Citation: | GAO Hui, HU Xiaohui, WANG Long, ZHOU Canfeng, JIAO Xiangdong. Digital twin technology of welding robot for on-site maintenance[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(11): 55-60. DOI: 10.12073/j.hjxb.20240804001 |
The engineering field environment of nuclear power, shield structure, ship and ocean engineering and other industries is characterised by complex, unstructured and severe conditions, which has an urgent demand for the use of robots to replace manual welding operations. In order to solve the problem of welding operations in complex environments, this paper addresses the key technologies such as network communication, three-dimensional modelling, pose update, collision detection, data acquisition, and human-machine interaction. Based on digital twin technology, a teleoperation test platform for the welding robot has been established. This platform creates a real-time and reliable bi-directional data channel between the upper computer controlling the virtual robot and the lower computer controlling the physical robot, thereby achieving precise synchronous control of the poses of both the virtual and physical robots. At the same time, a robot teleoperation MIG welding test is carried out as the potential application scene of shield machine cutter plate field repair for Q345 steel plate material in 2G and 3G positions, and the welded seam with good appearance is obtained. The results show that the robotic digital twin and teleoperation technology has certain advantages in solving the high-quality welding under complex working conditions, and has a broad application prospect in the field of nuclear power, shield structure, ship and ocean engineering repair.
[1] |
Vachálek J, Bartalský L, Rovný O, et al. The digital twin of an industrial production line within the industry 4.0 concept[C]//2017 21st International Conference on Process Control (PC), 2017: 258 − 262.
|
[2] |
Friederich J, Francis D P, Lazarova-Molnars, et al. A framework for data driven digital twins of smart manufacturing systems[J]. Computers in Industry, 2022, 136: 103586. doi: 10.1016/j.compind.2021.103586
|
[3] |
Agapakie, Brilakis I. CLOI-NET: class segmentation of industrial facilities point cloud datasets[J]. Advanced Engineering Informatics, 2020, 45: 101121. doi: 10.1016/j.aei.2020.101121
|
[4] |
Schroeder G, Steinmetz C, Pereira C E, et al. Visualizing the digital twin using web services and augmented reality[C]//Proceedings of the 14th IEEE International Conference on Industrial Informatics. Washington, D C , USA: IEEE, 2016: 522 − 527.
|
[5] |
Ludera, Schmidtn, Rosendahl R, et al. Integrating different information type within Automation ML[C]//Proceedings of 2014 IEEE Emerging Technology and Factory Automation. Washington, D C, USA: IEEE, 2014: 1 − 5.
|
[6] |
Adel Olabi, Mohamed Damak, Richard Bearee, et al. Improving the accuracy of industrial robots by offline compensation of joints errors[C]//2012 IEEE International Conference on Industrial Technology. Piscataway, N J, USA: IEEE, 2012: 492 − 497.
|
[7] |
Stefania Pellegrinelli, Nicola Pedrocchi, Lorenzo Molinari Tosatti, et al. Multi-robot spot-welding cells for car-body assembly: Design and motion planning[J]. Robotics and Computer-Integrated Manufacturing, 2017, 44: 97 − 116. doi: 10.1016/j.rcim.2016.08.006
|
[8] |
Jacopo Aleotti, Matteo Saveriano, Riccardo Monica. Learning, perception, and collaboration for fobots in industrial environments[J]. Frontiers in Robotics and AI, 2022, 9: 888971. doi: 10.3389/frobt.2022.888971
|
[9] |
Benjamin Schleicha , Nabil Anwerb , Luc Mathieub , et al. Shaping the digital twin for design and production engineering[J]. Cirp Annals-Manufacturing Technology, 2017, 66(1): 141 − 144.
|
[10] |
Ghosh A K, Ullah A M M S, Teti R, et al. Developing sensor signal-based digital twins for intelligent machine tools[J]. Journal of Industrial Information Integration, 2021, 24: 100242. doi: 10.1016/j.jii.2021.100242
|
[11] |
王冠, 宋微, 刘畅, 等. 基于数字孪生的焊接成套装备车间现场管理辅助系统设计[J]. 制造业自动化, 2023, 45(11): 91 − 95.
Wang Guan , Song Wei , Liu Chang, et al. Design of site management auxiliary system for welding equipment workshop based on digital twin[J]. Manufacturing Automation, 2023, 45(11): 91 − 95.
|
[12] |
仇晓黎, 朱睿, 幸研, 等. 螺线管装配生产线数字孪生建模技术[J]. 计算机集成制造系统, 2022, 28(6): 1696 − 1703.
Qiu Xiaoli, Zhu Rui, Xing Yan, et al. Digital twin modeling technology for solenoid assembly production line[J]. Computer Integrated Manufacturing Systems, 2022, 28(6): 1696 − 1703.
|
[13] |
李颖, 高岚, 朱志松, 等. 面向智能制造场景的机器人数字孪生建模与控制[J]. 系统仿真学报, 2024, 36(7): 1536 − 1545.
Li Ying, Gao Lan, Zhu Zhisong, et al. Digital twin modeling and control of robots for intelligent manufacturing Ssenarios[J]. Journal of System Simulation, 2024, 36(7): 1536 − 1545.
|
[14] |
周灿丰, 焦向东, 何峰, 等. 盾构刀盘高压焊接维修工艺研究[J]. 焊接技术, 2015, 44(9): 36 − 39.
Zhou Canfeng, Jiao Xiangdong, He Feng, et al. Research on high pressure welding repair process of shield cutter plate[J]. Welding Technology, 2015, 44(9): 36 − 39.
|
1. |
张伟. 焊接机器人数字孪生数据的采集传输与存储分析. 今日制造与升级. 2025(03): 102-104 .
![]() | |
2. |
刘长军,吴小翠,张昊,杜禧悦,邹婷. 铝合金激光-MIG复合焊接的研究进展与展望. 矿冶工程. 2025(02): 183-192 .
![]() |