the best conditions for environmental impact. Minimum quantity application of 0.5 weight percent (wt %) graphene-based nanofluid showed the least tangential cutting forces, while 0.3 wt % showed the least tool wear. Tool wear decreased by ≈70–84 % with 0.3 wt % graphene-based nanofluid compared with dry machining over the velocity range of 65–115 m/min. At 112 m/min, the minimum quantity application of 0.3 wt % graphene-based nanofluid reduced carbon emission by 3,334 kg carbon dioxide compared with dry machining per machine tool per year. Minimum quantity application of 0.3 wt % graphene-based nanofluid is also found to be most economical compared with other environments at all cutting velocities showing ≈70–80 % reduction in expenditure compared with dry machining. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aMetal-cutting. =650 \0$aMetal-working lubricants. =650 \0$aInconel. =700 1\$aRevuru, Rukmini Srikant,$eauthor. =700 1\$aSiva, B.,$eauthor. =700 1\$aKamesh, B.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200036.html =LDR 03762nab 2200553 i 4500 =001 SSMS20210021 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20210021$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20210021$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aHD31.2 =082 04$a658.4038$223 =100 1\$aDayam, Sunidhi,$eauthor. =245 10$aIn-Process Dimension Monitoring System for Integration of Legacy Machine Tools into the Industry 4.0 Framework /$cSunidhi Dayam, K. A. Desai, Mathew Kuttolamadom. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (22 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aManufacturers are implementing sensors, Internet of Things (IoT)-based automation, and communication devices on shop floors for connecting machine tools to a network-connected system for achieving “smart” functionality. The existing installation of legacy machines that offer either no or limited adaptability to these changes is a big obstacle to realizing the potential benefits of smart manufacturing. This research paper presents a sensor-based dimension monitoring system to capture and digitize component dimensions during machining operations. The capabilities are attained by developing an integrated framework consisting of sensors, data acquisition systems, feature extraction modules, and digital interfaces. The framework is implemented on legacy equipment such as lathes, milling, and drilling machines for component dimension monitoring while performing common operations. The proposed system functions at the edge level to improve man (operator)–machine–material interactions by displaying component dimensions and graphical visualization of the operations. The system also helps the operator recognize the resulting cutting forces and thereby achieve guided process control. The data generated at an edge level can be transmitted to the enterprise layer for performing tasks such as machine performance evaluation, man–machine utilization, process optimization, operator feedback, etc. The proposed framework provides a potential solution for integrating a vast base of the existing legacy machines into the Industry 4.0 framework. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aIndustrial management$xData processing. =650 \0$aProduction management$xData processing. =650 \0$aIndustrial management$xTechnological innovations. =700 1\$aDesai, K. A.,$eauthor. =700 1\$aKuttolamadom, Mathew,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20210021.html =LDR 03762nab 2200553 i 4500 =001 SSMS20210004 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20210004$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20210004$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTK1001 =082 04$a621.31$223 =100 1\$aPouralizadeh, Mojgan,$eauthor. =245 10$aSustainability Assessment of Electricity Supply Chain via Resource Waste Reduction and Pollution Emissions Management :$bA Case Study of the Power Industry /$cMojgan Pouralizadeh. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (21 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThe energy and power industries are one of the most important economic sectors, essential in creating value-added economic growth in any society. The waste of energy resources and environmental issues in the energy and power plant sectors have created various challenges for the power industry. Managing existing capacities and reducing environmentally harmful effects result in system efficiency enhancement and wasted energy returns to the natural cycle in electricity supply chain divisions. This study aims to determine divisions of an electricity supply chain as resource allocation handling, providing harmful emissions abatement (e.g., carbon dioxide and greenhouse gases) and waste energy mitigation. The proposed intermediate approach measures the unified inefficiency score of each production factor and determines the level of total unified inefficiency from the average of the sum of these inefficiency scores. Indeed, the proposed intermediate approach distinguishes influential supply chain divisions from the uninfluential ones to sources utilization management for abatement of significant flare and greenhouse gases and wasted energy in energy and power plant sectors, as well as transmission and distribution lines. The current paper introduces intermediate approaches for sustainability evaluation of the electricity supply chain based on data envelopment analysis models. The proposed approach evaluates the potential of supply chain divisions to reduce undesirable outputs. The intermediate approach, under managerial disposability, enables us to determine regions of the supply chain with the necessary preparation to confront harmful emissions and wasted energy. One empirical implication has been obtained from model performance in the electricity supply chain. Findings suggested that the oil field, power plant, and transmission line divisions, as well as industrial consumers, have adequate potential for pollution emissions abatement and energy loss prevention as the increase of inputs provides more outputs. Moreover, these divisions are efficient in 10 supply chains. The implications of the results, including the fact that the increase of inputs provides desirable output increments and productivity enhancement in the oil field, power plant sections, and industrial consumers, have been considered. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aElectric power distribution. =650 \0$aElectric power production. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20210004.html =LDR 03762nab 2200553 i 4500 =001 SSMS20210013 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20210013$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20210013$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aQC174.85.M64 =082 04$a530.13$223 =100 1\$aTiwari, K.,$eauthor. =245 10$aMonte Carlo Method–Based Tool Life Prediction during the End Milling of Ti-6Al-4V Alloy for Smart Manufacturing /$cK. Tiwari, N. Arunachalam. