As the basic visual morphological characteristics of molten pool, contour extraction plays an important role in on-line monitoring of welding quality. The limitations of traditional edge detection algorithms make deep learning play a more important role in the task of target segmentation. In this paper, a molten pool visual sensing system in a tungsten inert gas welding (TIG) process environment is established and the corresponding molten pool image data set is made. Based on a residual network, a multi-scale feature fusion semantic segmentation network Res-Seg is designed. In order to further improve the generalization ability of the network model, this paper uses deep convolutional generative adversarial networks (DCGAN) to supplement the molten pool data set, then performs color and morphological data enhancement before network training. By comparing with other traditional edge detection algorithms and semantic segmentation network, it is verified that the scheme has high accuracy and robustness in the actual welding environment. Moreover, a back propagation (BP) neural network is used to predict the weld width, and a fitting test is carried out for the pixel width of the molten pool and its corresponding actual weld width. The average testing error is less than 0.2 mm, which meets the welding accuracy requirements.

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.