Acoustic signal-based wear monitoring for belt grinding tools with pyramid-structured abrasives using BO-KELM

Abstract

Pyramid-structured abrasive belts have been widely used in the field of precision machining of complex surfaces over recent years. However, continuous wear directly affects their machining performance and quality. The lack of effective engineering monitoring methods limits the further application of such abrasive belts. To address this issue, this study presents an acoustic signal monitoring method for the wear state of pyramid-structured abrasive belts based on the BO-KELM model. Compared with traditional methods, the proposed method can automatically adjust model hyperparameters, saving manual tuning time and improving model performance. A Rat index is proposed, which accurately quantifies the wear state of the abrasive belt. When the number of wear states is set to 10, the proposed method achieves precision matrix-based accuracy, precision, recall, and F1 score values of 97.88 %, 95.90 %, 96.01 %, and 0.9592, respectively. The model performs even better when the number of wear states is reduced.