Welding deviation detection method based on weld pool image contour features

Automatic on-line detection of welding deviation based on machine vision is one of the key technologies of arc welding robot tracking welding, in which obtaining high quality weld pool image and accurate welding deviation detection algorithm are two important steps of tracking welding. Through the research and analysis of the weld pool image of gas metal arc welding (GMAW), it was found that the weld pool contains abundant welding information. First, the average gray value of the weld pool image can reflect the interference degree of arc to weld pool image and the heat input of welding process. Secondly, the tip of the weld pool image contour can reflect the center of the groove gap. Finally, the horizontal distance between the center coordinate of the wire contour and the tip coordinate of the weld pool image contour can reflect the welding deviation. On the basis of analyzing the characteristics of weld pool image, this paper proposes a new method of weld seam deviation detection, which includes the collection of weld pool image, image preprocessing, contour extraction and deviation calculation. The results of the tests and analyses showed that the maximum error of the on-line welding deviation obtained was about 2 pixels (0.17 mm) when the welding speed was ≤60 cm/min, and the algorithm was stable enough to meet the requirements of real-time deviation detection for I-groove butt welding. The method can be applied to the on-line automatic welding deviation detection of arc welding robot.