journal article·2025-05-23Open access

Research on the Construction of Automobile Wheel Hub Intelligent Production Line Based on Digital Twin

Yanjun Chen, Min Zhou, Meizhou Zhang, Meng Zha

Applied Sciences· Volume 15· Issue 11

Abstract

This study addresses the challenges associated with virtual–real interactions, the limitations of one-dimensional data presentation, restricted real-time functionalities, and the lack of effective models for monitoring production line status. It specifically investigates intelligent production lines for automotive wheels as the focal point of the research. This study explores the construction methodology and the application of intelligent production lines through the utilization of digital twin technology. A hierarchical design approach is employed, integrating industrial Internet of Things (IoT) technology to create a comprehensive digital twin system. This system consists of four layers: the physical production line layer, the data acquisition and processing layer, the digital twin production line layer, and the application service layer. Precise mapping from the physical production line to the digital twin model is achieved using the advanced 3D modeling and simulation software, PQ Factory, while real-time data collection and transmission are facilitated through the standardized OPC UA protocol. The effectiveness of the system is substantiated through a detailed case study. The findings demonstrate that the intelligent production line system, which leverages digital twin technology for automotive wheels, enables real-time monitoring of the production process and provides innovative solutions, along with a robust theoretical framework for comprehensive analysis, diagnosis, evaluation, optimization, prediction, and decision making in the production of automotive wheels.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Digital Transformation in Industry · Engineering Technology and Methodologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China

Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China

Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan 430081, China

Funding

National Natural Science Foundation of China

References (47)

