## 3 The Results of Experiments
### Seam Tracking  
This experiment utilizes the U-Net deep neural network to learn semantic segmentation and prediction of weld boundaries. It obtains the weld line by image processing, which enables stable weld seam tracking control to ensure welding quality control. Through the designed experiment, an evaluation is conducted on the established deep learning model, image processing algorithms, and welding control system. The experiment flowchart is shown in Figure 16. In the control system, when the welding starts, the CMOS camera captures the original images. Secondly, the region of interest is cropped and inputted into the model that will be trained. By using the prediction image with U-Net, image processing calculates the weld line position. To prevent the stability of the controller, the values obtained are averaged with the weld center positions obtained from the last two photos and the values are transmitted to the PLC to adjust the weld line by control system. If the average value is equal to the set reference value, the current state is maintained until the end of the weld. In this experiment, the relative positions of the base material and the camera were set to align the center 640 pixels with the weld line in the original image from the camera. Indeed, the weld tracking control process can be considered to be successful when the positions of the weld lines identified through image processing coincide with the desired positions. In this experiment, the groove center was the start position, and the end position was 3 mm away from the groove center. A real-time control program was built with Halcon and Python to control weld imitation. Figure 15: The initial response of the plate moving axis to a pulse under control. This system utilizes a camera to grab weld pool images during the welding process, which are transmitted to the PC via the GigE connection. The boundary line is measured in real time from the results of the model outputs in U-Net. Halcon processes the weld line value and transmits it to the PLC via RS232C. By using Equation (3), the deviation e[i] is calculated between the weld line and the reference positions Rt. At the same time, the pulse value of the position calculated by Equation (7) in the PLC is transmitted to the servo motor, and the servo motor can move in real-time control is performed. The vertical direction of 640 pixels (Ref 0 mm) was determined for the desired positions of the weld line position Rt in this experiment. One millimeter is equivalent to 78 pixels in the original images. The welding process began after a 4 s stop for plasma arc generation, and the seam tracking started about 10 s after the keyhole formed in a steady state. Image processing was applied to determine how the welding robot was moved, and the welding line was tracked in this experiment. Specifically, the weld line was set up in the correct position before starting the experiment. In this experiment, the boundaries of the weld pool are identified and verified by using the U-Net network, and the weld line is identified by image processing. It evaluated the training models and image processing techniques through a designed control welding system. One pass welding was performed on the base material of 30 cm in length. The experimental results were analyzed using the deviation values obtained from the image pixels. The result of deviation from the weld line with control is shown in Figure 17. The systems are without control until the keyhole weld pool stabilizes, and after approximately 10 s, the weld line is detected through prediction and image processing. In the beginning, the weld line deviates from the weld seam. Subsequently, control of the arc torch near the weld line reference position is initiated, and the control process proceeds. When the weld line is detected through prediction and image processing, the weld line deviates from the weld seam. offset exceeds the reference value of 0 during the control process, the PLC will control the servo motor to keep moving in that direction.