Point Cloud 3D Weldment Reconstruction and Welding Feature Extraction for Robotic Multi-bead Arc Weld Cladding Path Planning

Traditional manual teaching or offline programming welding modes may lead to long teaching time, low efficiency, and inability to adapt to changing welding environments when facing complex trajectory workpieces or Multi-welded workpieces. In this paper, a teaching-free welding method based on visual sensing system for robotic is proposed. Firstly, the three-dimensional information of the workpiece surface is captured by a monocular structured light camera and characterized by point cloud data. Point cloud stitching is performed on multiple local images of large-size workpieces to reconstruct the welding surface. Then, statistical filtering and deep learning methods are used to preprocess and segment the point cloud to obtain the reference points of the welding path. Finally, according to the shape characteristics of the workpiece, the auxiliary projection method is used to automatically generate the robot surfacing path. Experimental results show that under the condition of camera accuracy of ± 0.05 mm, the maximum planning path error is less than 1 mm, which meets the actual welding needs. This method is significant for achieving welding automation and improving production efficiency.