{"title":"使用机器学习来理解制造控制问题","authors":"B. L. Whitehall, B. Fulkerson, J. Hall, S. Lu","doi":"10.1109/CAIA.1992.200029","DOIUrl":null,"url":null,"abstract":"The authors describe how machine learning can be used to help departmental supervisors to operate a factory as an integrated system. The feasibility of predicting potential problems on the shop floor using symbolic machine learning and neural networks is demonstrated with simulated data of a single department, paint system, and final assembly line. Rules of operation implicit in the simulation model were identified by both methods.<<ETX>>","PeriodicalId":388685,"journal":{"name":"Proceedings Eighth Conference on Artificial Intelligence for Applications","volume":"364 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using machine learning to understand manufacturing control issues\",\"authors\":\"B. L. Whitehall, B. Fulkerson, J. Hall, S. Lu\",\"doi\":\"10.1109/CAIA.1992.200029\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors describe how machine learning can be used to help departmental supervisors to operate a factory as an integrated system. The feasibility of predicting potential problems on the shop floor using symbolic machine learning and neural networks is demonstrated with simulated data of a single department, paint system, and final assembly line. Rules of operation implicit in the simulation model were identified by both methods.<<ETX>>\",\"PeriodicalId\":388685,\"journal\":{\"name\":\"Proceedings Eighth Conference on Artificial Intelligence for Applications\",\"volume\":\"364 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-03-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Eighth Conference on Artificial Intelligence for Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CAIA.1992.200029\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Eighth Conference on Artificial Intelligence for Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAIA.1992.200029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using machine learning to understand manufacturing control issues
The authors describe how machine learning can be used to help departmental supervisors to operate a factory as an integrated system. The feasibility of predicting potential problems on the shop floor using symbolic machine learning and neural networks is demonstrated with simulated data of a single department, paint system, and final assembly line. Rules of operation implicit in the simulation model were identified by both methods.<>
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