关键词: 刀具磨损, 多传感器, 混合智能数据融合, 小波包分解

Abstract: Hybrid intelligent data fusion for monitoring end milling tool wear is presented in this paper.Signals of cutting force and vibration is measured with multi-sensors and features extraction in frequency domain and time-frequency domain using wavelet package decomposition.Several hybrid intelligent data fusion methods,which are wavelet neural networks,Generic Algorithm Neural Networks（GA-NN）,and wavelet generic algorithm neural networks for predicting tool wear value are debated.The results show experimently all of these presented methods effectively implement tool wear monitoring and prediction,and the characters of these methods are analyzed.

Key words: tool wear, multi-sensors, hybrid intelligent data fusion, wavelet package decomposition