【摘要】 在中厚板氩弧焊自动化系统中，对焊缝几何特征的精确、稳定感知是实现高质量焊接的关键。针对传统方法在复杂工况下特征适应性差、泛化能力弱、鲁棒性低的问题，提出一种融合投影极差与能量约束的两阶段特征提取算法。该方法首先构造投影极差指标，并结合自适应阈值分割，实现特征点的鲁棒粗提取；进而构建融合法向引力与排斥势能的优化模型，对特征点进行位置精优化与分布均匀化；最后通过局部法向量统计准确判别焊缝谷点。实验结果表明，该算法在中厚板典型焊缝上的平均提取误差小于0.35 mm,F1分数均高于0.83，显著提升了对不同焊缝类型的适应能力与抗干扰性，为智能化焊接提供了稳定可靠的特征感知方案。更多还原

【Abstract】 In automated TIG welding systems for medium-thick plate, accurate and stable perception of weld seam geometry is critical for achieving high-quality welds. To address the limitations of conventional methods, such as poor feature adaptability, weak generalization capability, and low robustness in complex working conditions, this paper proposes a two-stage feature extraction algorithm integrating projection range and energy constraints. The method begins by constructing a projection range metric combined with adaptive threshold segmentation to achieve robust initial extraction of feature points. Subsequently, an optimization model incorporating normal attraction and repulsive potentials is developed to refine the positions of the feature points and improve distribution homogenization. Finally, a local normal vector statistics-based approach is employed to accurately identify weld valley points. Experimental results demonstrate that the proposed algorithm achieves an average extraction accuracy of less than 0.35 mm and F1-scores above 0.83 on typical medium-thick plate weld seams. It significantly enhances adaptability to different weld seam types and antiinterference capability, providing a stable and reliable feature perception solution for intelligent welding systems.更多还原