Intelligent Weld Seam Error Correction Using 3D Visual Localization and Machine Learning
Jinbo Li
Journal of Multimedia Processing and Technologies, Vol. 16, No. 4, 2024.
DOI: https://doi.org/10.6025/jmpt/2025/16/4/193-201
Link: https://www.dline.info/jmpt/fulltext/v16n4/jmptv16n4_3.pdf
Abstract:
The paper presents a method for correcting weld seam errors in intelligent robotic welding systems using three-dimensional (3D) visual localization technology. Authored by Jinbo Li from Henan Vocational College of Logistics, the study addresses limitations in conventional robotic welding, particularly the inability to detect and correct seam deviations in real-time. The proposed approach integrates 3D visual sensing (via laser scanning or structured light) with machine learning algorithms, such as Support Vector Machines and deep neural networks, to accurately identify and classify weld seam geometry from point cloud data. Key features like curvature, normal vectors, elevation statistics, and spatial density are extracted to enhance localization precision. The system enables real-time error correction by comparing actual seam positions with predefined paths, adjusting the robotâ€™s trajectory accordingly. Experimental results demonstrate high accuracy, with a relative error of only 0.38% in distance measurement and endpoint positioning errors ranging from 2.5 to 4 mm, meeting industrial standards for crane beam welding. The paper validates the effectiveness of the algorithm through multiple tests, including Canny edge detection and dual-pixel scanning for depth analysis. The authors conclude that combining 3D visual localization with intelligent algorithms significantly improves welding quality, reduces defects, and enhances the adaptability and efficiency of robotic welding systems. Future work aims to refine classification models and further integrate deep learning for more robust seam tracking and correction in complex industrial environments.