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (26 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aTool wear prediction during machining is of significant interest for the development of intelligent functionalities in manufacturing industry. A data-driven Bayesian Monte Carlo–based probabilistic approach is used for predicting the wear of TiAlN-coated carbide inserts during the end milling of Ti-6Al-4V alloy. A series of slot milling passes at varying combinations of speed, feed, and depth of cut were conducted, and wear was measured after each pass. Each insert was used for successive passes at a particular cutting condition until the flank wear crosses the failure threshold of 0.3 mm of average flank wear. The wear estimation from the model is good at tracking wear growth for the unknown data sets, which can provide a timely tool change command before the tool failure. This model thus leads to the formulation of an adaptive control strategy for timely replacement of cutting tools for optimal machining. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aMonte Carlo method. =650 \0$aStatistical physics. =650 \0$aDigital computer simulation. =700 1\$aArunachalam, N.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20210013.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200027 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2021\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200027$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200027$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA157 =082 04$a660.2$223 =100 1\$aLou, Helen H.,$eauthor. =245 10$aProblem-Based Learning on Incorporation of Data Analysis Skills into Chemical Engineering Senior Advanced Analysis Course /$cHelen H. Lou, Yifan Chen, Ravinder Singh. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2021. =300 \\$a1 online resource (9 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThrough years of production, the chemical process industry has accumulated rich data assets. In the age of information and artificial intelligence, processing facilities are using data in new ways to improve efficiency, reliability, and safety. However, a skilled workforce is in shortage. To embrace the digital transformation, chemical engineering students need to get in-depth training for data analytics skills. In a senior Advanced Analysis class, a problem-based learning approach was utilized to incorporate data analysis skills into traditional curriculum using real-world problems and real plant data. Students diagnosed process operation data and planned maintenance for a series of heat exchangers based on condition rather than time. This approach was well received by the students, some of whom eventually became process engineers or maintenance engineers who are using this skill in their work. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aEngineering$xStudy and teaching$zUnited States. =650 \0$aChemical engineering. =650 \0$aEngineering$xStudy and teaching. =700 1\$aChen, Yifan,$eauthor. =700 1\$aSingh, Ravinder,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 2.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200027.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200003 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200003$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200003$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ159 =082 04$a621$223 =100 1\$aMenandro, Fernando César Meira,$eauthor. =245 10$aGame-Based Mechanical Engineering Teaching and Learning - A Review /$cFernando César Meira Menandro, Sylvester Arnab. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (15 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThe usual approach to engineering education, though appropriate for some students, in general lacks adaptability for different learning needs. Game-based learning is an attempt to provide adaptable learner-centered education. This paper presents a review of the published works on game-based applications for Mechanical Engineering teaching and learning from the past five decades. A comprehensive review was performed, and more than 200 papers were screened, evaluating the gamification approach, educational objectives, and Mechanical Engineering curriculum. Bloom’s taxonomy was used to identify cognitive learning outcomes for each of the games studied. There was also an attempt to establish Mechanical Engineering topics for an efficient curriculum and a correspondence of each game analyzed with the specific topic. The references found are presented according to Mechanical Engineering knowledge topic and cognitive learning outcome. Suggestions for further research on the field are made. The main ones include the need to formalize the educational objectives and development goals of the games, since most of the games studied did no such formalization, as well as the development and design strategies adopted to achieve such goals, a recently growing field of study. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aEngineering$xEducation$vCongresses. =650 \0$aEngineering$xStudy and teaching. =650 \0$aMechanical engineering. =700 1\$aArnab, Sylvester,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 2.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200003.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200008 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200008$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200008$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTS183 =082 04$a658.5/00285$223 =100 1\$aRaoufi, Kamyar,$eauthor. =245 10$aDevelopment and Implementation of a Framework for Adaptive Undergraduate Curricula in Manufacturing Engineering /$cKamyar Raoufi, Brian K. Paul, Karl R. Haapala. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (20 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aAdvanced manufacturing, through the application of science and technology, compels an expanded view of the role of engineers in driving innovation. Advanced manufacturing requires engineers to imagine new ways of making products for smart, rapid, flexible, sustainable, and competitive production. Such manufacturing innovation is driving operational, tactical, and strategic advantages for industry while also creating a demand for a dynamic global workforce and market. The work herein supports the preparation of next-generation engineers for careers in academia and industry by developing and implementing a framework for adaptive manufacturing engineering curricula. The framework is founded upon a benchmarking study that applied the Society of Manufacturing Engineers Four Pillars of Manufacturing Knowledge Model to examine Accreditation Board for Engineering and Technology, Inc.–accredited undergraduate manufacturing engineering programs in the United States. Results of this work will enable universities, along with their industry partners, to identify topics that have garnered the attention of other curriculum developers and define opportunities for improvement. Thus, the adaptive framework can serve as a basis for defining how individual undergraduate programs can best meet the human resource needs of affiliated advanced manufacturing industry. To illustrate, a resulting revision to the manufacturing engineering curriculum at Oregon State University is described. The curriculum consists of a set of foundational courses and supporting thrusts in manufacturing systems and product development. The framework enables keystone options addressing needs for educating students in manufacturing systems, product development, smart manufacturing, and sustainable manufacturing. The framework supports local industry needs while taking advantage of faculty expertise. Initial implementation has demonstrated a positive student reception of the revised program, which also facilitates dual majors with industrial and mechanical engineering. