{"title":"基于数据挖掘的致动器制造整体生产分析","authors":"Christian Sand, K. Bogus, Sabrina Kunz, J. Franke","doi":"10.1109/EDPC.2016.7851346","DOIUrl":null,"url":null,"abstract":"Holistic production optimizations within large-scale productions are not yet used because classic methods like Six Sigma or DoE are less expedient when it comes to huge or more complex data sets. Thus no standardized analysis for integrated production optimization exists, to realize 0 ppm defects. This paper introduces a holistic analytics approach using data mining techniques to reduce the error and scrap rate, addressing experts and non-specialized workers.","PeriodicalId":121418,"journal":{"name":"2016 6th International Electric Drives Production Conference (EDPC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Holistic production analysis for actuator manufacturing using data mining\",\"authors\":\"Christian Sand, K. Bogus, Sabrina Kunz, J. Franke\",\"doi\":\"10.1109/EDPC.2016.7851346\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Holistic production optimizations within large-scale productions are not yet used because classic methods like Six Sigma or DoE are less expedient when it comes to huge or more complex data sets. Thus no standardized analysis for integrated production optimization exists, to realize 0 ppm defects. This paper introduces a holistic analytics approach using data mining techniques to reduce the error and scrap rate, addressing experts and non-specialized workers.\",\"PeriodicalId\":121418,\"journal\":{\"name\":\"2016 6th International Electric Drives Production Conference (EDPC)\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 6th International Electric Drives Production Conference (EDPC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EDPC.2016.7851346\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 6th International Electric Drives Production Conference (EDPC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EDPC.2016.7851346","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Holistic production analysis for actuator manufacturing using data mining
Holistic production optimizations within large-scale productions are not yet used because classic methods like Six Sigma or DoE are less expedient when it comes to huge or more complex data sets. Thus no standardized analysis for integrated production optimization exists, to realize 0 ppm defects. This paper introduces a holistic analytics approach using data mining techniques to reduce the error and scrap rate, addressing experts and non-specialized workers.