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

Accurate identification of weld regions is a critical step in weld quality inspection and automated grinding operations. However, weld point cloud data is typically characterized by high irregularity and lack of topological structure, making its efficient processing and accurate recognition a persistent research challenge. In recent years, advanced machine learning methods have shown significant potential for weld identification, yet research on 3D vision-based recognition of three-dimensional weld point clouds remains in its early stages. This study focuses on three typical weld seam trajectories—linear, curved, and S-shaped—conducting classification and recognition experiments based on point cloud data. Using Overall Accuracy, Precision, Recall, and F1-score as evaluation metrics, a systematic comparison was performed on the classification performance of three models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)—across different neighborhood scales. Experimental results indicate that LightGBM achieved optimal classification performance within a 1.5mm neighborhood range. To further enhance the classification accuracy, this study introduced the Artificial Lemming Algorithm (LAL), Alpha Evolution Algorithm (AE), and Starfish Optimization Algorithm (SFOA) to improve the LightGBM model. The optimized LAL-LightGBM, AE-LightGBM, and SFOA-LightGBM models attained accuracy rates on linear, curved, and S-shaped test sets of (98.02%, 96.07%, 93.87%), (98.83%, 96.36%, 94.18%), and (98.00%, 96.12%, 93.91%), respectively. Comprehensive results demonstrate that the AE-LightGBM model performed best in overall evaluation, verifying the effectiveness of intelligent optimization algorithms in improving classification model performance. Furthermore, this study employed the SHAP interpretability framework to analyze the feature contributions of the AE-LightGBM model, effectively revealing its decision-making mechanism and enhancing model interpretability. This research provides a feasible technical pathway for robot-based weld grinding recognition tasks utilizing 3D vision and supervised machine learning.