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aManufacturing processes$xData processing. =650 \0$aIndustrialists$xInformation resources management. =650 \0$aIndustrial management$xData processing. =700 1\$aPaul, Brian K.,$eauthor. =700 1\$aHaapala, Karl R.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 2.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200008.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200007 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200007$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200007$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTD794.5 =082 04$a628.4458$223 =100 1\$aBrowne, C. A.,$eauthor. =245 10$aRecycling or Rubbish? Understanding Decision-Making in Household Recycling Programs /$cC. A. Browne, B. Moloney. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (12 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aMaterial recycling is an increasingly complex global problem. Unprecedented levels of material consumption across developed economies and continuous innovation around materials and manufacturing within the economy have made the practical challenge of closing the loop in recycling increasingly difficult. At the same time, political decisions at the global scale, such as exported recyclables not meeting standards for processing, are forcing local municipalities and councils to reconsider their approach to curbside recycling systems. In recent decades, local councils in Australia have tried various methods, such as education and awareness training, for decreasing contamination of recyclable material in curbside recycling bins. These efforts have been shown to have had a limited effect. To investigate this phenomenon at a local level, we conducted a practical workshop and knowledge experiment with undergraduate engineering students. Students were given a worksheet where they were required to sort household items into curbside recycling, drop-off recycling, or landfill. In this experiment, there were two intervention groups: a picture-based intervention and slogan-based intervention. We found that students in both groups were able to more reliably sort items intended for the Recycling waste stream than the Landfill waste stream. Further, we found that waste education interventions did little to improve correct sorting of items in the Recycling waste stream but did make a difference for Landfill items. This shows that methods for improving communication about material waste streams are needed to improve end-of-life disposal efforts, even within a cohort of young adults who have a background in engineering materials. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aEnvironmental protection. =650 \0$aRecycling. =650 \0$aIndustrial real estate$xEnvironmental aspects$zUnited States. =700 1\$aMoloney, B.,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 2.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200007.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200012 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200012$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200012$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTH7140 =082 04$a621.402$223 =100 1\$aLi, Hua,$eauthor. =245 10$aAn Innovation Framework to Integrate Engineering Standards into Industrial Engineering Graduate Curriculum /$cHua Li, Kai Jin. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (8 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThe importance of standards is that they provide product manufacturers with clear material, mechanical, and dimensional requirements coupled with specific quality assurance and test methods used to ensure their quality and functionality. Manufacturing-related material standards provide a consensus regarding desired properties between both manufacturers and customers. Moreover, these standards provide analysis methods to measure the properties and leads to standard manufacturing procedures that ensure the quality. To fulfill the need of real-world systems, a curriculum innovation was planned and implemented into the industrial engineering graduate program at Texas A&M University-Kingsville with the aim to integrate manufacturing-related materials and quality management standards into graduate engineering education through innovative course modules and a certificate program. There are four major components in the framework, including (1) course module development on manufacturing-related material standards, (2) course module development on manufacturing-related quality management standards, (3) graduate-level certificate program development, and (4) industrial experience sharing through webinars. ASTM and ISO standards were introduced to graduate students with hands-on experiences on applying standards to real-world case studies. It creates a systematic framework to strengthen graduate students’ education and learning about manufacturing-related materials and quality management standards and standardization. Student surveys were used to collect their feedback, which show very positive impact on the students’ knowledge and interests in standards and standardization. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aEngineering ethics. =650 \0$aEngineering ethics$vCase studies. =650 \0$aFurnaces$xDesign and construction. =700 1\$aJin, Kai,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 2.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200012.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200009 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200009$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200009$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA183 =082 04$a670$223 =100 1\$aKovalenko, Ilya,$eauthor. =245 10$aDeveloping the Workforce for Next-Generation Smart Manufacturing Systems :$bA Multidisciplinary Research Team Approach /$cIlya Kovalenko, Efe C. Balta, Yassine Qamsane, Patricia D. Koman, Xiao Zhu, Yikai Lin, Dawn M. Tilbury, Z. Morley Mao, Kira Barton. