Skip to main content

A Cognitive Approach to Manage the Complexity of Digital Twin Systems

Part of the Progress in IS book series (PROIS)

Abstract

During the entire lifecycle of system development, various digital twins could be developed to support different systems engineering activities, such as verification and validation. The increasing complexity of digital twins leads to a challenge to manage the consistency, changes and traceability across the entire lifecycle. In this paper, a semantics modeling approach is provided to formalize the digital twins using systems thinking. The semantic models represent the information of each digital twin and the interrelationships among them. Using the semantic models, system developers are enabled to promote the cognitive capabilities of digital twins, which in return will provide more potentials for decision-makings based on digital twins. Finally, the feasibility of the proposed approach is evaluated through a case study in the Swiss Innovation Project IMPURSE.

Keywords

  • Digital twins
  • Complexity management
  • Semantics modeling
  • Cognitive twin

Jinzhi Lu is a research scientist in ICT4SM group at EPFL.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-72090-2_10
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   129.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-72090-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   169.99
Price excludes VAT (USA)
Hardcover Book
USD   169.99
Price excludes VAT (USA)
Fig. 1
Fig. 2

References

  • Borschev, & Anylogic. (2008). How to build a combined agent based/system dynamics model in any logic. In System Dynamics Conference.

    Google Scholar 

  • Boschert, S., & Rosen, R. (2016). Digital twin—the simulation aspect. In Mechatronic futures (pp. 59–74). Springer.

    Google Scholar 

  • Cho, S., May, G., & Kiritsis, D. (2019). A semantic-driven approach for industry 4.0. In 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 347–354).

    Google Scholar 

  • Efthymiou, K., Pagoropoulos, A., Papakostas, N., Mourtzis, D., & Chryssolouris, G. (2012). Manufacturing systems complexity review: Challenges and outlook. Procedia CIRP, 3, 644–649.

    CrossRef  Google Scholar 

  • Efthymiou, K., Pagoropoulos, A., Papakostas, N., Mourtzis, D., & Chryssolouris, G. (2014). Manufacturing systems complexity: An assessment of manufacturing performance indicators unpredictability. CIRP Journal of Manufacturing Science and Technology, 7(4), 324–334.

    CrossRef  Google Scholar 

  • El Saddik, A. (2018). Digital twins: The convergence of multimedia technologies. IEEE Multimedia, 25(2), 87–92.

    CrossRef  Google Scholar 

  • Frank, M. (2012). Engineering systems thinking: Cognitive competencies of successful systems engineers. Procedia Computer Science, 8, 273–278.

    CrossRef  Google Scholar 

  • Gharaei, A., Lu, J., Stoll, O., Zheng, X., West, S., & Kiritsis, D. (2020). Systems engineering approach to identify requirements for digital twins development. In B. Lalic, V. Majstorovic, U. Marjanovic, G. von Cieminski, & D. Romero (Eds.), Advances in production management systems the path to digital transformation and innovation of production management systems (pp. 82–90). Cham: Springer International Publishing.

    Google Scholar 

  • Goldstein, H. (2001, Nov). Emergence: The connected lives of ants, brains, cities, and software [Book Review]. IEEE Spectrum, 38(11), 66. Retrieved from https://ieeexplore.ieee.org/document/963260/. https://doi.org/10.1109/MSPEC.2001.963260.

  • Greene, M. T., & Papalambros, P. Y. (2016). A cognitive framework for engineering systems thinking. In 2016 Conference on Systems Engineering Research (pp. 1–7).

    Google Scholar 

  • Haskins, C. (2014, July). A journey through the systems landscape. SIGHT, 17(2), 63–64. Retrieved from http://doi.wiley.com/10.1002/inst.201417263a. https://doi.org/10.1002/inst.201417263a.

  • ISO/IEC. (2007). Systems and software engineering: Recommended practice for architectural description of software-intensive systems (Vol. 2007). Technical Report.

    Google Scholar 

  • Kasser, J., & Mackley, T. (2008). Applying systems thinking and aligning it to systems engineering. In Incose International Symposium (Vol. 18, pp. 1389–1405).

    Google Scholar 

  • Kenett, R. S., Zonnenshain, A., & Swarz, R. S. (2018). Systems engineering, data analytics, and systems thinking: Moving ahead to new and more complex challenges. In Incose International Symposium (Vol. 28, pp. 1608–1625).

    Google Scholar 

  • Lu, J., Töorngren, M., Chen, D. J., & Wang, J. (2018). A tool integration language to formalize co-simulation tool-chains for cyber-physical system (CPS). Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10729, pp. 391–405). https://doi.org/10.1007/978-3-319-74781-1.

  • Lu, J., Wang, G., & Torngren, M. (2020, Mar). Design ontology in a case study for cosimulation in a model-based systems engineering tool-chain. IEEE Systems Journal, 14(1), 1297–1308. Retrieved from https://ieeexplore.ieee.org/document/8734748/. https://doi.org/10.1109/JSYST.2019.2911418.

  • Lu, J., Zheng, X., Gharaei, A., Kalaboukas, K., & Kiritsis, D. (2020). Cognitive twins for supporting decision-makings of internet of things systems. In Proceedings of 5th International Conference on the Industry 4.0 Model for Advanced Manufacturing (pp. 105–115).

    Google Scholar 

  • Meierhofer, J., West, S., Rapaccini, M., & Barbieri, C. (2020). The digital twin as a service enabler: From the service ecosystem to the simulation model. In International Conference on Exploring Services Science (pp. 347–359).

    Google Scholar 

  • Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., et al. (2019). Enabling technologies and tools for digital twin. Journal of Manufacturing Systems.

    Google Scholar 

  • Scaglioni, B., & Ferretti, G. (2018). Towards digital twins through object oriented modelling: A machine tool case study. IFAC-Papers OnLine, 51(2), 613–618. https://doi.org/10.1016/j.ifacol.2018.03.104.

  • Shank, B. (2013). Disorganized and organized complexity. Retrieved from https://pov.mastersprogram.org/2013/10/14/disorganized-and-organized-complexity/.

  • Stevens, R., & Hancock, J. M. (2004, Oct). Protégé. In Dictionary of bioinformatics and computational biology. Chichester, UK: Wiley. Retrieved from http://doi.wiley.com/10.1002/9780471650126.dob0577.pub2. https://doi.org/10.1002/9780471650126.dob0577.pub2.

  • Tao, F., Zhang, M., Cheng, J., & Qi, Q. (2017). Digital twin workshop: A new paradigm for future workshop. Computer Integrated Manufacturing Systems, 23(1), 1–9.

    Google Scholar 

  • Weaver, W. (1991). Science and complexity. In Facets of systems science (pp. 449–456). Boston, MA: Springer US. https://doi.org/10.1007/978-1-4899-0718-9.

Download references

Acknowledgements

The work presented in this paper is supported by the EU H2020 project (869951) FACTLOG-Energy-aware Factory Analytics for Process Industries and EU H2020 project (825030) QU4LITY Digital Reality in Zero Defect Manufacturing and the InnoSwiss IMPULSE project on Digital Twins.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jinzhi Lu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Lu, J., Zheng, X., Schweiger, L., Kiritsis, D. (2021). A Cognitive Approach to Manage the Complexity of Digital Twin Systems. In: West, S., Meierhofer, J., Ganz, C. (eds) Smart Services Summit. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-030-72090-2_10

Download citation