Sci Rep 2026 May 28; doi: 10.1038/s41598-026-54597-8.

A weld point cloud recognition method based on an improved Light Gradient Boosting Machine.

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Accurate weld-region identification is essential for weld quality inspection and automated grinding. However, weld point clouds are highly irregular and lack explicit topological structure, which makes accurate recognition challenging. To address this issue, this study formulates weld point-cloud recognition as a binary point-wise classification task. Each point is classified as either weld bead or base metal. A systematic classification framework is established by combining neighborhood-based geometric feature extraction, baseline model comparison, and metaheuristic hyperparameter optimization. Three morphology-specific weld subsets, including straight-line, curved-line, and S-shaped welds, are used for evaluation. The classification performance of Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) is first compared under different neighborhood scales. Overall Accuracy (OA), Precision, Recall, and F1-Score are used as evaluation metrics. The results show that LightGBM achieves the best baseline performance at a neighborhood radius of 1.5 mm. To further improve classification performance, LightGBM hyperparameters are optimized using metaheuristic algorithms. The compared optimizers include the Artificial Lemming Algorithm (ALA), Alpha Evolution Algorithm (AE), and Starfish Optimization Algorithm (SFOA). Repeated-run results demonstrate that AE-LightGBM achieves the most favorable overall performance under the unified evaluation protocol. Statistical significance analysis and convergence analysis further support the effectiveness of AE among the compared optimizers. In addition, SHapley Additive exPlanations (SHAP) is employed to analyze feature contributions and improve the interpretability of the optimized model. The proposed method provides an effective technical pathway for robot-based weld recognition and grinding tasks using 3D vision and supervised machine learning.

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