Model-enabled robotic machining framework for repairing paint film defects

• Published in: Robotics and computer-integrated manufacturing Vol. 89; p. 102791
• Main Authors: Wang, Shengzhe, Xu, Ziyan, Wang, Yidan, Tan, Ziyao, Zhu, Dahu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-10-2024
• Subjects: Model-enabled robotic machining; Paint film defect detection; Robot path planning; Target defect positioning; YOLOv5 algorithm; Robot path planning; Paint film defect detection; YOLOv5 algorithm; Model-enabled robotic machining; Target defect positioning
• ISSN: 0736-5845; 1879-2537
• DOI: 10.1016/j.rcim.2024.102791

•Proposed a model-enabled robotic machining system for repairing paint film defects.•Presented an improved YOLOv5 to enhance visual detection accuracy of defects.•Developed a target positioning method based on pixel-point inverse projection.•Proposed an optimal tool deployment strategy based on vector projection.•Verified both effectiveness and practicality of the proposed strategy.
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.