Heye Xiao, Ruobing Wang, Xuefeng Li, Qi Zhang, Xudong Zhang, J. Bai
{"title":"Methodology for Design Process of Internal Supported Cylindrical Thin Shell Made by Additive Manufacturing","authors":"Heye Xiao, Ruobing Wang, Xuefeng Li, Qi Zhang, Xudong Zhang, J. Bai","doi":"10.1520/ssms20200074","DOIUrl":"https://doi.org/10.1520/ssms20200074","url":null,"abstract":"","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89215218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Gil, Germán D. Zapata-Madrigal, Gloria-Lucía Giraldo-Gómez
{"title":"An Ontological Model to Integrate and Assist Virtualization of Automation Systems for Industry 4.0","authors":"S. Gil, Germán D. Zapata-Madrigal, Gloria-Lucía Giraldo-Gómez","doi":"10.1520/ssms20210010","DOIUrl":"https://doi.org/10.1520/ssms20210010","url":null,"abstract":"","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"49 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73869079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Effects of Extrinsic Noise Factors on Machine Learning–Based Chatter Detection in Machining","authors":"Lance Lu, T. Kurfess, C. Saldana","doi":"10.1520/ssms20210007","DOIUrl":"https://doi.org/10.1520/ssms20210007","url":null,"abstract":"","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"21 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86028633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
David Romero, Thorsten Wuest, Makenzie Keepers, L. Cavuoto, F. Megahed
This 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.
{"title":"Smart Wearable and Collaborative Technologies for the Operator 4.0 in the Present and Post-COVID Digital Manufacturing Worlds","authors":"David Romero, Thorsten Wuest, Makenzie Keepers, L. Cavuoto, F. Megahed","doi":"10.1520/ssms20200084","DOIUrl":"https://doi.org/10.1520/ssms20200084","url":null,"abstract":"This 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.","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"19 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90915466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Smart 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, op-eration, 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 inter-actions, 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.
{"title":"Editorial: Special Issue on Education and Curriculum for Smart and Sustainable Manufacturing","authors":"J. L. Rickli, Yinlun Huang","doi":"10.1520/SSMS20210999","DOIUrl":"https://doi.org/10.1520/SSMS20210999","url":null,"abstract":"Smart 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, op-eration, 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 inter-actions, 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.","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"16 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85440116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The 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 lifecycles 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 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 allow 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 combines 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.
{"title":"Foundations of information governance for smart manufacturing.","authors":"K. C. Morris, Yan Lu, S. Frechette","doi":"10.1520/ssms20190041","DOIUrl":"https://doi.org/10.1520/ssms20190041","url":null,"abstract":"The 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 lifecycles 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 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 allow 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 combines 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.","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"134 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2020-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75078010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zimo Wang, Pawan Dixit, Faissal Chegdani, Behrouz Takabi, Bruce L. Tai, M. Mansori, S. Bukkapatnam
Natural 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. 1 Department of Industrial and Systems Engineering, Texas A&M University, 3131 TAMU, College Station, TX 77843, USA (Corresponding author), e-mail: zimo.zmw@gmail.com, https:// orcid.org/0000-0001-9667-0313 2 Department of Industrial and Systems Engineering, Texas A&M University, 3131 TAMU, College Station, TX 77843, USA 3 Capital One Financial Corp, Richmond, VA, USA 4 Arts et Metiers Institute of Technology, MSMP, HESAM Université, Châlons-enChampagne, F-51006, France 5 Texas A&M University, Department of Mechanical Engineering, 3123 TAMU, College Station, TX 77843, USA 6 Texas Engineering Experiment Station, Institute for Manufacturing Systems, College Station, TX 77843, USA