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (21 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aAs real-world systems become more complex and connected, the industrial sector requires engineers who can solve problems across multiple disciplines and work with people across various educational backgrounds. This is particularly apparent in the manufacturing industry, as the integration of new manufacturing system technology requires knowledge in a diverse set of fields, such as physics, computer science, and engineering, to name a few. To properly educate the next workforce in manufacturing, engineering education needs to incorporate cross-disciplinary, project-driven learning that provides students with ample opportunities to work with cutting-edge manufacturing technology. At the University of Michigan, the Secure Cloud Manufacturing Multidisciplinary Design Program team focuses on developing the next generation of manufacturing engineers through research-driven multidisciplinary projects. A group of 7–22 students work on several multisemester-long projects that focus on providing hands-on, student-driven learning. Each semester, these students work closely with several faculty members, research scientists, postdocs, and graduate students to propose, develop, and conduct industry-relevant research projects on multiple manufacturing testbeds. Example projects have included the implementation of a smart quality-control camera, the development of digital twins for manufacturing processes, and the integration of secure cloud-based infrastructures for industrial controllers. In these highly collaborative and multidisciplinary project groups, students learn from each other, take on leadership roles, and disseminate their work through technical reports and presentations to academic and industry experts. Students leave the group with an understanding of the capabilities and needs of future manufacturing systems, ready to become, and lead, the next set of manufacturing engineers. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aManufacturing industries$xAutomation. =650 \0$aManufacturing processes$xTechnological innovations. =650 \0$aStructural health monitoring. =700 1\$aBalta, Efe C.,$eauthor. =700 1\$aQamsane, Yassine,$eauthor. =700 1\$aKoman, Patricia D.,$eauthor. =700 1\$aZhu, Xiao,$eauthor. =700 1\$aLin, Yikai,$eauthor. =700 1\$aTilbury, Dawn M.,$eauthor. =700 1\$aMorley Mao, Z.,$eauthor. =700 1\$aBarton, Kira,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 2.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200009.html =LDR 03762nab 2200553 i 4500 =001 SSMS20200011 =003 IN-ChSCO =005 20220119061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 220419s2020\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200011$2doi =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =037 \\$aSSMS20200011$bASTM International =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aLB1032 =082 04$a302.3$223 =100 1\$aAmini-Rankouhi, Aida,$eauthor. =245 10$aTeam–based Learning of Sustainability :$bIncorporation of Sustainability Concept and Assessment into Chemical Engineering Senior Design Course /$cAida Amini-Rankouhi, Yinlun Huang. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2020. =300 \\$a1 online resource (11 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aDesign for Sustainability (DfS) becomes as an increasing important component in engineering education. One of the most effective educational strategies for sustainability integration is through undergraduate senior capstone courses. Chemical engineering as a main engineering discipline aims to prepare next-generation engineers with adequate knowledge and skills for pursing sustainable engineering in the near future. In this paper, we introduce our approach for introducing sustainability concepts and sustainability assessment methods into our undergraduate capstone design course. In the course, we guided students to conduct a sophisticated team project: Process Design, Modification, and Sustainability Assessment for Distributed Biodiesel Manufacturing. Students in teams developed and evaluated all design options from the sustainability point of view using different sustainability metrics systems and discussed for identification of most desirable solutions. Educational experience with the team-based approach was summarized through classroom evaluation. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 19, 2022. =650 \0$aTeam learning approach in education. =650 \0$aClassroom management. =650 \0$aProduction management$vCongresses. =700 1\$aHuang, Yinlun,$eauthor. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 5, Issue 2.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200011.html =LDR 03617nab a2200445 i 4500 =001 SSMS20200071 =003 IN-ChSCO =005 20230127161000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 230127s2023\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20200071$2doi =037 \\$aSSMS20200071$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ1185 =082 04$a621.902$223 =100 1\$aSarath, S.,$eauthor. =245 10$aStudy on the Effect of Polyurethane-Based Magnetorheological Foam Damper on Cutting Performance during Hard Turning Process /$cS. Sarath, P. Sam Paul, D. S. Shylu, G. Lawrance. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2023. =300 \\$a1 online resource (16 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aIn the metal cutting process, a magnetorheological fluid damper was used to suppress tool vibration and to enhance cutting performance. However, as the machining progresses, the performance of magnetorheological fluid was affected by sedimentation and agglomeration factors, which resulted in a minimum reduction in the magnitude of vibration. For achieving vibration control during the machining process, it is required to use an appropriate damping technique that increases stability and enhances performance indexes. In this paper, a magnetorheological foam damper was developed where polyurethane foam was used to suppress tool vibration during the machining process. Iron particles along with carrier fluid were filled inside the pores of the foam and when the electric current is applied to the foam, particle chains form and it changes its stiffness, which behaves as a viscoelastic material with nonlinear vibration features. Cutting experiments were conducted, and based on the results, it is observed that the vibration during machining was suppressed and cutting performance was enhanced as a result of using this magnetorheological foam. Also, the stability of the magnetorheological material was improved without any settlement of iron particles. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 25, 2023. =650 \0$aMetal-cutting. =650 \0$aElectric metal-cutting. =650 \0$aMachine-tools. =700 1\$aPaul, P. Sam,$eauthor. =700 1\$aShylu, D. S.,$eauthor. =700 1\$aLawrance, G.,$eauthor. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 6, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20200071.html =LDR 03617nab a2200445 i 4500 =001 SSMS20210012 =003 IN-ChSCO =005 20230127161000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 230127s2023\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20210012$2doi =037 \\$aSSMS20210012$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA1505 =082 04$a670.425$223 =100 1\$aVivek, S.,$eauthor. =245 10$aAn Improved Quality Inspection of Engine Bearings Using Machine Vision Systems /$cS. Vivek, K. Srinivasan, B. Sharmila, Y. Dharshan, Hitesh Panchal, M. Suresh, R. Ashokkumar, Kishor Kumar Sadasivuni, Medhat Elkelawy, Neel Shrimali. