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. (c) 2007 Elsevier Ltd. All rights reserved.
Original language | English |
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Pages (from-to) | 371-379 |
Number of pages | 9 |
Journal | International Journal of Machine Tools and Manufacture |
Volume | 48 |
Issue number | 3-4 |
DOIs | |
Publication status | Published - 1 Mar 2008 |
Keywords
- complexity measure
- wavelet transform
- tool breakage
- permutation entropy
- motor current signals
- end milling