Teaching robots to weld by leveraging human expertise

Zhou, Junfu; Mohammad, Abdelkhalick; Zeng, Tianyi; Axinte, Dragos; Wright, Iain; March, Richard

Abstract

Robotic welding systems are pivotal in various manufacturing sectors, such as aerospace, construction, automotive, and maritime industries, due to their ability to operate in challenging environments with fewer physical constraints compared to human welders. However, their lack of process knowledge and adaptability necessitates heavy reliance on experienced technicians for process planning. To mitigate these challenges, a novel robotic welding system is proposed, focusing on learning from manual operations. In the proposed approach, proficient welders execute basic tasks, such as welding simple lines or arcs, while their actions are recorded using an operation tracking system. Then key welding parameters, such as torch travelling speed, welding arc length, welding angle, welding current, and wire feeding rate, are extracted and stored in a skill library. New welding tasks are segmented into the elements of the library. These are matched with archived parameters to plan the process for the robotic welding system, effectively transferring welding expertise to the automated system. Experiments have been conducted to verify the system. A skilled welder was asked to weld linear and arc-shaped grooves on stainless steel workpieces, while the welder’s skills were tracked, extracted, and stored digitally. These skills were further used to plan the robotic welding system to execute new complex tasks, such as polynomial curves. Welding results from the robot show a quality that is on par with that of a skilled welder.

Citation

Zhou, J., Mohammad, A., Zeng, T., Axinte, D., Wright, I., & March, R. (2025). Teaching robots to weld by leveraging human expertise. Robotics and Computer-Integrated Manufacturing, 95, Article 103027. https://doi.org/10.1016/j.rcim.2025.103027

• Journal Article Type: Article
• Acceptance Date: Mar 30, 2025
• Online Publication Date: Apr 11, 2025
• Publication Date: 2025-10
• Deposit Date: Apr 13, 2025
• Publicly Available Date: Apr 14, 2025
• Journal: Robotics and Computer-Integrated Manufacturing
• Print ISSN: 0736-5845
• Electronic ISSN: 1879-2537
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 95
• Article Number: 103027
• DOI: https://doi.org/10.1016/j.rcim.2025.103027
• Public URL: https://nottingham-repository.worktribe.com/output/47668371
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S073658452500081X?via%3Dihub