Digital Twin Implementation of Autonomous Planning Arc Welding Robot System

Xuewu Wang; Yi Hua; Jin Gao; Zongjie Lin; Rui Yu

doi:10.23919/CSMS.2023.0013

Complex System Modeling and Simulation 2023, 3(3): 236-251 https://doi.org/10.23919/CSMS.2023.0013

Open Access | Issue | Published: 03 August 2023

Digital Twin Implementation of Autonomous Planning Arc Welding Robot System

Show Author's Information Hide Author's Information Xuewu Wang^¹(

), Yi Hua^¹, Jin Gao^¹, Zongjie Lin^¹, Rui Yu^²

1School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

2Institute for Sustainable Manufacturing, and also with the Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA

Keywords:

path planning, digital twin, topology, arc welding robot

Cite this article:

Wang X, Hua Y, Gao J, et al. Digital Twin Implementation of Autonomous Planning Arc Welding Robot System. Complex System Modeling and Simulation, 2023, 3(3): 236-251. https://doi.org/10.23919/CSMS.2023.0013

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Abstract

Industrial robots are currently applied for ship sub-assembly welding to replace welding workers because of the intelligent production and cost savings. In order to improve the efficiency of the robot system, a digital twin system of welding path planning for the arc welding robot in ship sub-assembly welding is proposed in this manuscript to achieve autonomous planning and generation of the welding path. First, a five-dimensional digital twin model of the dual arc welding robot system is constructed. Then, the system kinematics analysis and calibration are studied for communication realization between the virtual and the actual system. Besides, a topology consisting of three bounding volume hierarchies (BVH) trees is proposed to construct digital twin virtual entities in this system. Based on this topology, algorithms for welding seam extraction and collision detection are presented. Finally, the genetic algorithm and the RRT-Connect algorithm combined with region partitioning (RRT-Connect-RP) are applied for the welding sequence global planning and local jump path planning, respectively. The digital twin system and its path planning application are tested in the actual application scenario. The results show that the system can not only simulate the actual welding operation of the arc welding robot but also realize path planning and real-time control of the robot.

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About this article

Digital Twin Implementation of Autonomous Planning Arc Welding Robot System

Show Author's information Hide Author's Information Xuewu Wang^¹(

), Yi Hua^¹, Jin Gao^¹, Zongjie Lin^¹, Rui Yu^²

1School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

2Institute for Sustainable Manufacturing, and also with the Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA

Abstract

Keywords: path planning, digital twin, topology, arc welding robot

References(24)

[1]

F. Tao, C. Y. Zhang, Q. Qi, and H. Zhang, Digital twin maturity model, (in Chinese), Computer Integrated Manufacturing Systems, vol. 28, no. 5, pp. 1267–1281, 2022.

Google Scholar

[2]

N. Zhao, G. Lodewijks, Z. Fu, Y. Sun, and Y. Sun, Trajectory predictions with details in a robotic twin-crane system, Complex System Modeling and Simulation, vol. 2, no. 1, pp. 1–17, 2022.

DOI Google Scholar

[3]

P. Gu and Y. Xu, Research on path planning of synchronous welding of dual robot based on QPSO, (in Chinese), Industrial Instrumentation &Automation, vol. 5, pp. 78–82, 2015.

Google Scholar

[4]

F. Tao, W. R. Liu, M. Zhang, T. Hu, Q. Qi, H. Zhang, F. Sui, T. Wang, H. Xu, Z. Huang, et al., Five-dimension digital twin model and its ten applications, (in Chinese), Computer Integrated Manufacturing Systems, vol. 25, no. 1, pp. 1–18, 2019.

Google Scholar

[5]

W. R. Liu, F. Tao, J. F. Cheng, L. C. Zhang, and W. M. Yi, Digital twin satellite: Concept, key technologies and applications, (in Chinese), Computer Integrated Manufacturing Systems, vol. 26, no. 3, pp. 565–588, 2020.

Google Scholar

[6]

Z. Xu, F. Ji, S. Ding, Y. Zhao, Y. Zhou, Q. Zhang, and F. Du, Digital twin-driven optimization of gas exchange system of 2-stroke heavy fuel aircraft engine, J. Manuf. Syst., vol. 58, pp. 132–145, 2021.

DOI Google Scholar

[7]

X. Li, H. Liu, W. Wang, Y. Zheng, H. Lv, and Z. Lv, Big data analysis of the Internet of Things in the digital twins of smart city based on deep learning, Future Gener. Comput. Syst., vol. 128, pp. 167–177, 2022.

