Abrasive tool wear prediction based on an improved hybrid difference grey wolf algorithm for optimizing SVM

In the era of intelligent manufacturing, it is necessary to monitor the wear condition of abrasive tools in real time to prevent the deterioration of workpiece quality due to tool breakage and wear. A wear prediction model of abrasive tools based on an improved hybrid differential grey wolf optimization algorithm for optimizing support vector machine (IHDGWO-SVM) is proposed based on the grinding of zirconia ceramic holes by sintered diamond grind bits. The features of the force and vibration signals were extracted by time domain, frequency domain and wavelet analysis. The wear experimental results showed that the prediction accuracy of the IHDGWO-SVM model was 92%, which was significantly higher than the prediction accuracy of 68%, 80% and 72% of SVM, GWO-SVM and DE-SVM. The new IHDGWO-SVM model provides a theoretical and practical method for the on-line wear monitoring of abrasive tools during grinding of NMBM.