Weld deviation detection based on wide dynamic range vision sensor in MAG welding process

Abstract

Weld deviation detection is a precondition for welding automation. Capturing high-quality welding images and extracting deviation information using image processing methods are two steps for weld deviation detection. In this paper, based on the analysis of the imaging characteristics during metal active gas (MAG) welding process, real-time welding images are acquired clearly and steadily using a wide dynamic range vision sensor. According to the connection relationship between the top of the arc and the end of the wire during MAG welding process, a method for determining the wire centreline is proposed. After extracting the precise arc region, the region of interest (ROI) is segmented along the welding direction. To detect the two edges of the V-groove, an improved Canny algorithm is developed. Furthermore, both the Hough Transform and a screening method based on prior knowledge are used to connect V-groove edges. The V-groove centreline is then determined. As a result, the weld deviation between the wire centreline and the V-groove centreline is calculated. Further experiments showed that the precision range of deviation detection can be controlled within ±0.25 mm, which can meet the requirement of real-time welding. This deviation can be used as the input variable for a welding robot, thus laying the foundation for MAG welding automation.