Abstract
The paper addresses the imperative shift towards automation in welding processes, leveraging advanced technologies such as industrial robotic systems. Focusing on the reconstruction and classification of weld joints, it introduces a methodology for automatic trajectory determination. Utilizing a laser profilometer mounted on the robot, weld joints are reconstructed in three dimensions, and spurious data is filtered out through signal processing. A classification algorithm, integrating signal processing and artificial intelligence, accurately categorizes joint profiles, including V-joints and single bevel T-joints. The proposed intelligent and adaptive system enhances welding automation by analyzing point cloud data from laser scanning to optimize welding trajectories. This study establishes a foundational framework for further refinement and broader application in welding automation. Key Points•Introduction of a methodology for automated trajectory determination in welding processes.•Utilization of laser scanning and signal processing for reconstruction and classification of weld joints.•Implementation of an intelligent and adaptive system to optimize welding trajectories.