Tool breakage diagnosis in face milling by support vector machine |
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| Authors: | Yao-Wen Hsueh Chan-Yun Yang |
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| Affiliation: | 1. Graduate School of Technology, Industrial and Social Sciences, Tokushima University, 2-1 Minami-jousanjima, Tokushima-shi, Tokushima 770-8506, Japan;2. School of Computing, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku Yokohama, Kanagawa 226-8502, Japan |
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| Abstract: | This paper introduces a new diagnosis technique for tool breakage in face milling using a support vector machine (SVM). The features of spindle displacement signals are first fed into the kernel-based SVM decision function. After the SVM learning procedure, the SVM can respond in real-time to automatically diagnose tool fracture under varying cutting conditions. Experimental results show that this new approach can detect tool breakage in a wide range of face-milling operations. |
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