Complexity measure of motor current signals for tool flute breakage detection in end milling |
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Authors: | Xiaoli Li Gaoxiang Ouyang Zhenhu Liang |
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Affiliation: | aInstitute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004 Hebei, China;bCercia, School of Computer Science, The University of Birmingham, B15 2TT, UK;cDepartment of MEEM, City University of Hong Kong, China |
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Abstract: | Automated tool condition monitoring is an important issue in the advanced machining process. Permutation entropy of a time series is a simple, robust and extremely fast complexity measure method for distinguishing the different conditions of a physical system. In this study, the permutation entropy of feed-motor current signals in end milling was applied to detect tool breakage. The detection method is composed of the estimation of permutation entropy and wavelet-based de-noising. To confirm the effectiveness and robustness of the method, typical experiments have been performed from the cutter runout and entry/exit cuts to cutting parameters variation. Results showed that the new method could successfully extract significant signature from the feed-motor current signals to effectively detect tool flute breakage during end milling. Whilst, this detection method was based on current sensors, so it possesses excellent potential for practical and real-time application at a low cost by comparison with the alternative sensors. |
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Keywords: | Complexity measure Permutation entropy Wavelet transform End milling Motor current signals Tool breakage |
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