{"title":"Research on Tool Wear State Based on Cutting Audio and Temperature Parameter Fitting","authors":"Ning Ding, Minliang Zhang","doi":"10.1145/3366194.3366214","DOIUrl":null,"url":null,"abstract":"Tool condition monitoring technology, as an important part of intelligent manufacturing field, plays a great role in promoting automation and intellectualization in the production and processing process. In this paper, audio and temperature signal parameters are used to monitor tool wear state during cutting. Then the characteristic parameters of audio and temperature signals obtained from experiments under different wear states are fitted and analyzed, and the acoustic signals are analyzed by wavelet transform. The application results show that this monitoring method effectively solves the limitation and stability of single factor monitoring tool status, and is of great significance to improve the processing efficiency and accuracy.","PeriodicalId":105852,"journal":{"name":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3366194.3366214","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Tool condition monitoring technology, as an important part of intelligent manufacturing field, plays a great role in promoting automation and intellectualization in the production and processing process. In this paper, audio and temperature signal parameters are used to monitor tool wear state during cutting. Then the characteristic parameters of audio and temperature signals obtained from experiments under different wear states are fitted and analyzed, and the acoustic signals are analyzed by wavelet transform. The application results show that this monitoring method effectively solves the limitation and stability of single factor monitoring tool status, and is of great significance to improve the processing efficiency and accuracy.