A Method of Feature Extraction of Position Detection and Weld Gap for GMAW Seam Tracking System of Fillet Weld With Variable Gaps

Abstract

Seam-tracking technology based on vision sensing is the key technology to realize robot intelligent welding. Because of changes in the weld gap, the diversity of workpiece material and surface conditions, and the interference of welding noises, it is extremely difficult for the robot to realize real-time seam-tracking in gas metal arc welding (GMAW) of large structures. So, a method of position detection and weld gap feature extraction is proposed for GMAW seam-tracking system of fillet weld with variable gap. This method established a highly robust weld feature extraction model based on a low-cost structured-light vision sensor. In this model, using the reflection characteristic of the stripes on fillet weld, a feature extraction method for variable gap weld based on column gray difference operator was proposed. To overcome the interference of welding noises, this model adopted the AND logic operation method between adjacent sampled images. To overcome the interference of uneven and strong reflection conditions, an optimized random sampling consistency (RANSAC) algorithm was proposed and used for weld feature extraction. The optimized RANSAC algorithm used the linear slope of the stripes to optimize the random sampling process of the algorithm. The experimental results showed that the average detection errors of the proposed method in the Y-axis and Z-axis directions are 0.19 mm and 0.23 mm, respectively, at a welding speed of 1000 mm/min. The proposed method could realize seam tracking of the variable gap fillet weld and still had good robustness under the uneven and strong reflection of the workpiece surfaces.