Artificial Intelligence for Tool Wear Prediction Under Multiple Cooling Strategies in the Turning of Stainless Steel—AISI 304

High-speed turning of AISI 304 stainless steel is limited by rapid tool wear driven by thermal accumulation and tribological instability. This study compares five cooling/lubrication strategies (dry, flood cooling, MQL, internally cooled tools—ICT, and ICT + MQL) under a fixed severe cutting regime (Vc = 400 m/min, f = 0.1 mm/rev, ap = 0.2 mm) and develops a low-complexity tool end-of-life predictor using cutting power as the sole monitoring signal. Dry machining produced the highest cutting forces 26.7 N), whereas lubricated/cooled conditions showed statistically similar force levels (≈11 6 – 118 N). Cutting force and derived power increased monotonically with wear, supporting power as an indirect tool-state indicator. A binary XGBoost classifier trained on statistical and trend descriptors of one-second power windows achieved accuracies of 96.5% (training), 95.9% (test), and 93.3% (validation) with AUC–ROC values of 0.988, 0.993, and 0.959, respectively, despite moderate class imbalance (≈85 % healthy/15% worn). SHAP analysis identified average power and distributional descriptors (skewness and amplitude ratios) as dominant predictors, providing interpretable links between signal statistics and wear progression. The results demonstrate that reliable end-of-life detection can be achieved using a single energetic signal across heterogeneous cooling environments, supporting scalable monitoring compatible with low-fluid and closed-loop cooling strategies.