###### Abstract
Additionally, the correlation between the keyhole length, width, and area with the main welding process parameters was visualized through vision-based measurements. It could obtain better welding quality with visual sensors during the welding controlling process. The development of deep learning in image processing has made remarkable progress, and it has been widely applied in computer vision, robotics, autonomous driving, and medical imaging in recent years [15; 16; 17]. The effectiveness of the convolutional neural network (CNN) was subsequently validated through adaptive control welding experiments. [PERSON] et al. [18] developed mathematical models using deep learning techniques to establish the relationship between topside image features and the status of the weld pool. Through their research, they successfully achieved prediction of the penetration and keyhole status based on these models. For the weld pool image, it is not possible to distinguish the weld boundary due to the grayscale of the image, which makes it impossible to control the weld. [PERSON] et al. [19] proposed an image processing method for automatic tracking of the weld line developed in cases where there is or is not a gap by using template matching. However, the weld line cannot be detected because the brightness values are unstable at the boundary between the unmolten and molten portions. U-Net is a pixel-by-pixel object recognition system based on semantic segmentation, a method that categorizes each pixel based on its meaning (information from surrounding pixels) [20]. U-Net segmentation can be predicted for each pixel to obtain higher segmentation accuracy. Due to the special network structure of U-Net, it is possible to output images with more features. [PERSON] et al. [21] proposed an automated approach using the Point-REND Res-U-Net model to improve the accuracy of detecting welding defects in welding inspection to improve model dice on X-ray data by iteratively refining coarse segmentation results. A new penetration status diagnosis strategy using U-Net for image segmentation and a lightweight CNN model for prediction real-time monitoring of laser welding [22]. In our study, the authors have chosen the weld pool boundary as the input for the U-Net network rather than the shape of the weld pool. Because the most distinct feature of welding deviation is reflected in the boundary of the weld pool during the plasma welding process, it could prevent misrecognition and measure with high accuracy to obtain weld boundaries. The process of weld seam tracking during the welding process is a challenging task due to the dynamic changes in the weld pool morphology and the effects of external perturbations. Conventional weld tracking methods frequently prove incapable of coping with these complex problems, as they typically rely on simple image processing techniques such as edge detection and threshold segmentation. The presence of impurities can lead to misidentification, resulting in poor accuracy and robustness. In recent years, significant advancements have been made in the field of image segmentation and target detection through the utilization of deep learning methods. Among these advancements, the U-Net algorithm has emerged as a promising solution for weld tracking tasks, owing to its advanced image segmentation capabilities. In this study, the authors employed U-Net to predict weld pool boundaries. The welding line was obtained through image processing. In addition, a control system was designed to conduct seam tracking experiments, and the results obtained were successful. It proves the effectiveness of this prediction method for seam tracking and improves the welding quality. This study will hold significant importance in the field of PAW in analyzing the welding process, improving welding quality, and accelerating the integration of welding technologies and artificial intelligence.