{"title":"Prediction and detection of cutting tool failure by modified group method of data handling","authors":"Tetsutaro Uematsu , Naotake Mohri","doi":"10.1016/0020-7357(86)90197-6","DOIUrl":null,"url":null,"abstract":"<div><p>This paper describes a method of predicting and detecting cutting tool failure in a process computer by means of a statistical model formed by the group method of data handling (GMDH).</p><p>An algorithm for modifying a process model, which has been formed beforehand by GMDH, using the newest real process data is derived at first.</p><p>The first application of the algorithm is directed to the prediction of cutting tool wear. It has been found that the relative prediction errors fall within ±10% at more than 90% of predicting points.</p><p>The second application of the algorithm is directed to the detection of cutting tool failure by chipping. The dynamic model of the cutting torque signal is formulated by GMDH and continuously renewed using time series process data. The difference between the estimated process output and the real process data is always monitored and becomes remarkably large when the tool failure by chipping occurs.</p></div>","PeriodicalId":100704,"journal":{"name":"International Journal of Machine Tool Design and Research","volume":"26 1","pages":"Pages 69-80"},"PeriodicalIF":0.0000,"publicationDate":"1986-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0020-7357(86)90197-6","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Machine Tool Design and Research","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/0020735786901976","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20
Abstract
This paper describes a method of predicting and detecting cutting tool failure in a process computer by means of a statistical model formed by the group method of data handling (GMDH).
An algorithm for modifying a process model, which has been formed beforehand by GMDH, using the newest real process data is derived at first.
The first application of the algorithm is directed to the prediction of cutting tool wear. It has been found that the relative prediction errors fall within ±10% at more than 90% of predicting points.
The second application of the algorithm is directed to the detection of cutting tool failure by chipping. The dynamic model of the cutting torque signal is formulated by GMDH and continuously renewed using time series process data. The difference between the estimated process output and the real process data is always monitored and becomes remarkably large when the tool failure by chipping occurs.
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