Tool wear condition monitoring based on wavelet packet analysis and RBF neural network

CNC milling is widely used in manufacturing complex parts of aerospace fields, and the development of the intelligent tool wear monitoring can improve the utilization of the tool during the milling process while ensuring the surface quality of the processed parts. In this paper, a novel method based on wavelet packet analysis and RBF neural network was proposed for monitoring the tool wear condition during milling. Firstly, cutting force signals were measured during milling, and filtered by filter function. Secondly, the cutting vibration signals caused by tool wear were separated by the wavelet packet decomposition from initial data, and the energy of the reconstructed signals was characterized for analyzing tool wear during the milling process. Then, the filtered cutting force and the cutting vibration features were trained by RBF neural network. Fifteen groups of features were trained by RBF neural network, and three groups of features were used to test RBF neural network. Finally, the results show that the method can accurately monitor the flank wear of milling cutter within a short time, which provides a theoretical basis and experimental scheme for further implementing the on-line tool wear monitoring.