Vision-based welding seam tracking is an important and unique branch of welding automation. Active vision seam tracking systems achieve accurate feature extraction by using an auxiliary light source, but this will introduce extra costs and the real-time performance will be affected. In contrast, passive vision systems achieve better real-time performance and their structure is relatively simple. This paper proposes a passive vision welding seam tracking system in Plasma Arc Welding (PAW) based on semantic segmentation. The BiseNetV2 network is adopted in this paper and online hard example mining (OHEM) is used to improve the segmentation effect. This network structure is a lightweight structure allowing effective image feature extraction. According to the segmentation results, the offset between the welding seam and the welding torch can be calculated. The results of the experiments show that the proposed method can achieve 57 FPS and the average error of the offset calculation is within 0.07 mm, meaning it can be used for real-time seam tracking.

Butt welding is an important link to ensure welding quality, and the penetration state of the weld is the main criterion to achieve this. Online monitoring and control of the penetration state of welded joints is an important measure to ensure welding quality. The molten pool image is monitored by a visual sensor in the gas metal arc welding (GMAW) process, and the bottom molten pool width is predicted by the regression network model. Combined with the real-time control method, the welding current is changed to monitor and control the bottom weld width in real time. Butt-welding experiments with different groove angles verified that the proposed method could achieve satisfactory control accuracy and generalization ability. For butt-welding experiments with constant groove angles of 30° and 45°, the MAE of the controlled backside melt width to the target values was 0.2603 mm and 0.2620 mm. Therefore, it provides a feasible method for the online control of weld penetration.