AI-Driven Tool Wear Prediction Under Severe Data Scarcity with SHAP-Guided Feature Selection and Fold-Safe Augmentation: A Case Study of Titanium Microdrilling

Microdrilling of titanium alloys suffers from rapid tool wear that degrades surface quality and dimensional accuracy, while industrial datasets are often too small for conventional data-hungry models. This work proposes a general, AI-driven modelling framework for tool wear prediction under severe data scarcity, which is validated using a titanium microdrilling case study. The study focuses on maximum flank-wear prediction (VBmax) using 18 experimental observations (VBmax = 4–13 µm). Three regression models—support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost)—were benchmarked under multiple validation protocols, with leave-one-out cross-validation (LOOCV) used as the primary assessment due to the limited sample size. To improve reliability and transparency, feature selection was performed using SHapley Additive exPlanations (SHAP), yielding a compact, interpretable feature subset dominated by thrust-force descriptors. Robustness was further evaluated using hyperparameter tuning and a conservative, leakage-controlled (“fold-safe”) augmentation strategy applied strictly within training folds. After tuning and fold-safe augmentation, XGBoost achieved the best LOOCV performance (R2 = 0.89, MSE = 0.70 µm2, MAPE = 7.62%). External validation on two additional tools under identical cutting conditions using a frozen model configuration showed bounded prediction errors under geometry and coating shifts. Overall, the results indicate that combining systematic benchmarking, SHAP-guided explainable feature selection, and leakage-controlled augmentation can enable accurate and interpretable VBmax prediction in the investigated titanium microdrilling case study, while broader validation across additional tools and cutting conditions is required to confirm generalization.