Ayantha Senanayaka, Wenmeng Tian, T. Falls, L. Bian
{"title":"了解工艺条件对热缺陷关系的影响:一种传递机器学习方法","authors":"Ayantha Senanayaka, Wenmeng Tian, T. Falls, L. Bian","doi":"10.1115/1.4057052","DOIUrl":null,"url":null,"abstract":"\n This study aims to develop an intelligent, rapid porosity prediction methodology for varying process conditions based on knowledge transfer from the existing process conditions. Conventional machine learning algorithms are extensively used in porosity prediction. However, these approaches assume that the training (source) and testing (target) data follow the same probability distribution, and the labeled data are available in both source and target domains. The source and target do not follow the same distribution in real-world manufacturing environments. The diversity of industrialization processes leads to heterogeneous data collection in different production conditions, and labeling is costly. Transfer learning is one of the robust techniques that enables transferring learned knowledge between source and target to establish a relationship while the target has less data. Therefore, this paper presents similarity-based multi-source transfer learning(SiMuS-TL) method to develop a relationship between a source and an unknown target. The similarities between sources and targets are learned by forming a new domain called the mixed domain, which organizes data into identity groups. Then, a group-based learning process is designated to transfer knowledge to make target predictions. The effectiveness of the SiMuS-TL is explored with the application of porosity prediction in additively manufactured parts in realistic situations, i.e., single-source and multi-sources transfer to unknown target porosity prediction. The porosity prediction accuracies are approximately 90% for both scenarios with the SiMuS-TL method, but conventional SVM and CNN classifiers barely perform well in predicting porosity while process condition varies.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":"25 12","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Understanding the Effects of Process Conditions on Thermal-Defect Relationship: A Transfer Machine Learning Approach\",\"authors\":\"Ayantha Senanayaka, Wenmeng Tian, T. Falls, L. Bian\",\"doi\":\"10.1115/1.4057052\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n This study aims to develop an intelligent, rapid porosity prediction methodology for varying process conditions based on knowledge transfer from the existing process conditions. Conventional machine learning algorithms are extensively used in porosity prediction. However, these approaches assume that the training (source) and testing (target) data follow the same probability distribution, and the labeled data are available in both source and target domains. The source and target do not follow the same distribution in real-world manufacturing environments. The diversity of industrialization processes leads to heterogeneous data collection in different production conditions, and labeling is costly. Transfer learning is one of the robust techniques that enables transferring learned knowledge between source and target to establish a relationship while the target has less data. Therefore, this paper presents similarity-based multi-source transfer learning(SiMuS-TL) method to develop a relationship between a source and an unknown target. The similarities between sources and targets are learned by forming a new domain called the mixed domain, which organizes data into identity groups. Then, a group-based learning process is designated to transfer knowledge to make target predictions. The effectiveness of the SiMuS-TL is explored with the application of porosity prediction in additively manufactured parts in realistic situations, i.e., single-source and multi-sources transfer to unknown target porosity prediction. The porosity prediction accuracies are approximately 90% for both scenarios with the SiMuS-TL method, but conventional SVM and CNN classifiers barely perform well in predicting porosity while process condition varies.\",\"PeriodicalId\":16299,\"journal\":{\"name\":\"Journal of Manufacturing Science and Engineering-transactions of The Asme\",\"volume\":\"25 12\",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-03-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Manufacturing Science and Engineering-transactions of The Asme\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4057052\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Science and Engineering-transactions of The Asme","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4057052","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
Understanding the Effects of Process Conditions on Thermal-Defect Relationship: A Transfer Machine Learning Approach
This study aims to develop an intelligent, rapid porosity prediction methodology for varying process conditions based on knowledge transfer from the existing process conditions. Conventional machine learning algorithms are extensively used in porosity prediction. However, these approaches assume that the training (source) and testing (target) data follow the same probability distribution, and the labeled data are available in both source and target domains. The source and target do not follow the same distribution in real-world manufacturing environments. The diversity of industrialization processes leads to heterogeneous data collection in different production conditions, and labeling is costly. Transfer learning is one of the robust techniques that enables transferring learned knowledge between source and target to establish a relationship while the target has less data. Therefore, this paper presents similarity-based multi-source transfer learning(SiMuS-TL) method to develop a relationship between a source and an unknown target. The similarities between sources and targets are learned by forming a new domain called the mixed domain, which organizes data into identity groups. Then, a group-based learning process is designated to transfer knowledge to make target predictions. The effectiveness of the SiMuS-TL is explored with the application of porosity prediction in additively manufactured parts in realistic situations, i.e., single-source and multi-sources transfer to unknown target porosity prediction. The porosity prediction accuracies are approximately 90% for both scenarios with the SiMuS-TL method, but conventional SVM and CNN classifiers barely perform well in predicting porosity while process condition varies.
期刊介绍:
Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining