The unique and unprecedented challenges of the COVID-19 pandemic have resulted in significant disruptions to diverse manufacturing supply chains across the globe. The negative economic impacts of these unexpected and rapid changes in demand and available supplies have been severe, and the economic sustainability of many businesses has been revealed as being highly sensitive to such changes. COVID-19 will inevitably change manufacturing, and potentially in a way that is not sustainable unless we factor sustainability into our "redesign." Otherwise, the industry will remain overwhelmed in a reactionary cycle when the next major problem emerges, such as a lack of resources during a natural or man-made disaster. In this article, we present strategies for addressing three sustainability challenges relevant to manufacturing introduced by the COVID-19 pandemic: 1) an increase in waste generation, 2) uncertainty in life cycle impacts, and 3) navigating new modes of operation for manufacturing. To mitigate the sustainability challenges of COVID-19 and create a more resilient industrial sector, we need to assess the potential of each risk to product development and production processes. We envision a systematic integration of sustainable manufacturing principles and metrics into the business practices of manufacturing enterprises, including the products they produce and the processes used to create them. Realizing this vision will require greater availability and transparency of key data related to environmental and social sustainability factors, to create a clean and sustainable future in which pandemic and disaster readiness is realized through sustainable manufacturing.
{"title":"Realizing Environmentally-Conscious Manufacturing in the Post-COVID-19 Era.","authors":"Nancy Diaz-Elsayed, K C Morris, Julius Schoop","doi":"10.1520/ssms20200052","DOIUrl":"https://doi.org/10.1520/ssms20200052","url":null,"abstract":"<p><p>The unique and unprecedented challenges of the COVID-19 pandemic have resulted in significant disruptions to diverse manufacturing supply chains across the globe. The negative economic impacts of these unexpected and rapid changes in demand and available supplies have been severe, and the economic sustainability of many businesses has been revealed as being highly sensitive to such changes. COVID-19 will inevitably change manufacturing, and potentially in a way that is not sustainable unless we factor sustainability into our \"redesign.\" Otherwise, the industry will remain overwhelmed in a reactionary cycle when the next major problem emerges, such as a lack of resources during a natural or man-made disaster. In this article, we present strategies for addressing three sustainability challenges relevant to manufacturing introduced by the COVID-19 pandemic: 1) an increase in waste generation, 2) uncertainty in life cycle impacts, and 3) navigating new modes of operation for manufacturing. To mitigate the sustainability challenges of COVID-19 and create a more resilient industrial sector, we need to assess the potential of each risk to product development and production processes. We envision a systematic integration of sustainable manufacturing principles and metrics into the business practices of manufacturing enterprises, including the products they produce and the processes used to create them. Realizing this vision will require greater availability and transparency of key data related to environmental and social sustainability factors, to create a clean and sustainable future in which pandemic and disaster readiness is realized through sustainable manufacturing.</p>","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"4 3","pages":"314-318"},"PeriodicalIF":1.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8752048/pdf/nihms-1701358.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39817005","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}
Pub Date : 2020-01-01DOI: 10.1016/b978-0-12-820027-8.00004-6
James F. Davis, H. Malkani, J. Dyck, P. Korambath, J. Wise
{"title":"Cyberinfrastructure for the democratization of smart manufacturing","authors":"James F. Davis, H. Malkani, J. Dyck, P. Korambath, J. Wise","doi":"10.1016/b978-0-12-820027-8.00004-6","DOIUrl":"https://doi.org/10.1016/b978-0-12-820027-8.00004-6","url":null,"abstract":"","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"100 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80591218","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}
Economic value added is a primary metric for measuring manufacturing activity; however, this metric and others exclude approximately half of the economic activity necessary for producing manufactured goods. With the recent disruption in the supply of goods and services by the COVID-19 pandemic, the criticality of these supply chains to production has become more apparent. Measuring and understanding these additional activities is foundational to reducing the effect of supply chain disruption. Additionally, manufacturing supply chains are fundamental to any response to the virus, including the production of masks, tests, and eventually a vaccine. When looked at closely, manufacturing stands out as a key driver of our economy. New manufacturing technologies can be leveraged to differentiate products in multiple ways resulting in a greater variety of products made more efficiently, with less environmental impacts, and higher quality. In addition, the digitization of manufacturing supports supply chains that are more connected, anticipatory, and agile. Metrics are needed that better reflect the role manufacturing plays in society, that better identify the social gains manufacturing produces, and that better establish the total economic activity that drives production. In this paper we propose a macro-economic metric to better measure the influence of manufacturing on our economy as an example of one such measure. We argue a need for solidifying similar radical changes to our current ways of measuring manufacturing's relevance and emphasizing the impact of new technologies that support the manufacturing economic sector.
