Tool wear predicting based on weighted multi-kernel relevance vector machine and probabilistic kernel principal component analysis

This paper proposes a novel tool wear predicting method based on a weighted multi-kernel relevance vector machine (WMKRVM) and the integrated radial basis function-based probabilistic kernel principal component analysis (PKPCA_IRBF). The proposed WMKRVM model is constructed using the optimized standard single kernel RVM and its weight parameters. As a new dimension increment technique, PKPCA_IRBF can extract the noise information of the cutting force signal feature and incorporate the noise information into the model. Moreover, PKPCA_IRBF is first proposed to fuse the cutting force signal feature to improve the confidence interval provided by the WMKRVM model. Compared to the traditional PKPCA_RBF method, PKPCA_IRBF has a broader range of kernel parameter selection intervals and higher model accuracy. The cutting experiment is carried out to validate the effectiveness of the proposed tool wear predicting technique. Experimental results show that the proposed tool wear predicting technique can accurately monitor the tool wear width with strong robustness under various cutting conditions, laying the foundation for application in the industrial field.