Plasma arc welding (PAW) was employed in joining thick materials with groove. Due to the high-density plasma arc, keyhole welding was used in the butt welding. The gap might be taken in places due to the heat distortion during the welding. To achieve a high-quality welding, the adaptive control is required according to the gap. The authors tried to apply CMOS camera to obtain information from the top surface and achieve synchronization between the camera shutter and welding current. Thus, a clear image of the weld pool with the keyhole and the gap was taken. The brightness distribution in the front of the weld pool becomes uneven due to the plasma arc. To classify the gap and select the welding conditions, such as the welding current and the plasma gas flow rate, the authors used one of the deep learning algorithms, convolutional neural network (CNN). In the training and testing data, the performance of the CNN was found to be satisfactory. However, if the plasma torch does not trace the welding line, the pool image will be different from the training image. Hence, the CNN will not be able to estimate the correct gap. To avoid this, the authors detected the welding line from the pool image and selected the image area near the welding line as the input of the CNN, i.e., the input area is selected based on the image obtained during the welding. The validity of the proposed method was verified by the welding experiments.

Welding is an essential technology for joining metal plates. In general, gas metal arc welding (GMAW) generates a large amount of fumes in the welding of thick metal plates. In contrast, the butt joining of thick metal plates can be achieved using plasma arc welding (PAW) with a lower amount of fumes. Further, the improvement of the welding environment is critical in welding. In particular, if there are gaps between the base metals, the welding conditions are adjusted based on the gap. A visual sensor, such as a complementary metal-oxide-semiconductor (CMOS) camera, is useful for observing the welding situation. In this study, such a camera was attached to a plasma torch. During welding, we obtained weld pool images using the camera and detected the gaps by processing the images. As the arc light is very intense, it is difficult to obtain a clear image of the weld pool in PAW. In conventional welding, a constant current is used; however, pulsed welding current is used herein to obtain a clear image. The frequency of the current is 20 Hz, which indicates that the interval time is 50 ms. Moreover, the welding current was reduced to 30 A to minimize the effect of the intense arc light while the shutter of the CMOS camera was opened. The exposure time of the CMOS camera is 1 ms. Furthermore, gaps can be detected through image processing. It is necessary to identify the base metals with or without a gap. It was observed that the gap is darker than the solid area of the base metal. Moreover, a gap can be detected through the binarization method. The center area is not dark in the image of the weld pool without the gap. As the image of the weld pool is uneven without a gap, the binarization method can provide a detection result with some errors. Hence, it is challenging to identify whether there is a gap. A convolutional neural network (CNN) is useful for analyzing images. Thus, we applied a CNN to the weld pool image. If the gap is identified using the CNN, the binarization method is used to obtain the gap width. Hence, in PAW, welding conditions are adjusted based on the gap.

No abstract

No abstract