{"title":"Measuring Manufacturing's Significance in the USA.","authors":"K C Morris, Douglas S Thomas","doi":"10.1520/SSMS20200054","DOIUrl":"https://doi.org/10.1520/SSMS20200054","url":null,"abstract":"<p><p>Economic value added is a primary metric for measuring manufacturing activity; however, this metric and others exclude approximately half of the economic activity necessary for producing manufactured goods. With the recent disruption in the supply of goods and services by the COVID-19 pandemic, the criticality of these supply chains to production has become more apparent. Measuring and understanding these additional activities is foundational to reducing the effect of supply chain disruption. Additionally, manufacturing supply chains are fundamental to any response to the virus, including the production of masks, tests, and eventually a vaccine. When looked at closely, manufacturing stands out as a key driver of our economy. New manufacturing technologies can be leveraged to differentiate products in multiple ways resulting in a greater variety of products made more efficiently, with less environmental impacts, and higher quality. In addition, the digitization of manufacturing supports supply chains that are more connected, anticipatory, and agile. Metrics are needed that better reflect the role manufacturing plays in society, that better identify the social gains manufacturing produces, and that better establish the total economic activity that drives production. In this paper we propose a macro-economic metric to better measure the influence of manufacturing on our economy as an example of one such measure. We argue a need for solidifying similar radical changes to our current ways of measuring manufacturing's relevance and emphasizing the impact of new technologies that support the manufacturing economic sector.</p>","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"4 no3 2020","pages":""},"PeriodicalIF":1.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8751656/pdf/nihms-1701362.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39817006","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}
A. Gaikwad, Farhad Imani, Hui Yang, E. Reutzel, Prahalada K. Rao
{"title":"In Situ Monitoring of Thin-Wall Build Quality in Laser Powder Bed Fusion Using Deep Learning","authors":"A. Gaikwad, Farhad Imani, Hui Yang, E. Reutzel, Prahalada K. Rao","doi":"10.1520/ssms20190027","DOIUrl":"https://doi.org/10.1520/ssms20190027","url":null,"abstract":"","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"71 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2019-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90362740","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}
Convolutional 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, partially due to the lack of standardized methods for deep neural network representation. Additionally, the interoperability between different machine learning frameworks remains poor. This paper 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 Xray 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.
{"title":"A Standardized PMML Format for Representing Convolutional Neural Networks with Application to Defect Detection.","authors":"M. Ferguson, Y. T. Lee, A. Narayanan, K. Law","doi":"10.1520/ssms20190032","DOIUrl":"https://doi.org/10.1520/ssms20190032","url":null,"abstract":"Convolutional 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, partially due to the lack of standardized methods for deep neural network representation. Additionally, the interoperability between different machine learning frameworks remains poor. This paper 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 Xray 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.","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"72 1","pages":"79-97"},"PeriodicalIF":1.0,"publicationDate":"2019-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76665459","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}
Vincenzo Ferrero, A. Raman, Karl R. Haapala, Bryony DuPont
Sustainability 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.
{"title":"Validating the Sustainability of Eco-Labeled Products Using a Triple-Bottom-Line Analysis","authors":"Vincenzo Ferrero, A. Raman, Karl R. Haapala, Bryony DuPont","doi":"10.1520/ssms20190022","DOIUrl":"https://doi.org/10.1520/ssms20190022","url":null,"abstract":"Sustainability 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.","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"60 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84602815","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}
Vendy Eko Prasetyo, B. Belleville, B. Ozarska, J. Mo
Enhanced wood recovery mirrors a successful wood manufacturing operation. Studies of wood recovery in secondary wood processing, however, are scarce, particularly in furniture manu-facturing. Although recovery rates are under the continuous surveillance of sophisticated tech-nology, 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 sys-tem, a different layout process and distinctive machinery, species, and other customer require-ments. 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 furni-ture-making station. There was a significant difference in cumulative wood recovery rates be-tween 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.
{"title":"A Wood Recovery Assessment Method Comparison between Batch and Cellular Production Systems in the Furniture Industry","authors":"Vendy Eko Prasetyo, B. Belleville, B. Ozarska, J. Mo","doi":"10.1520/SSMS20190001","DOIUrl":"https://doi.org/10.1520/SSMS20190001","url":null,"abstract":"Enhanced wood recovery mirrors a successful wood manufacturing operation. Studies of wood recovery in secondary wood processing, however, are scarce, particularly in furniture manu-facturing. Although recovery rates are under the continuous surveillance of sophisticated tech-nology, 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 sys-tem, a different layout process and distinctive machinery, species, and other customer require-ments. 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 furni-ture-making station. There was a significant difference in cumulative wood recovery rates be-tween 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.","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"42 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79926037","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}
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