A welding quality detection method for arc welding robot based on 3D reconstruction with SFS algorithm

• Published in: International journal of advanced manufacturing technology Vol. 94; no. 1-4; pp. 1209 - 1220
• Main Authors: Yang, Lei, Li, En, Long, Teng, Fan, Junfeng, Mao, Yijian, Fang, Zaojun, Liang, Zize
• Format: Journal Article
• Language: English
• Published: London Springer London 2018; Springer Nature B.V
• Subjects: Algorithms; Arc welding; CAE) and Design; Computer-Aided Engineering (CAD; Curvature; Engineering; Evaluation; Feature extraction; Image detection; Image quality; Image reconstruction; Industrial and Production Engineering; Mechanical Engineering; Media Management; Original Article; Production management; Quality; Robots; Shading; Structural strength; Support vector machines; SFS; Feature extraction; 3D reconstruction; SVM; Welding quality
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-017-0991-9

In the modern manufacturing industry, the welding quality is one of the key factors which affect the structural strength and the comprehensive quality of the products. It is an important part to establish the standard of welding quality detection and evaluation in the process of production management. At present, the detection technologies of welding quality are mainly performed based on the 2D image features. However, due to the influence of environmental factors and illumination conditions, the welding quality detection results based on grey images are not robust. In this paper, a novel welding detection system is established based on the 3D reconstruct technology for the arc welding robot. The shape from shading (SFS) algorithm is used to reconstruct the 3D shapes of the welding seam and the curvature information is extracted as the feature vector of the welds. Furthermore, the SVM classification method is adopted to perform the evaluation task of welding quality. The experimental results show that the system can quickly and efficiently fulfill the detection task of welding quality, especially with good robustness for environmental influence cases. Meanwhile, the method proposed in this paper can well solve the weakness issues of conventional welding quality detection technologies.