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2023. =300 \\$a1 online resource (14 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThe growth of an industry is mostly based on the quality of the product. When there is a fault in the manufactured product in a batch, the entire batch is rejected because of a single faulty product. Quality has become one of the important factors deciding the growth of the industry. Industries nowadays are being automated in every phase of the manufacturing process. Here, an automated system is developed for the inspection of the product developed for the automobile industry. The bearing is one of the parts that play a major role in the connections with the engine and the shafts. Therefore, the bearing has to be perfect, as faulty bearings will lead to greater damage regardless of others. In the automated system, the bearing is inspected for the missing operations in the product. The faulty product is rejected and would be checked for rework or to be scraped. The inspected products are then being sent for the packing process. Through the automated inspection system, the production rate of the product can be increased, also increasing the reputation of the industry. The objective of the present work is to develop a machine vision system for quality inspection of bearing efficiency. The LabVIEW-based approach is carried out for the implementation in current research work. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 25, 2023. =650 \0$aComputer vision. =650 \0$aComputer vision$xIndustrial applications. =650 \0$aEngineering inspection$xAutomation. =700 1\$aSrinivasan, K.,$eauthor. =700 1\$aSharmila, B.,$eauthor. =700 1\$aDharshan, Y.,$eauthor. =700 1\$aPanchal, Hitesh,$eauthor. =700 1\$aSuresh, M.,$eauthor. =700 1\$aAshokkumar, R.,$eauthor. =700 1\$aSadasivuni, Kishor Kumar,$eauthor. =700 1\$aElkelawy, Medhat,$eauthor. =700 1\$aShrimali, Neel,$eauthor. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 6, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20210012.html =LDR 03617nab a2200445 i 4500 =001 SSMS20210018 =003 IN-ChSCO =005 20230127161000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 230127s2023\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20210018$2doi =037 \\$aSSMS20210018$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTC173.H348 =082 04$a620$223 =100 1\$aPanchal, Ketan D.,$eauthor. =245 10$aDepth of Penetration Model in AWJ Cutting Process Considering the Effect of Frictional Drag of Kerf Wall on the Water-Jet Velocity /$cKetan D. Panchal, Abdul Hafiz Shaikh. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2023. =300 \\$a1 online resource (10 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aAnalytical modeling and experimental verification for the process are vital for selecting an optimum range of process parameters in abrasive water-jet (AWJ) cutting. Many numerical models for the prediction of depth of penetration in the AWJ machining process have been presented by various researchers in the past years. However, very little work that has been represented has taken into account the effect of kerf wall frictional drag on the AWJ velocity. The present paper aims to develop a modified mathematical model for penetration depth using a dimensional analysis approach. The kerf wall frictional drag coefficient has been included in the proposed model to explain the variation of particle velocity at a particular depth of penetration. The results predicted with the theoretical model are validated under various process parameters through the AWJ machining experiments. The predicted values of depth of penetration using the proposed model agree well with the experimental data. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 25, 2023. =650 \0$aWater jet cutting. =650 \0$aJet cutting. =650 \0$aWater jet cutting$xSafety measures. =700 1\$aHafiz Shaikh, Abdul,$eauthor. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 6, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20210018.html =LDR 03617nab a2200445 i 4500 =001 SSMS20210019 =003 IN-ChSCO =005 20230127161000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 230127s2023\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20210019$2doi =037 \\$aSSMS20210019$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTA710 =082 04$a620.19$223 =100 1\$aPrasad, S. J. Suji,$eauthor. =245 10$aDesign and Development of Novel Computer Vision-Based Automatic Calibration System for Analog Dial Pressure Gauge /$cS. J. Suji Prasad, J. Indra, M. Thangatamilan, Radhey Shyam Meena, Suresh Muthusamy, Hitesh Panchal, Mahendran Krishnamoorthy, Kishor Kumar Sadasivuni, Manish Doshi. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2023. =300 \\$a1 online resource (17 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aPressure gauge calibration refers to comparing the standard and test gauge to determine the error percentage. The reading accuracy of the analog dial pressure gauge is found in the conventional method through manual calculations and then tabulating the values in the calibration certificate. The present study is undertaken to increase the accuracy level of the analog dial pressure gauges. A new automated computer-based method is proposed to calibrate the meters more accurately than the conventional method. The indicated value of the pressure gauge is obtained through the angular position of the pointer and the error value is identified by comparing both standard and test gauge values. In this work, Red, Green, Blue (RGB) images of standard and test pressure gauges are acquired through a high-definition camera. After the image processing operations, the pointer orientation of the indicatoris identified, and the indicated values are calculated. The computer-vision-based automatic calibration system is applied in a 0–100-psi (0–689.476 kPa) analog dial pressure gauge, and three trials were performed to determine the accuracy. The computer vision technique improved accuracy, varying from 97 % to 98 % compared with the conventional manual observation method. The observed results have improved repeatability and accuracy with the proposed computer-vision-based system. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 25, 2023. =650 \0$aComputer vision. =650 \0$aComputer vision$xIndustrial applications. =650 \0$aEmbedded computer systems. =700 1\$aIndra, J.,$eauthor. =700 1\$aThangatamilan, M.,$eauthor. =700 1\$aMeena, Radhey Shyam,$eauthor. =700 1\$aMuthusamy, Suresh,$eauthor. =700 1\$aPanchal, Hitesh,$eauthor. =700 1\$aKrishnamoorthy, Mahendran,$eauthor. =700 1\$aSadasivuni, Kishor Kumar,$eauthor. =700 1\$aDoshi, Manish,$eauthor. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 6, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20210019.html =LDR 03617nab a2200445 i 4500 =001 SSMS20210020 =003 IN-ChSCO =005 20230127161000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 230127s2023\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20210020$2doi =037 \\$aSSMS20210020$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTX838 =082 04$a664.9$223 =100 1\$aBapat, Salil,$eauthor. =245 10$aCellular Agriculture: An Outlook on Smart and Resilient Food Agriculture Manufacturing /$cSalil Bapat, Vishvesh Koranne, Neha Shakelly, Aihua Huang, Michael P. Sealy, John W. Sutherland, Kamlakar P. Rajurkar, Ajay P. Malshe. