{"title":"基于知识提炼的先进制造过程在线监控多阶段增量学习","authors":"Zhangyue Shi, Yuxuan Li, Chenang Liu","doi":"10.1109/ICDMW58026.2022.00154","DOIUrl":null,"url":null,"abstract":"In advanced manufacturing, the incorporation of online sensing technologies has enabled great potentials to achieve effective in-situ process monitoring via machine learning-based approaches. In manufacturing practice, the online sensor data are usually collected in a progressive manner, and the stream data collected at latter stages may also contain informative knowledge for process monitoring. Therefore, it is highly valuable to make the machine learning-based monitoring model learn incrementally in manufacturing. To achieve this goal, this paper develops a multi-stage incremental learning approach enabled by the knowledge distillation, which distills representative information from the machine learning model trained at early/offline stage and then enhances the monitoring performance at the latter stages. To validate its effectiveness, a real-world case study in additive manufacturing, which is an emerging advanced manufacturing technology, is conducted. The experimental results show that the developed knowledge distillation-enabled multi-stage incremental learning is very promising to improve the online monitoring performance in advanced manufacturing.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Knowledge Distillation-enabled Multi-stage Incremental Learning for Online Process Monitoring in Advanced Manufacturing\",\"authors\":\"Zhangyue Shi, Yuxuan Li, Chenang Liu\",\"doi\":\"10.1109/ICDMW58026.2022.00154\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In advanced manufacturing, the incorporation of online sensing technologies has enabled great potentials to achieve effective in-situ process monitoring via machine learning-based approaches. In manufacturing practice, the online sensor data are usually collected in a progressive manner, and the stream data collected at latter stages may also contain informative knowledge for process monitoring. Therefore, it is highly valuable to make the machine learning-based monitoring model learn incrementally in manufacturing. To achieve this goal, this paper develops a multi-stage incremental learning approach enabled by the knowledge distillation, which distills representative information from the machine learning model trained at early/offline stage and then enhances the monitoring performance at the latter stages. To validate its effectiveness, a real-world case study in additive manufacturing, which is an emerging advanced manufacturing technology, is conducted. The experimental results show that the developed knowledge distillation-enabled multi-stage incremental learning is very promising to improve the online monitoring performance in advanced manufacturing.\",\"PeriodicalId\":146687,\"journal\":{\"name\":\"2022 IEEE International Conference on Data Mining Workshops (ICDMW)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Data Mining Workshops (ICDMW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMW58026.2022.00154\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW58026.2022.00154","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Knowledge Distillation-enabled Multi-stage Incremental Learning for Online Process Monitoring in Advanced Manufacturing
In advanced manufacturing, the incorporation of online sensing technologies has enabled great potentials to achieve effective in-situ process monitoring via machine learning-based approaches. In manufacturing practice, the online sensor data are usually collected in a progressive manner, and the stream data collected at latter stages may also contain informative knowledge for process monitoring. Therefore, it is highly valuable to make the machine learning-based monitoring model learn incrementally in manufacturing. To achieve this goal, this paper develops a multi-stage incremental learning approach enabled by the knowledge distillation, which distills representative information from the machine learning model trained at early/offline stage and then enhances the monitoring performance at the latter stages. To validate its effectiveness, a real-world case study in additive manufacturing, which is an emerging advanced manufacturing technology, is conducted. The experimental results show that the developed knowledge distillation-enabled multi-stage incremental learning is very promising to improve the online monitoring performance in advanced manufacturing.