Integrating physics-informed recurrent Gaussian process regression into instance transfer for predicting tool wear in milling process

Effective management of tool condition is of key importance to produce precision parts with desirable structural shape and excellent surface integrity. Due to the variable cutting conditions and the limited tool wear data in practice, it is a great challenge to predict tool wear with traditional machine learning methods. To overcome the dual challenges, this study proposes a physics-informed transfer learning (PITL) framework to predict tool wear under variable working conditions. Specifically, in order to predict tool wear under new tasks with limited data, instance transfer learning algorithm Two-stage TrAdaBoost.R2 is used to improve the generalization ability of the prediction model by transferring useful knowledge from source domains. Then, to compensate for the shortcomings of applying pure data-driven model as base learner in Two-stage TrAdaBoost.R2, physics-informed recurrent Gaussian process regression (PRGPR) is utilized as base learner to improve the extrapolation performance of the model in the target domain. For PRGPR, on the one hand, the tool wear predicted in the previous step is incorporated into the input vector to address the time-accumulation effect. On the other hand, the mean function of the model is generated by deriving equations from prior degradation knowledge to guide the forecasting process. Finally, the experimental results indicate that the proposed method has favorable foresight of the degradation process and can further improve the prediction accuracy of tool wear under variable working conditions.