Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-18T18:54:18.409Z Has data issue: false hasContentIssue false

Self-optimizing digital factory twin: an industrial use case

Published online by Cambridge University Press:  16 May 2024

Christian Nigischer*
Affiliation:
Austrian Center for Digital Production, Austria
Florian Reiterer
Affiliation:
Nemak Linz GmbH, Austria
Sébastien Bougain
Affiliation:
Austrian Center for Digital Production, Austria
Manfred Grafinger
Affiliation:
TU Wien, Austria

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Digital Twins (DTs) are intended to be utilized for a wide range of applications, promising benefits like visualization, monitoring, simulation and control of a physical system. Not only the development of a DT for a production facility is a time-consuming task, but also to keep the virtual counterpart up to date in the use phase. In this work, the implementation of an industrial-scale DT of an automotive supplier production site based on a Discrete-Event Simulation (DES) model with self-optimization capabilities for easier maintainability and increased simulation accuracy is presented.

Type
Artificial Intelligence and Data-Driven Design
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Cheng, J., Zhang, H., Tao, F. and Juang, C.-F. (2020), “DT-II: Digital twin enhanced Industrial Internet reference framework towards smart manufacturing”, Robotics and Computer Integrated Manufacturing, Vol. 62, pp. 1-14. https://doi.org/10.1016/j.rcim.2019.101881CrossRefGoogle Scholar
Grieves, M. and Vickers, J. (2017), “Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems”, In: Kahlen, F.-J., Flumerfelt, S. and Alves, A. (Eds.), Transdisciplinary Perspectives on Complex Systems: New findings and approaches, Springer, Cham, pp. 85-113.https://doi.org/10.1007/978-3-319-38756-7_4CrossRefGoogle Scholar
ISO (2021), ISO 23247-1:2021: Automation systems and integration – Digital twin framework for manufacturing – Part 1: Overview and general principles, Geneva.Google Scholar
Jiang, H., Qin, S., Fu, J., Zhang, J. and Ding, G. (2021), “How to model and implement connections between physical and virtual models for digital twin applications”, Journal of Manufacturing Systems, Vol. 58, Part B, pp. 36-51. https://doi.org/10.1016/j.jmsy.2020.05.012CrossRefGoogle Scholar
Kritzinger, W., Karner, M., Traar, G., Henjes, J. and Sihn, W. (2018), “Digital Twin in manufacturing: A categorical literature review and classification”, Ifac-PapersOnline 51-11, Elsevier Ltd., pp. 1016-1022. https://doi.org/10.1016/j.ifacol.2018.08.474Google Scholar
Lin, W.D. and Low, M.Y.H. (2019) “Concept and Implementation of a Cyber-Physical Digital Twin for a SMT Line”, 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Macao, China, December 15-18, IEEE, pp. 1455-1459. https://doi.org/10.1109/IEEM44572.2019.8978620Google Scholar
Liu, M., Fang, S., Dong, H. and Xu, C. (2021), “Review of digital twin about concepts, technologies, and industrial applications”, Journal of Manufacturing Systems, Vol. 58, Part B, pp. 346-361. https://doi.org/10.1016/j.jmsy.2020.06.017CrossRefGoogle Scholar
Lu, Y., Liu, C., Wang, K., Huang, H. and Xu, X. (2020), “Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues”, Robotics and Computer Integrated Manufacturing, Vol. 61, pp. 1-14. https://doi.org/10.1016/j.rcim.2019.101837CrossRefGoogle Scholar
Morabito, L., Ippolito, M., Pastore, E., Alfieri, A. and Montagna, F. (2021). “A Discrete Event Simulation Based Approach for Digital Twin Implementation”, Ifac-PapersOnline 54-1, Elsevier Ltd., pp. 414-419. https://doi.org/10.1016/j.ifacol.2021.08.164Google Scholar
Nigischer, C., Reiterer, F., Bougain, S. and Grafinger, M. (2023), “Finding the proper level of detail to achieve sufficient model fidelity using FlexSim: An industrial use case”, Procedia CIRP 119 / 33rd CIRP Design Conference, Sydney, Australia, May 17-19, Elsevier Ltd., pp. 1240-1245. https://doi.org/10.1016/j.procir.2023.02.192Google Scholar
Pöchgraber, G., Bougain, S., Trautner, T., Jeepjua, N. and Bleicher, F. (2023), “Digital Twin Preparation for the Prototyping Phase, a Use Case”, Proceedings of the Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV2023) and the World Mass Customization & Personalization Conference (MCPC2023), Bologna, Italy, June, 2023, Springer, Cham, pp. 1-9.https://doi.org/10.1007/978-3-031-34821-1_80Google Scholar
Santos, C.H., De Queiroz, J.A., Leal, F. and Montevechi, J.A.B. (2022), “Use of simulation in the industry 4.0 context: Creation of a Digital Twin to optimise decision making on non-automated process”, Journal of Simulation, Vol. 16, No. 3, pp. 1-14. https://doi.org/10.1080/17477778.2020.1811172CrossRefGoogle Scholar
Shao, G. and Kibira, D. (2018), “Digital manufacturing: Requirements and challenges for implementing digital surrogates”, Proceedings of the 2018 Winter Simulation Conference, Gothenburg, Sweden, December 9-12, IEEE, pp. 1226-1237. https://doi.org/10.1109/WSC.2018.8632242CrossRefGoogle Scholar
Singh, M., Fuenmayor, E., Hinchy, E., Qiao, Y., Murray, N. and Devine, D. (2021), “Digital Twin: Origin to Future”, Applied System Innovation, Vol. 4, No. 2, pp. 1-19. https://doi.org/10.3390/asi4020036Google Scholar