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| Tool Wear State Recognition Based on Cutting Acoustic Emission Signal Measurement |
| ZHU Jian-min,ZHAN Han,ZHANG Tong-chao,WANG Jian |
| University of Shanghai for Science and Technology, Shanghai 200093, China |
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Abstract Aiming at the low recognition rate under changing processing conditions of the existing tool wear state recognition methods, according to real-time acquisition of acoustic emission signal, an adaptive tool wear state features extraction method from acoustic emission signal and a tool wear state recognition method based on grey relational analysis between wear state feature data sequences are proposed. Experiment with four WNMG080408-TM T9125 type turning tools on ZCK20 digital controlled lathe was conducted and tool wear state recognition was implemented, the results show that the proposed methods are able to acquire the turning tools’ wear state feature effectively and adaptively, and the tools wear state recognition results are consistent with the actual condition, and a high recognition rate is achieved.
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2015, Vol. 36 
