Seam tracking system based on laser vision and CGAN for robotic multi-layer and multi-pass MAG welding. (November 2022)

• Record Type: Journal Article
• Authors: Liu, Chenfan; Shen, Junqi; Hu, Shengsun; Wu, Dingyong; Zhang, Chao; Yang, Hui
• Is Part Of: Engineering applications of artificial intelligence. Volume 116(2022)
• Publication Date: 2022-11
• Subjects: Seam tracking -- Laser vision -- CGAN -- Robotic welding -- Multi-layer and multi-pass -- MAG; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105377
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: A robotic seam tracking system based on laser vision and conditional generative adversarial networks (CGAN) was proposed to address the problem of low welding precision for the multi-layer and multi-pass MAG welding process. The seam tracking system consisted of three modules, i.e., laser-vision (LV), server-terminal (ST), and robot-terminal (RT), and the real-time seam tracking for multi-layer and multi-pass welding was realized though the seam feature points extraction, coordinate conversion, deviation calculation and welding torch position correction based on the KUKA robot sensor interface (RSI). Experimental results showed that the proposed restoration and extraction network (REN) based on the CGAN principle could not only restore the seam feature information but also extract the seam feature points accurately. The welding torch could run smoothly in the strong noise environment, and there were no obvious correction marks in the weld appearance. The average correction error was less than 0.6 mm, and the adjustment process of the welding torch position can be completed within 1 s, indicating that the accuracy and speed of the proposed seam tracking system were acceptable. Highlights: A seam tracking system based on laser vision and CGAN for robotic multi-layer and multi-pass welding is proposed. The proposed restoration and extraction network can extract the seam characteristic points accurately. The accuracy and speed of the proposed seam tracking system are acceptable for welding situation with strong noise.