Additive seam tracking technology based on laser vision

• Published in: International journal of advanced manufacturing technology Vol. 116; no. 1-2; pp. 197 - 211
• Main Authors: Zhao, Zhuang, Luo, Jun, Wang, Yeyu, Bai, Lianfa, Han, Jing
• Format: Journal Article
• Language: English
• Published: London Springer London 01.09.2021; Springer Nature B.V
• Subjects: Algorithms; Automatic welding; Background noise; CAE) and Design; Computer-Aided Engineering (CAD; Engineering; Feature extraction; Fusion welding; Grooves; Industrial and Production Engineering; Industrial development; Laser beam welding; Mass production; Mechanical Engineering; Media Management; Original Article; Path planning; Seam tracking; Tracking systems; Vision; Weld tracking; High robustness; Additive welding; Strong adaptability; ERFNet
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-021-07380-0

With the development of manufacturing industry and technology, traditional manual welding technology is gradually unable to meet the need for industrial mass production in the field of fusion welding and additive manufacturing. As a result, an automatic welding method using robots to replace manual welding is needed. This paper studies the additive weld seam tracking technology based on laser vision and designs a welding seam tracking system. The images of linear structure light which reflect welding seam information are collected by vision sensor. The structure light extraction algorithm window is selected under the guidance of the over-exposure characteristics of all kinds of noises. The ERFNet network is applied for the structure light and its corresponding feature point extraction. The accurate center line of structure light is extracted from strong background noise and the feature point of weld seam is obtained through regression. It realizes the online path planning and deviation correction of the weld seam tracking in real-time. The proposed algorithm is demonstrated by the weld feature extraction experiment and welding seam tracking experiment based on groove additive task. It shows that the offset is within one pixel and the distance is within 0.25 mm between the welding feature points extracted by ERFNet and the manually marked welding points. The proposed algorithm has the performance of high robustness, strong adaptability and can meet the practical welding requirements.