Cutting tool monitoring by acoustic emission based upon wavelet-neural networks

Weigong Huang; Jisheng Wang

doi:10.1117/12.664337

20 February 2006 Cutting tool monitoring by acoustic emission based upon wavelet-neural networks

Weigong Huang, Jisheng Wang

Proceedings Volume 6041, ICMIT 2005: Information Systems and Signal Processing; 60411I (2006) https://doi.org/10.1117/12.664337
Event: ICMIT 2005: Merchatronics, MEMS, and Smart Materials, 2005, Chongqing, China

Abstract

The features of cutting tool states (normal, worm, breakage) were extracted using Acoustic Emission (AE) signals. AE signals were measured by a built-in piezoelectric transducer, which was inserted in the tool holder of an NC lathe. The 8 wavelet packets were taken using wavelet packet analysis for 3-leve. The powers calculating from the 8 wavelet packets were as 8 nodes of the input layer in BP neural networks, which identified three states of cutting tool. The corrected rate of classification in the experiments were normal 100%, worn 95%, breakage 95%. The results obtained show that this method is reliable and efficient.

Citation Download Citation

Weigong Huang and Jisheng Wang "Cutting tool monitoring by acoustic emission based upon wavelet-neural networks", Proc. SPIE 6041, ICMIT 2005: Information Systems and Signal Processing, 60411I (20 February 2006); https://doi.org/10.1117/12.664337

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