Abstract
Visual sensing is an effective means for obtaining arc-welding characteristic information, such as the position and pose of the welding torch, the shape and size of the welding groove, for closed-loop feedback control in intelligent arc welding. The paper describes a visual sensing image processing and feature extraction method for arc welding. A multi-source sensor was developed based on the fusion of visual information with the effect of gravity. The hardware and image preprocessing algorithm are optimized to reduce the interference of the strong arc light, spatter, and other effects on the CCD image. The algorithm then uses the edge extraction based on a Canny operator or the skeleton thinning algorithm based on iterative erosion. The two algorithms separately process the CCD image of the welding groove collected by the multi-source sensor to extract the laser lines, the laser line intersection coordinates and the laser line bending points coordinates caused by the welding groove. Comparison of the feature information extraction speeds and recognition accuracies of the two algorithms shows that the edge extraction algorithm based on the Canny operator can provide real-time weld seam tracking during arc welding.
