Tool wear monitoring based on physics-informed Gaussian process regression

Tool Wear Monitoring (TWM) plays a vital role in safeguarding product quality and enhancing machining efficiency. TWM technology mainly includes physics-based models and data-driven methods. However, physical models established under simplified or idealized conditions struggle to capture the complexity of machining processes. Moreover, the predictive efficacy of data-driven methods is heavily contingent upon the quantity of labeled data available. Addressing these issues, a hybrid-driven physics-informed Gaussian process regression model (PIGPR) is proposed. First, a health indicator construction strategy based on feature fitness analysis and Gaussian weighted moving average filtering is proposed to eliminate interference and redundancy in the measurement signal and improve monitoring efficiency. Second, a novel explicit physical model of tool wear was developed, with a determination coefficient of at least 0.98. On this basis, health indicator and proposed priori physical models are employed to constrain the mean function of the Gaussian process regression (GPR), combining data mining and physical models to provide prediction guidance for key physical domain knowledge for the hybrid model. Third, grid search algorithm is used to optimize the model parameters, adaptively identify tool wear conditions, and 95 % prediction confidence interval is given to provide more reliability. Finally, nine sets of experiments with varying cutting settings confirmed the PIGPR model's dependability. The findings demonstrate that the suggested hybrid approach significantly enhances the prediction precision of tool wear, achieving an accuracy of 0.997. Compared to the solely data-driven GPR model, the width and variance of the 95 % confidence interval decreased by 46.44 % and 60.80 %, respectively, which demonstrates that incorporating prior physical knowledge significantly enhances the smoothness and reliability of predictions.