Accurate and interpretable weld-quality assessment is essential for ensuring the reliability of resistance spot welding in industrial production. This study develops a data-efficient classification framework that integrates dual-interval mean discretization (DIMD) of dynamic-resistance signals with gradient-boosting models. The proposed DIMD method applies fine discretization during the rapid heating–melting and coarse discretization during the subsequent slow-evolving period, effectively preserving the peak–valley morphology of resistance curves while reducing feature dimensionality. Using these compact features, XGBoost and CatBoost classifiers were trained on a dataset of DC01 low-carbon steel, covering five weld conditions. CatBoost achieved the highest accuracy of 98.9%, attributed to its ordered-boosting mechanism and symmetric-tree structure. Validation on an independent 198-sample dataset confirmed the generalization capability of the proposed approach. SHapley Additive exPlanations (SHAP)-based interpretability analysis further revealed that resistance-peak characteristics and energy-related descriptors dominate model decisions, aligning with the physical process of nugget formation and expulsion. Experimental results demonstrate that the DIMD–CatBoost framework provides a physically consistent, interpretable, and high-accuracy solution for intelligent weld-quality inspection.
Gao et al. (2026) studied this question.