Measurement and prediction of wear volume of the tool in nonlinear degradation process based on multi-sensor information fusion

Excessive tool wear seriously affects the surface quality of the workpiece and reduces the processing efficiency. Therefore, real-time monitoring of the tool wear status is very important. This paper proposes a new tool wear prediction method based on multi-sensor hybrid domain information fusion. First, the collected multi-sensor signals are decomposed by wavelet packet to extract the energy values of 16 frequency bands; in addition, the energy values are combined with the time domain and frequency domain features to construct a hybrid domain feature set; then, the gated recurrent unit model is used to adaptively explore the internal relationship between the hybrid domain features and tool wear, which overcomes the low efficiency of manual feature fusion monitoring; finally, the wear milling cutter data is used to verify the superiority of the proposed method. The results show that the prediction accuracy of tool wear based on multi sensor feature fusion is significantly better than that based on a single sensor. Also, compared with traditional wear prediction methods, it again verifies the advancement of the proposed method in predicting tool wear.