Narrow gap deviation detection in Keyhole TIG welding using image processing method based on Mask-RCNN model

Abstract

In the process of K-TIG deep penetration welding, the workpiece does not need to be bevelled; therefore, the welding method is butt welding, and the gap to be welded is very narrow (0.2~1 mm). Because of the large welding current, the welding arc light intensity is very strong. These factors cause difficulties in the K-TIG weld seam tracking process. To realize weld seam tracking in the K-TIG welding process, it is necessary to extract the keyhole entrance centre and weld centreline accurately. To reduce the interference of strong arc light in the process of K-TIG welding, the information of keyhole entrance and weld seam in the process of K-TIG welding is obtained by using a high-dynamic-range camera. An image processing algorithm based on Mask-RCNN is proposed to extract the centre of the keyhole entrance accurately. An image processing algorithm based on Hough line fitting is used to accurately identify the weld centreline in the welding image and extract the welding deviation. In welding experiments, it is verified that the welding deviation extracted by the method proposed in this paper fluctuates within ± 0.133 mm, which meets the requirements of actual K-TIG welding seam tracking.