XAI-PdMNet-Bench: an explainable generative AI framework for leakage-safe predictive maintenance in industry 5.0 manufacturing

As manufacturing transitions toward Industry 5.0, the demand for trustworthy, operator-centric predictive maintenance has intensified; yet today’s CNC milling models often fall short on the very qualities that make them deployable: leakage-free evaluation, calibrated performance under imbalance, and explainability. This paper introduces XAI-PdMNet-Bench, a leakage-safe and explainable PdM framework validated on two CNC milling benchmarks: AI4I 2020 ( n=9,973 ; 3.31 % failures) and PHM 2010 (real cutting signals; tool-independent split). A 25-feature physics-informed matrix is constructed after removing all leaked indicator columns, and minority-class augmentation is performed via statistically audited CTGAN (Frobenius $$= 0.083$$ ; all KS tests passed). The proposed XGBoost achieves F1 $$= 0.991$$ and PR-AUC $$= 0.994$$ under leakage-safe cross-validation, exceeding the best leakage-unsafe baseline (F1 $$= 0.983$$ ). TabTransformer achieves F1 $$= 0.987$$ , the best deep-learning result. On PHM 2010, the proposed RCNN (Conv1D + BiLSTM) attains F1 $$= 0.921$$ and PR-AUC $$= 0.941$$ ; staged transfer learning from AI4I 2020 yields F1 $$= 0.894$$ . SHAP attribution identifies RPM × wear as the dominant fault driver, and a structured operator report supports operator-facing maintenance interpretation. Ablation studies confirm CTGAN contributes the largest gain under leakage-safe balanced cross-validation ( Δ F1 $$= +0.160$$ over no augmentation; Δ F1 $$= +0.140$$ on the imbalanced held-out set), followed by physics-informed feature engineering ( Δ F1 $$= +0.050$$ ).