Visual sensing is a key technology for online detection of welding groove size parameters, and welding torch positions and postures in intelligent welding systems. Accurate visual sensing requires accurate calibration of the internal visual sensor parameters. This paper describes an integrated calibration method for the internal parameters of a visual sensor based on combined laser structured lights. The method is based on an ordinary checkerboard calibration board with the calibration system extracting the centerline of the laser line in the image using the skeleton thinning method and Hough line detection. The system then determines the three-dimensional coordinates in the camera coordinate system of the points on the laser center line of the calibration board to fit the parameters in the laser structured light plane equation. This integrated calibration method improves the calibration accuracy, efficiency and convenience. Tests measuring the welding groove size on a flat workpiece gave mean and repeated measurement errors of the welding groove size of not more than 0.04 mm, which verifies that the sensor calibration accuracy meets the needs for welding groove size measurements.

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.