天然纤维增强聚合物(NFRP)复合材料在工业上越来越被认为是创造环保产品的替代品。纤维的复杂结构及其在基体中的随机分布影响了NFRP复合材料的可加工性和产品质量。本文研究了一种智能过程监测方法，该方法利用声发射(AE) -来自各种塑性变形和断裂机制的弹性波-来表征NFRP加工过程中的变化。最先进的分析工具无法处理AE中具有长期相关性和突发的瞬态动态模式以及工艺条件和潜在材料去除机制如何影响这些模式。为了解决这一问题，我们研究了两种双向门控循环深度学习神经网络(BD-GRNN)模型，即双向长短期记忆和双向门控循环单元，以预测基于动态声发射模式的过程条件。在六种不同切割速度和三种纤维取向的正交切割实验中，对NFRP样品的声发射信号进行了测试。实验研究结果表明，基于提取的声发射信号的时间谱特征，BD-GRNNs可以正确预测切割条件(准确率约为87%)。1德州农工大学工业与系统工程系，3131 TAMU, College Station, TX 77843, USA(通讯作者)，e-mail:zimo.zmw@gmail.com, https:// orcid.org/0000-0001-9667-0313 2德州农工大学工业与系统工程系，3131 TAMU，大学城，TX 77843，美国3 Capital One Financial Corp，弗吉尼亚州里士满，美国4 Arts et Metiers理工学院，MSMP, HESAM大学，ch lons- enchampagne, F-51006，法国5德州农工大学，机械工程系，3123 TAMU，大学城，TX 77843，美国6德克萨斯工程实验站，制造系统研究所，大学城，TX 77843，美国
{"title":"Bidirectional Gated Recurrent Deep Learning Neural Networks for Smart Acoustic Emission Sensing of Natural Fiber–Reinforced Polymer Composite Machining Process","authors":"Zimo Wang, Pawan Dixit, Faissal Chegdani, Behrouz Takabi, Bruce L. Tai, M. Mansori, S. Bukkapatnam","doi":"10.1520/ssms20190042","DOIUrl":"https://doi.org/10.1520/ssms20190042","url":null,"abstract":"Natural 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. 1 Department of Industrial and Systems Engineering, Texas A&M University, 3131 TAMU, College Station, TX 77843, USA (Corresponding author), e-mail: zimo.zmw@gmail.com, https:// orcid.org/0000-0001-9667-0313 2 Department of Industrial and Systems Engineering, Texas A&M University, 3131 TAMU, College Station, TX 77843, USA 3 Capital One Financial Corp, Richmond, VA, USA 4 Arts et Metiers Institute of Technology, MSMP, HESAM Université, Châlons-enChampagne, F-51006, France 5 Texas A&M University, Department of Mechanical Engineering, 3123 TAMU, College Station, TX 77843, USA 6 Texas Engineering Experiment Station, Institute for Manufacturing Systems, College Station, TX 77843, USA","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"77 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2020-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88120749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Raman, Karl R. Haapala, Kamyar Raoufi, B. Linke, W. Bernstein, Katherine C. Morris
Over the past century, research has focused on continuously improving the performance of manufacturing processes and systems-often 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.
{"title":"Defining Near-Term to Long-Term Research Opportunities to Advance Metrics, Models, and Methods for Smart and Sustainable Manufacturing.","authors":"A. Raman, Karl R. Haapala, Kamyar Raoufi, B. Linke, W. Bernstein, Katherine C. Morris","doi":"10.1520/ssms20190047","DOIUrl":"https://doi.org/10.1520/ssms20190047","url":null,"abstract":"Over the past century, research has focused on continuously improving the performance of manufacturing processes and systems-often 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.","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"104 2 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2020-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88549531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Arvind Shankar Raman, Karl R Haapala, Kamyar Raoufi, Barbara S Linke, William Z Bernstein, K C Morris
Over the past century, research has focused on continuously improving the performance of manufacturing processes and systems-often 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.
{"title":"Defining Near-Term to Long-Term Research Opportunities to Advance Metrics, Models, and Methods for Smart and Sustainable Manufacturing.","authors":"Arvind Shankar Raman, Karl R Haapala, Kamyar Raoufi, Barbara S Linke, William Z Bernstein, K C Morris","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Over the past century, research has focused on continuously improving the performance of manufacturing processes and systems-often 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.</p>","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"4 2","pages":""},"PeriodicalIF":1.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7542542/pdf/nihms-1613441.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38573733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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