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《Computer Engineering & Science》 2008-02
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End Mill Wear Monitoring Based on Computational Intelligent Algorithms

ZHENG Jin-xing,ZHANG Ming-jun,MENG Qing-xin(School of Mechatronics Engineering,Harbin Engineering University,Harbin 150001,China)  
A computational intelligent data fusion method for monitoring end mill wear is presented in this paper.The signals of cutting force and vibration are measured with multi-sensors,the cutting force features are extracted with frequency mutation and the vibration features are extracted using wavelet package decomposition.Several computational intelligent data fusion methods,which are wavelet neural networks,genetic algorithm neural networks(GA-NN),and wavelet generic algorithm neural networks for predicting the tool wear values are discussed.The experimental results show all of these presented methods can effectively perform tool wear prediction.
【CateGory Index】: TP274
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