Model-enabled robotic machining framework for repairing paint film defects

Abstract

Region-based robotic machining is considered an effective strategy for automatically repairing paint film defects compared to conventional global machining. However, this process faces challenges due to irregularities in defect position, shape, and size. To overcome these challenges, this paper proposes a model-enabled robotic machining framework for repairing paint film defects by leveraging the workpiece model as an enabling means. Within the system framework, an improved YOLOv5 algorithm is presented at first to enhance the visual detection accuracy of paint film defects in terms of network structure and loss function. Additionally, a target positioning method based on the pixel-point inverse projection technology is developed to map the 2D defect detection results onto the workpiece 3D model, which primarily aims at obtaining the orientation information through the connection between the monocular vision unit and the model. Finally, an optimal tool deployment strategy by virtue of the least projection coverage circle is proposed to determine the least machined position as well as the shortest robot path by constructing the mapping between the defects and the tool operation size. The constructed system framework is verified effective and practical by the experiments of region-based robotic grinding and repairing of paint film defects on high-speed train (HST) body sidewalls.