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2023. =300 \\$a1 online resource (11 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aOver the centuries, the application of grassland and cutting of livestock have been the primary foundations for the production of food agriculture manufacturing. Growing human population, accelerated human activities globally, staggering food inequity, changing climate, precise nutrition for extended life expectancy, and more demand for protein food call for a new outlook to smartness in food agriculture manufacturing for delivering nutritious food. Cellular agriculture, 3-D printing of food, vertical urban farming, and digital agriculture, alongside traditional means, are envisioned to transform food agriculture and manufacturing systems for acceptability, availability, accessibility, affordability, and resiliency for meeting demands of food in this century for communities across the United States and the world. This technical note illustrates the thought leadership for cellular agriculture as a part of the new food agriculture manufacturing revolution. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 25, 2023. =650 \0$aArtificial foods. =650 \0$aAgriculture. =650 \0$aFood. =700 1\$aKoranne, Vishvesh,$eauthor. =700 1\$aShakelly, Neha,$eauthor. =700 1\$aHuang, Aihua,$eauthor. =700 1\$aSealy, Michael P.,$eauthor. =700 1\$aSutherland, John W.,$eauthor. =700 1\$aRajurkar, Kamlakar P.,$eauthor. =700 1\$aMalshe, Ajay P.,$eauthor. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 6, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20210020.html =LDR 03617nab a2200445 i 4500 =001 SSMS20210022 =003 IN-ChSCO =005 20230127161000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 230127s2023\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20210022$2doi =037 \\$aSSMS20210022$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTS183 =082 04$a670$223 =100 1\$aBapat, Salil,$eauthor. =245 10$aApplications of Hybrid Manufacturing during COVID-19 Pandemic: Pathway to Convergent Manufacturing /$cSalil Bapat, Michael P. Sealy, Kamlakar P. Rajurkar, Tom Houle, Kimberly Sablon, Ajay P. Malshe. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2023. =300 \\$a1 online resource (11 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThis paper presents the advancements in manufacturing science and the engineering learned because of the global emergencies resulting from pandemics. Established manufacturing processes strained to the limit delivering parts and services during the pandemic in industrialized as well as industrializing nations. These limitations call for manufacturing by integrating or hybridizing multiple processes and sometimes materials. This paper illustrates value propositions resulting from hybrid manufacturing by using pertinent case studies of a ventilator filter housing and an injection molding tool. This paper concludes by making a case for convergence of heterogenous materials, processes, and systems in a unified platform allowing adaptability, agility, and flexibility in manufacturing geared toward offering resilience in similar future global catastrophes. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 25, 2023. =650 \0$aManufacturing processes. =650 \0$aComputer engineering. =650 \0$aEngineering design. =700 1\$aSealy, Michael P.,$eauthor. =700 1\$aRajurkar, Kamlakar P.,$eauthor. =700 1\$aHoule, Tom,$eauthor. =700 1\$aSablon, Kimberly,$eauthor. =700 1\$aMalshe, Ajay P.,$eauthor. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 6, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20210022.html =LDR 03617nab a2200445 i 4500 =001 SSMS20210025 =003 IN-ChSCO =005 20230127161000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 230127s2023\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20210025$2doi =037 \\$aSSMS20210025$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTS155.7 =082 04$a628$223 =100 1\$aCrosby, Brett,$eauthor. =245 10$aIntegrating Lean and Sustainable Manufacturing Principles for Sustainable Total Productive Maintenance (Sus-TPM) /$cBrett Crosby, Fazleena Badurdeen. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2023. =300 \\$a1 online resource (17 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aTotal productive maintenance (TPM) is a systematic method to ensure equipment can function at the required performance to meet customer demand. The benefits of TPM are well known in lean manufacturing environments, and the technique is widely used to ensure no waste is created by defective equipment. Lean manufacturing principles and practices provide a solid basis to build upon to create more sustainable products, processes, and systems. TPM practices, therefore, can serve as a useful tool for more sustainable maintenance practices, leading to more sustainable manufacturing when additional criteria are incorporated into the existing foundation of the practice. This paper introduces a framework to integrate sustainability criteria and develop Sustainable TPM (Sus-TPM) as a complement to TPM practices. The approach to visually present the temporal sustainability performance variation of equipment by developing impact assessment trees using the Sus-TPM technique is presented. The application of the approach to an example is provided to demonstrate its benefits. Potential future expansions are also discussed to highlight how the Sus-TPM methodology could become an important tool for more sustainable manufacturing. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 25, 2023. =650 \0$aSustainable engineering. =650 \0$aLean manufacturing. =650 \0$aManufacturing processes$xEnvironmental aspects. =700 1\$aBadurdeen, Fazleena,$eauthor. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 6, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20210025.html =LDR 03617nab a2200445 i 4500 =001 SSMS20210036 =003 IN-ChSCO =005 20230127161000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 230127s2023\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20210036$2doi =037 \\$aSSMS20210036$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ211.49 =082 04$a629.892$223 =100 1\$aNguyen, Vinh,$eauthor. =245 10$aEvaluation of Robot Degradation on Human-Robot Collaborative Performance in Manufacturing /$cVinh Nguyen, Jeremy Marvel. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2023. =300 \\$a1 online resource (14 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aHuman-robot collaborative systems are highly sought candidates for smart manufacturing applications because of their adaptability and consistency in production tasks. However, manufacturers are still hesitant to adopt these systems because of the lack of metrics regarding the influence of the degradation of collaborative industrial robots on human-robot teaming performance. Hence, this paper defines teaming performance metrics with respect to robot degradation. In addition, the defined metrics are applied to a human-robot collaborative inverse peg-in-hole case study with respect to the degradation of the joint angular encoder and current sensor. Specifically, this case study compares pure insertion versus insertion with spatial scanning to solve the peg-in-hole problem, and manual intervention is implemented in the event of robotic failure. The metrics used in the case study showed that pure insertion more sensitive to robot degradation with manual intervention was required at 0.04° as opposed to 0.12° from insertion with scanning. Therefore, insertion with scanning was shown to be more robust to robot degradation at the cost of a slower insertion time of 9.48 s compared to 3.19 s. Thus, this paper provides knowledge and usable metrics regarding the influence of robot degradation on human-robot collaborative systems in manufacturing applications. