Research on the Deep Integration and Application of Digital Twins and Virtual Debugging Technology in Teaching Experimental Platform
DOI:
https://doi.org/10.54691/w7ewzk20Keywords:
Digital Twin, virtual debugging, mechatronics conceptual Design, teaching experiment, PLCSIM, TIA Portal.Abstract
Digital Twin and Virtual Commissioning are the core enablement technology of Industry 4.0, which is deeply penetrated from the industry to the field of education, providing a new paradigm for innovating the traditional experimental teaching model. This paper proposes a teaching experimental platform construction solution that deeply integrates digital twins and virtual debugging technology. This solution takes Siemens TIA Portal and UGNX/MCD as the technical base. By establishing a high-fidelity mechatronic digital twin model and using PLCSIM Advanced to achieve real-time data interaction with virtual PLCs, it builds a complete "virtual and real mapping, rehearsal optimization, and linkage verification" closed-loop system. This article not only explains the overall architecture and key technologies of the system, but also theoretically analyzes the mathematical model of the virtual debugging process driven by digital twins. Through design comparison experiments, the improvement of the platform's teaching effectiveness was quantitatively evaluated. The data shows that after adopting this plan, the pass rate of student program debugging increased by about 40%, the time of experimental equipment occupied by 60%, and the depth of understanding of complex systems was significantly enhanced. This study provides replicable technical paths and theoretical support for the new generation of practical teaching of engineering science.
Downloads
References
[1] Zhu, Z., Liu, P., & Xu, X. (2022). A review of virtual commissioning technology and its applications in industry and education. Journal of Manufacturing Systems, 64, 562 578. https://doi.org/10.1016/j.jmsy.2022.07.009.
[2] Uhlemann, T. H. J., Lehmann, C., & Steinhilper, R. (2020). The digital twin: Realizing the cyber physical production system for Industry 4.0. Procedia CIRP, 88, 99 104. https://doi.org/10.1016/j.procir.2020.05.018
[3] Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State of the art. IEEE Transactions on Industrial Informatics, 15(4), 2405 2415. https://doi.org/10.1109/TII.2018.2873186
[4] Schluse, M., Priggemeyer, M., Atorf, L., & Rossmann, J. (2018). Experimentable digital twins—Streamlining simulation based systems engineering for Industry 4.0. IEEE Transactions on Industrial Informatics, 14(4), 1722 1731. https://doi.org/10.1109/TII.2018.2794183
[5] Cimino, C., Negri, E., & Fumagalli, L. (2019). Review of digital twin applications in manufacturing. Computers in Industry, 113, 103130. https://doi.org/10.1016/j.compind.2019.103130
[6] Lee, C., & Zhou, M. (2021). Modeling and control of discrete event systems with learning based approaches: A survey. IEEE/CAA Journal of Automatica Sinica, 8(2), 328 349. https://doi.org/10.1109/JAS.2021.1003864
[7] Hossain, R. B., Ahmed, F., Kobayashi, K., & Sato, T. (2024). Virtual sensing enabled digital twin framework for real time monitoring of nuclear systems leveraging deep neural operators.
[8] Glaessgen, E., & Stargel, D. (2012). The digital twin paradigm for future NASA and U.S. Air Force vehicles. https://doi.org/10.2514/6.2012 1818
[9] Qi, Q. L., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access, 6, 3585 3593. https://doi.org/10.1109/ACCESS.2018.2793265
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Scientific Journal Of Humanities and Social Sciences

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.





