Data-Driven Offline Compensation of Robotic Welding Trajectories Using 3D Optical Metrology in Industrial Manufacturing

The geometric variability of industrial components represents a persistent challenge in robotic arc welding, particularly in high-volume manufacturing environments where parts are positioned in fixtures based on nominal CAD assumptions. Even moderate deviations in dimensions or seating conditions can lead to weld defects, rework, and reduced process capability when conventional offline programming is employed. This paper presents an applied industrial workflow for adaptive robotic welding trajectory correction that integrates full-field 3D optical metrology with a data-driven deep reinforcement learning (DRL) model. Prior to welding, each component is scanned using a structured-light 3D system, and critical geometric deviations are extracted relative to the nominal CAD model. These deviations define a compact state representation that is mapped, via a trained DRL agent, to corrective translational and rotational adjustments of the welding trajectory. Importantly, all trajectory corrections are computed offline, ensuring compatibility with standard industrial robot controllers and avoiding real-time computational overheads. The proposed approach is validated using real production data from an industrial batch of 5000 components characterized by significant dimensional variability and limited process capability. Experimental results demonstrate a reduction in welding defects exceeding 90%, elimination of rework associated with improper part positioning, and an improvement of the overall process performance to a sigma level of 5.219. The results show that combining 3D optical metrology with learning-based trajectory adaptation enables robust compensation of part-level geometric deviations without mechanical fixture modifications. The proposed method provides a practical and scalable solution for improving welding quality in manufacturing environments affected by upstream variability and imperfect part positioning.