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Proceedings Paper

Cutting tool monitoring by acoustic emission based upon wavelet-neural networks
Author(s): Weigong Huang; Jisheng Wang
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Paper 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.

Paper Details

Date Published: 20 February 2006
PDF: 6 pages
Proc. SPIE 6041, ICMIT 2005: Information Systems and Signal Processing, 60411I (20 February 2006); doi: 10.1117/12.664337
Show Author Affiliations
Weigong Huang, Xihua Univ. (China)
Jisheng Wang, Xihua Univ. (China)


Published in SPIE Proceedings Vol. 6041:
ICMIT 2005: Information Systems and Signal Processing
Yunlong Wei; Kil To Chong; Takayuki Takahashi, Editor(s)

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