A study on tool wear monitoring using time-frequency transformation techniques

Proceedings of the 2014 International Conference on Innovative Design and Manufacturing (ICIDM) Pub Date : 2014-08-01 DOI:10.1109/IDAM.2014.6912718

Javad Soltani Rad, Youmin Zhang, F. Aghazadeh, Zezhong C. Chen

{"title":"A study on tool wear monitoring using time-frequency transformation techniques","authors":"Javad Soltani Rad, Youmin Zhang, F. Aghazadeh, Zezhong C. Chen","doi":"10.1109/IDAM.2014.6912718","DOIUrl":null,"url":null,"abstract":"It is in a high demand to automatically monitor and diagnose tool wear, tool fault, or tool damage during machining process to increase efficiency and product quality and reduce production cost. This paper investigates an online tool condition monitoring method using acoustic emission signal in milling operation. The flank wear (VB) is investigated as the system fault. The nature of faulty signals in tool condition monitoring (TCM) is time varying. Therefore time-frequency transformation is an ideal analysis tool for signal interpretation. Short-time Fourier transform (STFT), Wavelet transform, S-transform, the smoothed pseudo-Wigner-Ville distribution and the Choi-Williams distribution are used for signal decomposition and two-dimensional (2D) principal component analysis (PCA) is implemented for dimensionality reduction. A 2D correlation analysis represents the deviation of the faulty signals from the healthy signal and a curve fitting approach is used to find the tool fault. Experimental tests are used for validation and the efficiency of each time-frequency transformation method in the designed TCM system is evaluated and compared.","PeriodicalId":135246,"journal":{"name":"Proceedings of the 2014 International Conference on Innovative Design and Manufacturing (ICIDM)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2014 International Conference on Innovative Design and Manufacturing (ICIDM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IDAM.2014.6912718","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 10

Abstract

It is in a high demand to automatically monitor and diagnose tool wear, tool fault, or tool damage during machining process to increase efficiency and product quality and reduce production cost. This paper investigates an online tool condition monitoring method using acoustic emission signal in milling operation. The flank wear (VB) is investigated as the system fault. The nature of faulty signals in tool condition monitoring (TCM) is time varying. Therefore time-frequency transformation is an ideal analysis tool for signal interpretation. Short-time Fourier transform (STFT), Wavelet transform, S-transform, the smoothed pseudo-Wigner-Ville distribution and the Choi-Williams distribution are used for signal decomposition and two-dimensional (2D) principal component analysis (PCA) is implemented for dimensionality reduction. A 2D correlation analysis represents the deviation of the faulty signals from the healthy signal and a curve fitting approach is used to find the tool fault. Experimental tests are used for validation and the efficiency of each time-frequency transformation method in the designed TCM system is evaluated and compared.

查看原文本刊更多论文

基于时频变换技术的刀具磨损监测研究

为了提高加工效率和产品质量，降低生产成本，对加工过程中刀具磨损、刀具故障或刀具损坏的自动监测和诊断提出了很高的要求。研究了一种利用声发射信号对铣削加工过程中的刀具状态进行在线监测的方法。将侧面磨损作为系统故障进行了研究。刀具状态监测中的故障信号具有时变特性。因此，时频变换是一种理想的信号解释分析工具。利用短时傅里叶变换(STFT)、小波变换、s变换、平滑伪wigner - ville分布和Choi-Williams分布进行信号分解，并利用二维主成分分析(PCA)进行降维。二维相关分析表示故障信号与健康信号的偏差，并采用曲线拟合方法寻找刀具故障。通过实验验证，对所设计的TCM系统中各时频变换方法的效率进行了评价和比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Proceedings of the 2014 International Conference on Innovative Design and Manufacturing (ICIDM)

自引率

0.00%

发文量