Monitoring cutting tool wear based on spindle current signal multi-feature fusion

In order to gain long-term online monitoring cutting tool wear data under normal cutting conditions, a new method for monitoring cutting tool wear was proposed based on the spindle current signal and particle swarm optimization support vector machine (PSO-SVM) model. Firstly, the spindle motor current signals of CNC machine tool were analyzed. Multiple feature parameters related to tool wear and the EMD energy entropy were fused as an input feature vector. Secondly, the SVM model parameters were optimized by the PSO algorithm. The tool wear condition recognition model was established based on the spindle current signal and PSO-SVM theory. Finally, the spindle current signals of the vertical machining center under different tool wear conditions were collected by the experiment. The proposed method was compared with traditional SVM model and BP neural network model. The results show that the proposed method has higher recognition accuracy and better generalization ability. The proposed method can realize the long-term online monitoring of the tool wear condition.