A vision-based algorithm for seam detection in a PAW process for large-diameter stainless steel pipes

Abstract

Manual monitoring and seam tracking through welding images in real-time, by naked eye or industrial TV are experience-dependant, subjective, labor-intensive, and sometimes biased. So it is necessary to realize computer-aided seam tracking. We have developed a plasma arc welding (PAW) seam-tracking system, which senses the molten and the seam in a frame using a vision sensor, and then detects the seam deviation to adjust the work piece motion adaptively to the seam position sensed by the vision sensor with our collaborator. In this paper, we propose a novel molten pool area image-processing algorithm based on machine vision. The algorithm processes each image at a speed of 20 frames/s in real-time to extract three feature variables to get the seam deviation. It is proved experimentally that the algorithm is very fast and effective. Issues related to the algorithm are also discussed.