IJAT Vol.13 No.6 pp. 796-802
doi: 10.20965/ijat.2019.p0796


Gap Detection Using Convolutional Neural Network and Adaptive Control in Robotic Plasma Welding

Satoshi Yamane and Kouki Matsuo

Department of Environmental Science and Technology, Saitama University
255 Shimo-okubo, Sakura-ku, Saitama-shi, Saitama 338-8570, Japan

Corresponding author

May 17, 2019
September 2, 2019
November 5, 2019
convolutional neural network, visual sensor, robotic welding, plasma arc welding, image processing

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.

Cite this article as:
S. Yamane and K. Matsuo, “Gap Detection Using Convolutional Neural Network and Adaptive Control in Robotic Plasma Welding,” Int. J. Automation Technol., Vol.13 No.6, pp. 796-802, 2019.
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