###### Abstract  
Plasma arc welding can achieve high-quality welding joints in high-strength manufacturing fields, such as aviation and automotive, and improve production efficiency. It is important to observe the weld pool state in real-time in robot automatic welding. However, the electrodes of the plasma welding torch cannot be observed from the outside. Teaching the weld line to torch in real-time to be observable to humans will be difficult. Also, it is difficult to process the image to obtain the position of the weld line in K-PAW. In this study, a camera was utilized to observe the weld pool. The authors estimate the weld line position in real time by image processing based on U-Net prediction. The U-Net model demonstrates sufficient prediction where the accuracy reached 99.5% for the training data and 96.5% for the test data recognition. Moreover, a control method utilized weld line position estimated from the boundary area to verify the effectiveness of this prediction model from 3 mm within the deviation of 1 mm, which is within the range of permissible welding errors. It could reduce image processing errors in the weld pool image and provide higher recognition accuracy than image processing. Combining vision sensing technologies and deep learning methods will provide new technologies to enable higher welding precision and improve welding quality. It could also accelerate the development of welding technology in the intelligent manufacturing field. plasma arc welding; U-Net; image processing; adaptive control; deep learning +
Footnote †: journal: Applied sciences  
0000-0001-9900-0001-9900-0001-9900-9900-9900-9900-9900-9900-9900-9900-9900-9900-9900-9900-9900-9900-9900-99000-9900-99000-9900-99000-9000-99000-99000-9000-99000-99000-90000-99000-99000-90000-90000-90000-90000-90 during K-PAW can detect the keyhole state under the keyhole control process. [PERSON] et al. [6] investigated the effect of the flow of molten metal on the weld pool surface and the weld pool and keyhole size in PAW by ultrasonic vibration exerted on the tungsten electrode. An algorithm was developed to calibrate and integrate the weld pool and keyhole images from different directions into one image with two cameras. To examine the correlation between the keyhole exit deviation and the process parameters, a vision sensor is used to observe the keyhole exit at the back side of the workpieces with medium thickness. A new control system designed to monitor the efflux plasma voltage condition during PAW can reflect the back side's weld width and keyhole state with the development of an ultrasonic-assisted plasma arc welding (U-PAW) process to enhance penetrability [7]. [PERSON] et al. [8] developed a pulsed plasma gas variable polarity PAW process that showed its potential to enhance welding stability, quality, and fluidity of the weld pool, thus reducing the porosity of aluminum keyhole welding. [PERSON] et al. [9] studied a process of pulsed power ultrasonic vibration-assisted plasma arc welding (PU-PAW) that enhances the keyhole capability of the plasma arc for medium-thick plate structures, ensuring the open keyhole establishment and improved penetrating ability through plasma arc constriction and increased plasma arc pressure. Visual sensing has become a standard method for observing the welding process [10; 11; 12]. [PERSON] et al. [13] utilized a dual-camera setup to monitor the entire upper weld pool from a lateral side while simultaneously capturing the keyhole entrance from the rear view in PAW. [PERSON] et al. [14] devised a vision system by incorporating a charge-coupled device (CCD) camera equipped with a custom-designed filter. This system was employed to capture images of the weld pool and keyhole during the PAW process. A vision system was implemented to capture real-time images of the keyhole from the underside of the workpiece, allowing for the measurement and monitoring of dynamic variations in keyhole geometry.