Online tool wear monitoring via a deep-kernel Gaussian process regression algorithm

Abstract

Machine learning is advantageous for the online monitoring of tool wear conditions. However, current algorithms encounter limitations in proper extraction and application of features in high-dimensional data, which degrade accuracy and efficiency in the identification of tool wear states. In this study, a Kernel Principal Component Analysis-Sparse Principal Component Analysis (KPCA-SPCA) feature is proposed, which overcomes the limitations of conventional statistical models in managing high-dimensional nonlinear data. A Deep-Kernel Gaussian Process Regression (DKGPR) method is proposed for online tool wear monitoring, which integrates long short-term memory into radial basis function, extracts critical time-dependent features, and reduces sensitivity to short-term abnormal fluctuations. The performance of the DKGPR is compared with different machine-learning-based algorithms, and results show that the proposed algorithm has higher accuracy. The average Root Mean Squared Error (RMSE) of the DKGPR model is 1.686, which is 41.8% less than that of the conventional Gaussian process regression; the KPCA-SPCA reduces RMSE by over 55% and compresses the average confidence interval width by more than 70%. The KPCA-SPCA effectively improves the identification of multi-scale features and robustness to non-stationary signals, and the DKGPR is capable of learning via small-batch data. The combination of modified data-processing and machine-learning algorithms provides a highly efficient solution for tool wear monitoring.

Graphical abstract

Keywords

tool wear monitoring / high-dimensional features / kernel principal component analysis / sparse principal component analysis / Gaussian process regression / long short-term memory

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