Computer vision technology for seam tracking in robotic GTAW and GMAW. (April 2015)

• Record Type: Journal Article
• Authors: Xu, Yanling; Fang, Gu; Lv, Na; Chen, Shanben; Jia Zou, Ju
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 32(2015)
• Page Start: 25
• Page End: 36
• Publication Date: 2015-04
• Subjects: Robot welding -- Vision technology -- Seam tracking -- Image processing -- GTAW/GMAW; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2014.09.002
• Languages: English
• ISSNs: 0736-5845

Abstract

Abstract: Due to ever increasing demand in precision in robotic welding automation and its inherent technical difficulties, seam tracking has become the research hotspot. This paper introduces the research in application of computer vision technology for real-time seam tracking in robotic gas tungsten arc welding (GTAW) and gas metal arc welding (GMAW). The key aspect in using vision techniques to track welding seams is to acquire clear real-time weld images and to process them accurately. This is directly related to the precision of seam tracking. In order to further improve the accuracy of seam tracking, in this paper, a set of special vision system has been designed firstly, which can acquire clear and steady real-time weld images. By analyzing the features of weld images, a new and improved edge detection algorithm was proposed to detect the edges in weld images, and more accurately extract the seam and pool characteristic parameters. The image processing precision was verified through the experiments. Results showed that the precision of this vision based tracking technology can be controlled to be within ±0.17 mm and ±0.3 mm in robotic GTAW and GMAW, respectively. Highlights: Using computer vision technology, we have solved some problems of seam tracking in robotic welding automation. We have designed a set of special vision sensor system for seam tracking and it has worked very well. A new improved Canny edge detection algorithm was proposed to detect the edges of weld image. We have further improved the precision of image processing in seam tracking. This paper systematically studied the application of computer vision technology in robotic GTAW and GMAW.