A real-time seam tracking system based on extraction and restoration model in robotic GMAW

Seam tracking technology is an important part of the intelligent welding field. In this paper, a laser vision-based real-time seam tracking system was built. The system consists of a self-developed laser vision sensor, a six-axis robot, a gas metal arc welding system, and an industrial computer. After building the system, the system calibration was performed. During the seam tracking, the arc light, spatter, and other welding noise have a negative impact on the image processing algorithm to extract the weld feature points, and even lead to system drift and algorithm failure. To this end, a two-stage extraction and restoration model (ERM) was proposed for processing real-time welding images to improve the robustness and accuracy of the seam tracking system. In the ERM, the region of interest was first detected and extracted by the YOLOv5s model, then the extracted images were restored by the conditional generation adversarial network. After using the ERM model, a series of image processing was performed to obtain the coordinates of the weld feature points. The total time consumed by the algorithm is 37 ms per frame on average, which meets the real-time requirement. Moreover, the experimental results show that the seam tracking system based on the ERM can achieve real-time tracking for different types of planar V-bevel welds, and the average error is 0.21 mm, which meets the requirements for actual welding.