{"title":"Visualization and Explainable Machine Learning for Efficient Manufacturing and System Operations","authors":"D. Le, Vung V. Pham, Huyen N. Nguyen, Tommy Dang","doi":"10.1520/ssms20190029","DOIUrl":"https://doi.org/10.1520/ssms20190029","url":null,"abstract":"","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"7 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80828850","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}
Dan Newman, Mahmoud Parto, K. Saleeby, T. Kurfess, A. Dugenske
{"title":"Development of a Digital Architecture for Distributed CNC Machine Health Monitoring","authors":"Dan Newman, Mahmoud Parto, K. Saleeby, T. Kurfess, A. Dugenske","doi":"10.1520/ssms20190038","DOIUrl":"https://doi.org/10.1520/ssms20190038","url":null,"abstract":"","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"71 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90490641","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}
Jorge Chuya Sumba, Israel Ruiz Quinde, Luis Escajeda Ochoa, J. C. T. Martínez, Antonio Vallejo Guevara, R. Morales-Menéndez
{"title":"Intelligent Fault Diagnosis for Rotating Machines Using Deep Learning","authors":"Jorge Chuya Sumba, Israel Ruiz Quinde, Luis Escajeda Ochoa, J. C. T. Martínez, Antonio Vallejo Guevara, R. Morales-Menéndez","doi":"10.1520/ssms20190023","DOIUrl":"https://doi.org/10.1520/ssms20190023","url":null,"abstract":"","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"174 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79679065","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":"A Reference Model for Evolving Digital Twins and Its Application to Cases in the Manufacturing Floor","authors":"H. Ahuett-Garza, Pedro Daniel Urbina Coronado","doi":"10.1520/ssms20190049","DOIUrl":"https://doi.org/10.1520/ssms20190049","url":null,"abstract":"","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"24 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75976240","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}
H. Nagarajan, Suraj Panicker, H. Mokhtarian, Théo Remy-Lorit, E. Coatanéa, Romaric Prod’hon, Hesam Jafarian, Karl R. Haapala, Ananda Chakraborti
{"title":"Graph-Based Metamodeling for Characterizing Cold Metal Transfer Process Performance","authors":"H. Nagarajan, Suraj Panicker, H. Mokhtarian, Théo Remy-Lorit, E. Coatanéa, Romaric Prod’hon, Hesam Jafarian, Karl R. Haapala, Ananda Chakraborti","doi":"10.1520/ssms20190026","DOIUrl":"https://doi.org/10.1520/ssms20190026","url":null,"abstract":"","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"21 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85964790","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":"Smart Scheduling of a Batch Manufacturer’s Operations by Utilization of a Genetic Algorithm to Minimize Electrical Demand","authors":"Paige Brimley, Derek Machalek, Kody M. Powell","doi":"10.1520/ssms20190018","DOIUrl":"https://doi.org/10.1520/ssms20190018","url":null,"abstract":"","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"14 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81933426","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}
Adam Norton1, Peter Gavriel1, and Holly Yanco1 ABSTRACT Automatic 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 paper, 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 set up and increase versatility. For each test configuration, conditions are specified with respect to the a priori knowledge provided to the system (e.g., boundary and/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 environment (e.g., providing representations of obstacles in the system’s map when they are not physically present). Example test performance data from an AMR is 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
{"title":"A Standard Test Method for Evaluating Navigation and Obstacle Avoidance Capabilities of AGVs and AMRs","authors":"Adam Norton, Peter Gavriel, H. Yanco","doi":"10.1520/ssms20190028","DOIUrl":"https://doi.org/10.1520/ssms20190028","url":null,"abstract":"Adam Norton1, Peter Gavriel1, and Holly Yanco1 ABSTRACT Automatic 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 paper, 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 set up and increase versatility. For each test configuration, conditions are specified with respect to the a priori knowledge provided to the system (e.g., boundary and/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 environment (e.g., providing representations of obstacles in the system’s map when they are not physically present). Example test performance data from an AMR is 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","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"296 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83493797","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}
Vinh Nguyen, Thomas Malchodi, Mahmoud Dinar, S. Melkote, A. Mishra, Sudhir Rajagopalan
{"title":"An IoT Architecture for Automated Machining Process Control: A Case Study of Tool Life Enhancement in Turning Operations","authors":"Vinh Nguyen, Thomas Malchodi, Mahmoud Dinar, S. Melkote, A. Mishra, Sudhir Rajagopalan","doi":"10.1520/ssms20190017","DOIUrl":"https://doi.org/10.1520/ssms20190017","url":null,"abstract":"","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"4 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88205122","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":"Supply–Demand Prediction for Agile Manufacturing with Deep Neural Network","authors":"Rong Wen, Wenjing Yan","doi":"10.1520/ssms20190025","DOIUrl":"https://doi.org/10.1520/ssms20190025","url":null,"abstract":"","PeriodicalId":51957,"journal":{"name":"Smart and Sustainable Manufacturing Systems","volume":"26 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81504244","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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