## 5 Conclusions  
This paper utilizes the U-Net method in image segmentation to predict the weld pool boundary region in real time and employs image processing to determine the weld line within the boundary area. In addition, a digital control welding system was proposed and applied to track the weld center in real time using process images from the boundary line. In this study, the authors developed a pixel semantic segmentation network U-Net to determine the weld pool boundary areas. They employed the weld line imaging processing based on the result of U-Net segmentation. An adaptive controller was designed for seam tracking in real time. The detailed findings are outlined below:  
1. The welding current was synchronized with the camera shutter timing to minimize the impact of arc light, resulting in clear images of the molten area. Weld pool images were captured from the top side of the base material to obtain welding information. Additionally, a welding system was developed by utilizing a PLC to control the PAW power source and the torch movement.
2. Deep learning methods were employed to predict the weld pool boundary areas. The U-Net semantic segmentation method was utilized to predict and visualize the welding boundaries on the weld line, as well as any deviations from the weld line. In this study, the model that trained has prediction achieved a recognition accuracy of 99.5% for the training data and 96.5% for the test data. This model obtained a relatively high accuracy prediction.
3. Seam tracking was conducted on a V-groove plate where the weld line had shifted by 3 mm at the end of welding. The deviation value changes and gradually becomes smaller within a deviation of 1 mm in the controlling process, which is the deviation within industrial tolerance. The system successfully demonstrated its effectiveness in PAW seam tracking during the experiment. Based on this study, developing a higher precision welding system is feasible.  
In future work, the refinement of the U-Net model and improving its robustness will be a focus. While this approach can be applied to most industrial steels, its effectiveness in specific welding materials remains to be investigated. Adapting the proposed U-Net-based segmentation and seam tracking system to other welding techniques (e.g., TIG or laser welding) would validate its robustness and versatility under different heat input and process conditions--it could be integrated into smart factory environments to enhance production efficiency and adaptability to the broader goals of Industry 4.0.  
Supervision by [PERSON] and [PERSON]; Material preparation: [PERSON] and Y.X.; First draft: J.L.; Experiments guide: S.Y.; Data collection [PERSON] and [PERSON]; [PERSON] and [PERSON] are co-first authors of the article, and they contribute equally to the article. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the National Natural Science Foundation of China (No. 62303229), Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 23 KJB460024), China Postdoctoral Science Foundation (No. 2021M701724), and Japan Society for the Promotion of Science (No. 19K05076).