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 25, 2023. =650 \0$aArtificial intelligence. =650 \0$aAutomation. =650 \0$aEngineering. =700 1\$aMarvel, Jeremy,$eauthor. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 6, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20210036.html =LDR 03617nab a2200445 i 4500 =001 SSMS20210038 =003 IN-ChSCO =005 20230127161000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 230127s2023\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20210038$2doi =037 \\$aSSMS20210038$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTL798.N3 =082 04$a623.893$223 =100 1\$aPriegnitz, Nathan,$eauthor. =245 10$aIdentification of Significant Stop Locations in a Mine through GPS Clustering /$cNathan Priegnitz, John Yoo. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2023. =300 \\$a1 online resource (11 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aSurface mining applications are highly dynamic environments; conditions one day may not resemble those the next. Not only are these environments subject to changes in weather and other factors, but the locations, personnel, and equipment operations continuously move. Conditions will also vary depending on where equipment and personnel are in the operation. Permanent locations, such as shop and crusher areas, and dynamic locations, such as material loading and dumping areas, will have different lighting, traffic, floor conditions, etc.; personnel need to be aware of these differing conditions to adjust behavior. While modern large-scale mining equipment is generally outfitted with global positioning system (GPS) devices to track machines and support the optimization of operations in the mine, GPS location tracking has also become nearly ubiquitous with the increasing distribution of smart devices. The continued development and implementation of improved data processing and transmission technology increase the potential of equipment to provide more meaningful information to personnel. This paper proposes the hybrid spatial clustering for identification of locations of significance (HSCILS) algorithm—a hybrid of multiple clustering techniques—to analyze GPS data to identify and differentiate different types of locations in the mining operation. The HSCILS algorithm will then be contrasted with the k-means clustering algorithm and the Density-Based Spatial Clustering of Applications with Noise algorithm to demonstrate its capability as an appropriate tool to identify key locations in a mine and differentiate between their nature. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 25, 2023. =650 \0$aGlobal Positioning System. =650 \0$aEngineering. =650 \0$aArtificial satellites in navigation. =700 1\$aYoo, John,$eauthor. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 6, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20210038.html =LDR 03617nab a2200445 i 4500 =001 SSMS20210041 =003 IN-ChSCO =005 20230127161000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 230127s2023\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20210041$2doi =037 \\$aSSMS20210041$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ1191 =082 04$a671.35$223 =100 1\$aGalati, Manuela,$eauthor. =245 10$aPerformance Analysis of Electro-chemical Machining of Ti-48Al-2Nb-2Cr Produced by Electron Beam Melting /$cManuela Galati, Silvio Defanti, Lucia Denti. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2023. =300 \\$a1 online resource (15 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aTi-48Al-2Nb-2Cr is a challenging and difficult-to-cut titanium aluminide (TiAl) alloy with several manufacturing issues because of the high sensitivity to crack formation and oxygen picking up. Electron beam powder bed fusion (EB-PBF) made feasible TiAl near net shape components, but the surfaces are particularly rough and present complex surface topographies. In this present investigation, experimental analysis and optimization are proposed for electro-chemical machining (ECM) on as-built Ti-48Al-2Nb-2Cr surfaces manufactured using EB-PBF. Experimental runs are performed under pulsed machining conditions and varying specific process metrics to understand the machining effects on the process efficiency and removal phenomena. In particular, the morphology and isotropy of the surface are studied before and after the machining by scanning electron and confocal microscopies. The results establish the optimal machining conditions and a range for the active machining time that produce, compared to the as-built surface, an extremely smooth and isotropy surface without any detrimental effect on the surface integrity and microstructure. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 25, 2023. =650 \0$aElectrochemical cutting. =650 \0$aMetal-working machinery. =650 \0$aMachining. =700 1\$aDefanti, Silvio,$eauthor. =700 1\$aDenti, Lucia,$eauthor. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 6, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20210041.html =LDR 03617nab a2200445 i 4500 =001 SSMS20210042 =003 IN-ChSCO =005 20230127161000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 230127s2023\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20210042$2doi =037 \\$aSSMS20210042$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTK5105.59 =082 04$a005.8$223 =100 1\$aSreeramagiri, Praveen,$eauthor. =245 10$aAnalyzing Security Risks in Cyber-Physical Manufacturing Systems with Actor–Network Theory /$cPraveen Sreeramagiri, Gillian Andrews, Amanda K. Greene, Ganesh Balasubramanian. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2023. =300 \\$a1 online resource (12 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aThis article suggests that actor–network theory (ANT) can reveal unique challenges and consequences of cyberattacks in manufacturing. As an approach, ANT rejects the dualism that often separates humans and nonhumans, recognizing the active role of both in affecting events. Our approach adds an important new perspective to an existing body of research that focuses on analyzing vulnerabilities in cyberspace instead of their ramifications in the material world. Drawing on the case study of a faulty airbag inflator in an automobile, we use concepts and vocabularies drawn from ANT to discuss the consequences of attacks in manufacturing, such as viewing altered products as actants with agency to alter subsequent networks (e.g., when a manufactured part is integrated into an automotive vehicle). By tracing the movement of specific materials and products through networks it is possible to elucidate how cyberattacks not only impact cyber-physical systems themselves, but also reverberate into a multitude of broader impacts, potentially endangering physical safety, shaping public opinion, and influencing economic markets. Our examination of one particular context draws on existing work that has brought ANT and cybersecurity in dialogue, but we extend this work by focusing on the role of “translation” and “depunctualization” across the lifecycle of a cyberattack in manufacturing. This analysis stresses the need for sector-specific examinations of cyberthreats, while also demonstrating the value of interdisciplinary methods like ANT that do not reify artificial dualisms in addressing for conceptualizing security risks in cyber-physical manufacturing systems. