Abstract: Welding pool geometry includes plenty of welding quality information. The observation and reconstruction of the welding pool surface is the basis of developing intelligent control system for welding process to substitute skilled welders. The binocular vision system was ameliorated to capture images of welding pool surface by suppressing the strong arc interference during gas metal arc welding (GMAW). Combining and improving the algorithms of speeded up robust features, binary robust invariant scalable keypoints and KAZE, the feature information of points (i.e. RGB value, pixel coordinates and so on) was extracted as the feature vector of the welding pool surface. Based on the characteristic of welding images, the mismatch elimination algorithm was developed to increase the accuracy of image matching algorithms. The world coordinates of matching feature points was calculated to reconstruct the 3D shape of the welding pool surface. The effectiveness and accuracy of reconstruction for welding pool surface were verified by the experimental results.
Current research of binocular vision systems mainly need to resolve the camera’s intrinsic parameters before the reconstruction of three-dimensional (3D) objects. The classical Zhang’ calibration is hardly to calculate all errors caused by perspective distortion and lens distortion. Also, the image-matching algorithm of the binocular vision system still needs to be improved to accelerate the reconstruction speed of welding pool surfaces. In this paper, a preset coordinate system was utilized for camera calibration instead of Zhang’ calibration. The binocular vision system was modified to capture images of welding pool surfaces by suppressing the strong arc interference during gas metal arc welding. Combining and improving the algorithms of speeded up robust features, binary robust invariant scalable keypoints, and KAZE, the feature information of points (i.e., RGB values, pixel coordinates) was extracted as the feature vector of the welding pool surface. Based on the characteristics of the welding images, a mismatch-elimination algorithm was developed to increase the accuracy of image-matching algorithms. The world coordinates of matching feature points were calculated to reconstruct the 3D shape of the welding pool surface. The effectiveness and accuracy of the reconstruction of welding pool surfaces were verified by experimental results. This research proposes the development of binocular vision algorithms that can reconstruct the surface of welding pools accurately to realize intelligent welding control systems in the future.
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