Monitoring Method for Wear State of Single Crystal Diamond Cutting Tools Based on WPA-BP Multi-Information Fusion

As an important factor in mechanical production, the wear state of single crystal diamond tool directly influences the machining quality and dimensional accuracy of the workpiece. In order to improve the recognition accuracy of tool wear state, a multi-information fusion tool wear state recognition method based on Wolf Pack Algorithm (WPA) optimized BP neural network was proposed in this paper. The tool vibration signal and the acoustic emission (AE) signal were used as the tool wear state recognition signal to perform wavelet packet decomposition. In this way, the best characteristic frequency band representing the tool wear state is obtained, and the energy value of the best frequency band obtained by the signal is taken as the characteristic parameter. After normalization, the multi-information WPA-BP tool wear state recognition model is input to realize multi-information fusion and output recognition results. By optimizing the initial weight of BP neural network by WPA algorithm, the problem that BP neural network is prone to fall into local minimum during training is solved. The experimental findings demonstrate that the accuracy of optimized multi-information fusion WPA-BP model recognition reaches 86.7%, which effectively improves the accuracy and stability of tool wear state recognition.