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 25, 2023. =650 \0$aComputer security. =650 \0$aComputer networks$xSecurity measures. =650 \0$aWeb site development$xComputer programs. =700 1\$aAndrews, Gillian,$eauthor. =700 1\$aGreene, Amanda K.,$eauthor. =700 1\$aBalasubramanian, Ganesh,$eauthor. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 6, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20210042.html =LDR 03617nab a2200445 i 4500 =001 SSMS20220010 =003 IN-ChSCO =005 20230127161000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 230127s2023\\\\pau|||||o|||||||||||eng|| =024 7\$a10.1520/SSMS20220010$2doi =037 \\$aSSMS20220010$bASTM =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aTJ211.35 =082 04$a629.8932$223 =100 1\$aSun, Jumei,$eauthor. =245 10$aMovement Trajectory Control of an Intelligent Mobile Robot Controlled by Machine Vision /$cJumei Sun, Qin Chu, Shukai Liu. =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c2023. =300 \\$a1 online resource (9 pages) :$billustrations, figures, tables =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =504 \\$aIncludes bibliographical references. =520 3\$aIntelligent mobile robots can take up various repetitive labor tasks instead of humans, but with the increase in labor requirements, the requirements for the autonomous working ability of intelligent mobile robots have also increased. This research used machine vision to identify obstacles in the working environment; then, a plane model was constructed for the working environment of the intelligent mobile robot with the help of machine vision. The moving path was planned by the ant colony algorithm. The robot adopted the trajectory tracking control law to track the planned path to realize the autonomous movement of the intelligent mobile robot. Finally, experiments were carried out, and the trajectory control of the robot moving on the path planned by the ant colony algorithm was compared with that planned by the genetic algorithm. The results showed that the ant colony algorithm converged faster and planned a shorter path after stability; when the coordinates of the starting and end points of the planned path were (0.0 m, 2.5 m) and (5.0 m, 0.0 m), respectively, the turning point coordinates of the paths planned by the genetic and ant colony algorithms were (2.5 m, 2.0 m) and (3.5 m, 0.0 m), respectively; the robot had shorter movement trajectory, smaller deviation, and less movement time on the path planned by the ant colony algorithm than the genetic algorithm. =541 \\$aASTM International$3PDF$cPurchase price$hFree. =588 \\$aDescription based on publisher's website, viewed January 25, 2023. =650 \0$aMobile robots. =650 \0$aAutomatic control. =650 \0$aArtificial intelligence. =700 1\$aChu, Qin,$eauthor. =700 1\$aLiu, Shukai,$eauthor. =710 2\$aAmerican Society for Testing and Materials. =710 2\$aASTM International. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =773 0\$tSmart and Sustainable Manufacturing Systems.$gVolume 6, Issue 1.$dWest Conshohocken, Pa. :$bASTM International, 2021$x2572-3928$ySSMSCY =856 40$uhttps://www.astm.org/ssms20220010.html =LDR 03693nas a2200865 i 4500 =001 SSMS20230101 =003 IN-ChSCO =005 20230127061000.0 =006 m|||||o||||||||||| =007 cr\|n||||||||n =008 230127c19739999pau|||||o|||||||||||eng|| =022 \\$a1945-7553 =022 \\$z0090-3973 =030 \\$aSSMSVAB =037 \\$bASTM International, 100 Barr Harbor Dr., West Conshohocken, PA 19428 =040 \\$aASTM$cSCOPE$beng$erda =041 \\$aeng =050 \4$aHD38.5 =082 04$a670$223 =130 0\$aSmart and Sustainable Manufacturing Systems (Online) =210 0\$aJ. test. eval. =245 10$aSmart and Sustainable Manufacturing Systems. =246 13$aSSMS =246 30$aTesting and evaluation =246 13$aASTM Smart and Sustainable Manufacturing Systems =264 \1$aWest Conshohocken, Pa. :$bASTM International,$c1973- =310 \\$aBimonthly =336 \\$atext$2rdacontent =337 \\$acomputer$2rdamedia =338 \\$aonline resource$2rdacarrier =347 \\$atext file$bPDF$2rda =362 0\$aVol. 1, Issue 1 (January 1973)- =500 \\$a"A multidisciplinary forum for applied sciences and engineering." =510 1\$aApplied science & technology index,$x0003-6986 =510 1\$aComputer & control abstracts,$bJan. 1973-,$x0036-8113 =510 2\$aAbstract Bulletin of Paper Science and Technology =510 2\$aAluminum Industry Abstracts =510 2\$aApplied Mechanics Reviews =510 2\$aApplied Science & Technology Index,$bNov.1983- =510 2\$aCAB Abstracts =510 2\$aC S A Civil Engineering Abstracts (Cambridge Scientific Abstracts) =510 2\$aC S A Engineered Materials Abstracts =510 2\$aC S A Mechanical & Transportation Engineering Abstracts (Cambridge Scientific abstracts) =510 2\$aCeramic Abstracts =510 2\$aChemical abstracts,$x0009-2258 =510 2\$aCOMPENDEX =510 2\$aComputer and Information Systems Abstracts Journal =510 2\$aCorrosion Abstracts =510 2\$aCurrent Contents/Engineering Computing & Technology =510 2\$aEarthquake Engineering Abstracts Database,$b2004- =510 2\$aElectronics and Communications Abstracts Journal =510 2\$aEngineering Index Monthly =510 2\$aINSPEC =510 2\$aISI Science Citation Index =510 2\$aMaterials Science Citation Index =510 2\$aMechanical Engineering Abstracts =510 2\$aMetals Abstracts/Alloys Index (METADEX) =510 2\$aPackaging Month =510 2\$aPersonal Alert =510 2\$aR A P R A Abstracts (Rubber and Plastics Research Association of Great Britain),$b1927- =510 2\$aR I L M Abstracts of Music Literature (Repertoire International de Litterature Musicale),$b1967- =510 2\$aScience Citation Index =510 2\$aSolid State and Superconductivity Abstracts =510 2\$aWorld Surface Coating Abstracts =588 \\$aDescription based on: Volume 1, Issue 1 (Jan. 1973); title from table of contents page (publisher's website, viewed Feb. 06, 2012). =588 \\$aLatest issue consulted: 2022 Volume 6, Issue 1 (viewed January 27, 2023). =650 \0$aTechnology (General) =650 \0$aEngineering (General). Civil engineering (General) =710 2\$aAmerican Society for Testing and Materials. =710 2\$aAmerican Society for Testing and Materials.$tSmart and Sustainable Manufacturing Systems. =710 2\$aASTM International. =776 18$iPrint version: $tSmart and Sustainable Manufacturing Systems.$dWest Conshohocken, Pa. : ASTM International, 1973-$x1945-7553 =856 40$uhttps://www.astm.org/journals/volume/listing/coden/SSMSCY/issue/1/volume/6/online-issue-date/2022-07-27+00%3A00%3A00/