Monitoring tool wear states in turning based on wavelet analysis

被引：0

作者：

Wang, Zhong-Min ^{[1
]}

Wang, Xin-Yi ^{[1
]}

Chen, Ai-Di ^{[1
]}

Jia, Yu-Ping ^{[1
]}

机构：

[1] Sch. of Mech. Eng. and Automat., Beijing Inst. of Technol., Beijing 100081, China

来源：

Journal of Beijing Institute of Technology (English Edition) | 2001年 / 10卷 / 01期

关键词：

Acoustic emissions - Fuzzy sets - Signal processing - Wavelet transforms - Wear of materials;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

To monitor the tool wear states in turning, a new way based on the wavelet transform was proposed to get the signal characters, which can reflect the tool wear states. Using discrete dyadic wavelet transform, the acoustic emission (AE) signal of cutting process was decomposed; the root mean square (RMS) values of the decomposed signals at different scales were taken as the feature vector; the technique of fuzzy pattern identification was used to identify the tool wear states. Experimental results showed that the monitoring technique based on wavelet analysis is suitable for real-time implementation in manufacturing application.

引用

页码：101 / 107