Tool Wear Monitoring of Milling Center Based on Optimize Variational Mode Decomposition

The tool wear monitoring of milling center is of great significance to reduce downtime and ensure quality of workpieces. A new method has been developed to classify tool wear based on vibration signals of milling process. The parameters of revised variational mode decomposition are selected by quantum genetic algorithm. The vibration signals of milling process are analyzed by the revised variational mode decomposition and classified by the gradient boosting decision tree algorithm. The comparison results indicate that the proposed method is an efficient method for tool wear monitoring of milling center.