F. Peysson, B. Doche, P. Lepetit, D. Leon, R. Hernansanz
{"title":"刀具磨损评价中的非质量风险评价","authors":"F. Peysson, B. Doche, P. Lepetit, D. Leon, R. Hernansanz","doi":"10.17973/mmsj.2023_11_2023113","DOIUrl":null,"url":null,"abstract":"The development of part quality virtual sensors requires knowledge and observability of cutting conditions and in particular tool wear as tool are consumables. This paper presents an unsupervised anomalies detection approach to assess tool wear from standard machine load sensors in order to evaluate a non-quality risk metric. The developed methodology combines physics and business rules with density estimators to analyse the behaviour of axes and spindle loads. Industrial data from an automotive production line are used to illustrate the methodology application.","PeriodicalId":18723,"journal":{"name":"MM Science Journal","volume":"11 10","pages":"0"},"PeriodicalIF":0.6000,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"NON-QUALITY RISK EVALUATION FROM TOOL WEAR ASSESSMENT\",\"authors\":\"F. Peysson, B. Doche, P. Lepetit, D. Leon, R. Hernansanz\",\"doi\":\"10.17973/mmsj.2023_11_2023113\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The development of part quality virtual sensors requires knowledge and observability of cutting conditions and in particular tool wear as tool are consumables. This paper presents an unsupervised anomalies detection approach to assess tool wear from standard machine load sensors in order to evaluate a non-quality risk metric. The developed methodology combines physics and business rules with density estimators to analyse the behaviour of axes and spindle loads. Industrial data from an automotive production line are used to illustrate the methodology application.\",\"PeriodicalId\":18723,\"journal\":{\"name\":\"MM Science Journal\",\"volume\":\"11 10\",\"pages\":\"0\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2023-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MM Science Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17973/mmsj.2023_11_2023113\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MM Science Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17973/mmsj.2023_11_2023113","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
NON-QUALITY RISK EVALUATION FROM TOOL WEAR ASSESSMENT
The development of part quality virtual sensors requires knowledge and observability of cutting conditions and in particular tool wear as tool are consumables. This paper presents an unsupervised anomalies detection approach to assess tool wear from standard machine load sensors in order to evaluate a non-quality risk metric. The developed methodology combines physics and business rules with density estimators to analyse the behaviour of axes and spindle loads. Industrial data from an automotive production line are used to illustrate the methodology application.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
