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《Tool Engineering》 2009-02
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Tool Wear State Monitoring Based on Wavelet Multi-resolution Analysis and RBF Neural Network

Tang Wei (Department of Mechanical Manufacturing and Automation,Shanghai Jiao Tong University,Shanghai 200240,China) Wang Haili Zhuang Zijie et al  
The tool wear monitoring has a great significance to improve the accuracy and automation of manufacturing process.A model of tool wear state monitoring based on RBF neural network was proposed.In this system,the cutting process is monitored by means of acoustic emission sensors,and the feature vectors of tool wear are extracted from AE signals by using the wavelet multi-resolution decomposition technology and are input into RBF neural network,so that the automatic detection of tool wear state can be carried out.
【CateGory Index】: TG71
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