Abstract
Identification of the seam to be welded in automatic welding is an important challenge, especially on a complex 3D surface. At present, most studies on weld extraction are for butt joints and fillet joints. Few studies focus on D-type welds, which connect to pipelines or pressure vessels. In this paper, a method of identification and extraction of pipeline joint welds based on 3D point clouds is proposed. First, the problem that the surface of pipe fittings cannot be effectively stitched owing to lack of markings is solved by putting marker points on the surface to allow accurate splicing of the point cloud. Second, according to the contour feature information of the workpiece, a processing method for D-type welding seams is proposed. Finally, a boundary extraction algorithm based on the point cloud normal is used to obtain the point cloud of the weld. The experimental results show that this method can effectively extract the weld. The running time for the point cloud weld extraction method is less than half that of the existing methods. The accuracy of the proposed method is improved in all three directions: by 28.2% in the X direction, 19.1% in the Y direction and 16.0% in the Z direction. Moreover, this method is convenient for transforming weld coordinates to robot-based coordinates for the automatic welding of spatially complex welds.
Highlights
Welding of metals plays a vital role in industrial manufacturing
The purpose of point cloud preprocessing is to minimize the effects of noise points, outliers, holes, etc., so that high-level applications such as feature extraction, registration, surface reconstruction, and visualization can be performed better
In this article, we propose a method for extracting D-type welds, such as those used to attach connections to pressure vessels and pipelines
Summary
Welding of metals plays a vital role in industrial manufacturing. D-type welds, which are welds where the joint crosses the vessel, can have highly concentrated stress. The binocular or multiocular stereo vision systems used to collect 3D information have many disadvantages: high cost, large size, complex extraction of feature points from images, and complex matching algorithms. They require calibration between multiple cameras, which can be affected by changes in the external environment. An alternative approach is to adopt point clouds to take the depth information about the parts to be welded (i.e., the distances between the part and sensors), and process these point clouds to directly obtain the coordinate points of the welding seam.
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