DOI Google Scholar

[8]

Y. Guo, F. Yang, S. Ge, Y. Huang, and X. You, Novel knowledge-driven active management and control scheme of smart coal mining face with digital twin, (in Chinese), Journal of China Coal Society, doi: 10.13225/j.cnki.jccs.2022.0223.

[9]

Y. Wang, F. Tao, M. Zhang, L. Wang, and Y. Zuo, Digital twin enhanced fault prediction for the autoclave with insufficient data, J. Manuf. Syst., vol. 60, pp. 350–359, 2021.

DOI Google Scholar

[10]

L. Liu, X. Zhang, X. Wan, S. Zhou, and Z. Gao, Digital twin-driven surface roughness prediction and process parameter adaptive optimization, Adv. Eng. Inform., vol. 51, p. 101470, 2022.

DOI Google Scholar

[11]

S. S. Akash and M. S. Ferdous, A blockchain based system for healthcare digital twin, IEEE Access, vol. 10, pp. 50523–50547, 2022.

DOI Google Scholar

[12]

J. Pang, Y. Huang, Z. Xie, J. Li, and Z. Cai, Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution, Tsinghua Science and Technology, vol. 26, no. 5, pp. 759–771, 2021.

DOI Google Scholar

[13]

X. Zhang, B. Wu, X. Zhang, J. Duan, C. Wan, and Y. Hu, An effective MBSE approach for constructing industrial robot digital twin system, Robotics Comput. Integr. Manuf., vol. 80, p. 102455, 2023.

DOI Google Scholar

[14]

W. Hu, C. Wang, F. Liu, X. Peng, P. Sun, and J. Tan, A grasps-generation-and-selection convolutional neural network for a digital twin of intelligent robotic grasping, Robotics Comput. Integr. Manuf., vol. 77, p. 102371, 2022.

DOI Google Scholar

[15]

N. Kousi, C. Gkournelos, S. Aivaliotis, C. Giannoulis, G. Michalos, and S. Makris, Digital twin for adaptation of robots’ behavior in flexible robotic assembly lines, Procedia Manuf., vol. 28, pp. 121–126, 2019.

DOI Google Scholar

[16]

W. Wang, W. Ding, C. Hua, H. Zhang, H. Feng, and Y. Yao, A digital twin for 3D path planning of large-span curved-arm gantry robot, Robotics Comput. Integr. Manuf., vol. 76, p. 102330, 2022.

DOI Google Scholar

[17]

S. Yan, Construction and evolution of digital twin for welding robots, (in Chinese), MSc dissertation, School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China, 2021.

[18]

F. Hu, Mutual information-enhanced digital twin promotes vision-guided robotic grasping, Adv. Eng. Inform., vol. 52, p. 101562, 2022.

DOI Google Scholar

[19]

R. S. Tabar, K. Wärmefjord, R. Söderberg, and L. Lindkvist, Efficient spot welding sequence optimization in a geometry assurance digital twin, Journal of Mechanical Design, vol. 142, no. 10, p. 102001, 2020.

DOI Google Scholar

[20]

Open CASCADE, https://www.opencascade.com/, 2022.

[21]

H. Zhang, Q. Qi, W. Ji, and F. Tao, An update method for digital twin multi-dimension models, Robotics Comput. Integr. Manuf., vol. 80, p. 102481, 2023.

DOI Google Scholar

[22]

T. Möller, A fast triangle-triangle intersection test, J. Graph. Tools, vol. 2, no. 2, pp. 25–30, 1997.

DOI Google Scholar

[23]

S. Gottschalk, Separating axis theorem, Tech. Rep. TR96-024, Department of Computer Science, UNC Chapel Hill, Chapel Hill, NC, USA, 1996.

[24]

J. J. Kuffner and S. M. LaValle, RRT-connect: An efficient approach to single-query path planning, in Proc. 2000 ICRA. Millennium Conference. IEEE Int. Conf. Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), San Francisco, CA, USA, 2002, pp. 995–1001.

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Publication history

Received: 22 March 2023

Revised: 17 May 2023

Accepted: 24 May 2023

Published: 03 August 2023

Issue date: September 2023

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Acknowledgment

This work was supported by the National Natural Science Foundation of China (Nos. 62076095 and 61973120) and National Key Research and Development Program (No. 2022YFB4602104).

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).