Prediction of tool breakage in face milling using support vector machine

被引：0
|
作者：
Hsueh, Yao-Wen [1 ]
Yang, Chan-Yun [1 ]
机构：
[1] Department of Mechanical Engineering, Technology and Science, Institute of Northern Taiwan, No. 2, Xueyuan Rd., Peitou, 112, Taipei, Taiwan
来源：
International Journal of Advanced Manufacturing Technology | 2008年 / 37卷 / 9-10期
关键词：
A new approach is proposed using a support vector machine (SVM) to classify the feature of the cutting force signal for the prediction of tool breakage in face milling. The cutting force signal is compressed by averaging the cutting force signals per tooth to extract the feature of the cutting force signal due to tool breakage. With the SVM learning process; the output of SVM's decision function can be utilized to identify a milling cutter with or without tool breakage. Experimental results are presented to verify the feasibility of this tool breakage prediction system in milling operations. © 2007 Springer-Verlag London Limited;
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页码：872 / 880
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