Show source-provided references
1. Digital twins-based smart manufacturing system design in Industry 4.0: A review10.1016/j.jmsy.2021.05.011 ↗
2. Improving material quality management and manufacturing organizations system through Industry 4.0 technologies10.1016/j.matpr.2021.01.585 ↗
3. Caiza, G., and Sanz, R. (2024). An Immersive Digital Twin Applied to a Manufacturing Execution System for the Monitoring and Control of Industry 4.0 Processes. Appl. Sci., 14.10.3390/app14104125 ↗
4. Wan, J., Chen, B., and Wang, S. (2023). Smart Manufacturing Factory: Artificial-Intelligence-Driven Customized Manufacturing, CRC Press. [1st ed.].10.1201/9781003460992 ↗
5. Product lifecycle management: The new paradigm for enterprises10.1504/IJPD.2005.006669 ↗
6. Grieves, M.W. (2006). Product Lifecycle Management, McGraw-Hill.
7. Grieves, M.W. (2011). Virtually Perfect: Driving Innovative and Lean Products Through Product Lifecycle Management, Space Coast Press.
8. Digital Twin: Enabling Technologies, Challenges and Open Research10.1109/ACCESS.2020.2998358 ↗
9. Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues10.1016/j.rcim.2019.101837 ↗
10. Reinforcement learning and digital twin-based real-time scheduling method in intelligent manufacturing systems10.1016/j.ifacol.2022.09.413 ↗
11. Digital twin and production line simulation technology
12. Xiao, R., Wang, S., Liang, Z., Zhang, X., and Xie, Y. (2023, January 24–26). Digital-Twin-Assisted Optimization of Intelligent Manufacturing Flexible Production Line with Automated Guided Vehicle. Proceedings of the 2023 42nd Chinese Control Conference (CCC), Tianjin, China.10.23919/CCC58697.2023.10240458 ↗
13. A digital twin framework for prognostics and health management10.1016/j.compind.2023.103948 ↗
14. Digital twin-driven product design, manufacturing and service with big data10.1007/s00170-017-0233-1 ↗
15. Mahomed, A.S., and Saha, A.K. (2025). Unleashing the Potential of 5G for Smart Cities: A Focus on Real-Time Digital Twin Integration. Smart Cities, 8.10.3390/smartcities8020070 ↗
16. An application framework of digital twin and its case study10.1007/s12652-018-0911-3 ↗
17. A digital twin framework for the simulation and optimization of production systems10.1016/j.procir.2021.11.128 ↗
18. Research and implementation of digital twin workshop based on real-time data driven
19. Research on Virtual Commissioning System for Human-Robot Collaboration Assembly Cell Based on AutomationML10.1016/j.procir.2024.10.243 ↗
20. The digital twin of the quality monitoring and control in the series solar cell production line10.1016/j.jmsy.2021.02.001 ↗
21. Cai, H., Zhang, W., and Zhu, Z. (2019, January 24–25). Quality Management and Analysis of Aircraft Final Assembly Based on Digital Twin. Proceedings of the 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China.10.1109/IHMSC.2019.00054 ↗
22. Exploration of intelligent production line applications based on digital twin technology
23. Qi, Y., Xie, B., and Deng, S. (2020, January 16–18). Research on Intelligent Manufacturing Flexible Production Line System based on Digital Twin. Proceedings of the 2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC), Zhanjiang, China.
24. Make more digital twins10.1038/d41586-019-02849-1 ↗
25. Wu, J., Yang, Y., Cheng, X., Zuo, H., and Cheng, Z. (2020, January 6–8). The development of digital twin technology review. Proceedings of the 2020 Chinese Automation Congress (CAC), Shanghai, China.10.1109/CAC51589.2020.9327756 ↗
26. Digital twin-driven intelligent production line for automotive MEMS pressure sensors10.1016/j.aei.2022.101779 ↗
27. A digital twin-based flexible cellular manufacturing for optimization of air conditioner line10.1016/j.jmsy.2020.07.012 ↗
28. Research on digital twin framework for customized product manual assembly systems10.1016/j.jmsy.2023.08.018 ↗
29. Chaux, J.D., Sanchez-Londono, D., and Barbieri, G. (2021). A digital twin architecture to optimize productivity within controlled environment agriculture. Appl. Sci., 11.10.3390/app11198875 ↗
30. Guo, Z., Hu, S., Jin, W., Ye, Y., and Shan, C. (2023). Application of Digital Twin in the Industry of Axial Hollow-Wall Pipes. Appl. Sci., 13.10.3390/app13148093 ↗
31. Digital Twin applications toward Industry 4.0: A Review10.1016/j.cogr.2023.04.003 ↗
32. A literature review and design methodology for digital twins in the era of zero defect manufacturing10.1080/00207543.2022.2101960 ↗
33. Automatic Generation of Charging Point’s Digital Twin for Virtual Commissioning of Their Automation Systems10.1109/OJIES.2022.3230214 ↗
34. Digital twin and its potential application exploration
35. Virtual Commissioning of Linked Cells Using Digital Models in an Industrial Metaverse10.3390/automation5010001 ↗
36. Liu, J., and Zhang, K. (2023). Design and Simulation Debugging of Automobile Connecting Rod Production Line Based on the Digital Twin. Appl. Sci., 13.10.3390/app13084919 ↗
37. Konstantinov, S., Assad, F., Ahmad, B., Vera, D.A., and Harrison, R. (2022). Virtual Engineering and Commissioning to Support the Lifecycle of a Manufacturing Assembly System. Machines, 10.10.3390/machines10100939 ↗
38. Visual Monitoring System of Digital Twin Workshop for Process Manufacturing
39. Construction and Application of Digital Twin System for Workshop Production Process
40. Evaluation index system for digital twin model
41. Digital Twins and Cyber-Physical Systems Toward Smart Manufacturing and Industry 4.0: Correlation and Comparison10.1016/j.eng.2019.01.014 ↗
42. Online Prediction of workshop Running State based on Digital Twin
43. Industrial Equipment Data Acquisition System Based on OPC UA
44. Digital twin modeling10.1016/j.jmsy.2022.06.015 ↗
45. Cai, H., Wan, J., and Chen, B. (2024). Digital Twin-Driven Multi-Factor Production Capacity Prediction for Discrete Manufacturing Workshop. Appl. Sci., 14.10.3390/app14073119 ↗
46. 3D visual Real-time Monitoring Method for Digital Twin Workshop
47. Visual Real-time monitoring Method of Discrete Manufacturing Workshop based on Digital Twin