Skip to main content
SpringerLink
Log in

A Process Control Methodology Based on Digital Twins of Production System Objects

  • INFORMATION SYSTEMS
  • Published:
Automatic Documentation and Mathematical Linguistics Aims and scope

Abstract

We propose a methodology for building digital twins for the management of production systems. The digital twin consists of invariant and variable parts. The variable part is associated with industry-specific features of the subject area. A theoretical and quantitative mathematical description of the elements of the digital twin model is proposed. On the basis of predicate calculus a mathematical model for search of a problem situation and development of management actions is proposed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.

Similar content being viewed by others

REFERENCES

  1. Alam, K.M. and El Saddik, A., C2ps: A digital twin architecture reference model for the cloud-based cyber-physical systems, IEEE Access, 2017, vol. 5, pp. 2050–2062. https://doi.org/10.1109/ACCESS.2017.2657006

    Article  Google Scholar 

  2. Grieves, M., Digital twin: Manufacturing excellence through virtual factory replication, A Whitepaper by Dr. Michael Grieves, 2014. http://innovate.fit.edu/plm/documents/doc_mgr/912/1411.0_Digital_Twin_White_Paper_Dr_Grieves.pdf

  3. Negri, E., Fumagalli, L., and Macchi, M., A review of the roles of digital twin in CPS-based production systems, Procedia Manuf., 2017, vol. 11, pp. 939–948. https://doi.org/10.1016/j.promfg.2017.07.198

    Article  Google Scholar 

  4. Boschert, S. and Rosen, R., Digital twin—The simulation aspect, Mechatronic Futures, Hehenberger, P. and Bradley, D., Eds., Cham: Springer, 2016, pp. 59–74. https://doi.org/10.1007/978-3-319-32156-1_5

    Book  Google Scholar 

  5. Rosen, R., von Wichert, G., Lo, G., and Bettenhausen, K.D., About the importance of autonomy and digital twins for the future of manufacturing, IFAC-PapersOnLine, 2015, vol. 48, no. 3, pp. 567–572.  https://doi.org/10.1016/j.ifacol.2015.06.141

    Article  Google Scholar 

  6. Schluse, M. and Rossmann, J., From simulation to experimentable digital twins: Simulation-based development and operation of complex technical systems, in IEEE Int. Symp. on Systems Engineering (ISSE), Edinburgh, 2016 (IEEE, 2016), pp. 1–6.  https://doi.org/10.1109/SysEng.2016.7753162

  7. Kraft, E.M., The air force digital thread/digital twin - Life cycle integration and use of computational and experimental knowledge, in 54th AIAA Aerospace Sciences Meeting.  https://doi.org/10.2514/6.2016-0897

  8. Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., and Barbaray, R., The industrial management of smes in the era of industry 4.0, Int. J. Prod. Res., 2018, vol. 56, no. 3, pp. 1118–1136.  https://doi.org/10.1080/00207543.2017.1372647

    Article  Google Scholar 

  9. Qi, Q., Tao, F., Zuo, Y., and Zhao, D., Digital twin service towards smart manufacturing, Procedia CIRP, 2018, vol. 72, pp. 237–242. https://doi.org/10.1016/j.procir.2018.03.103

    Article  Google Scholar 

  10. Tao, F., Zhang, M., Cheng, J. Qi, Q., Digital twin workshop: A new paradigm for future workshop, Comput. Integr. Manuf. Syst., 2017, vol. 23, no. 1, pp. 1–9.

    Google Scholar 

  11. Knapp, G.L., Mukherjee, T., Zuback, J.S., Wei, H.L., Palmer, T.A., De, A., and DeBroy, T., Building blocks for a digital twin of additive manufacturing, Acta Mater., 2017, vol. 135, pp. 390–399.  https://doi.org/10.1016/j.actamat.2017.06.039

    Article  Google Scholar 

  12. Wang, X.V. and Wang, L., Digital twin-based weee recycling, recovery and remanufacturing in the background of industry 4.0, Int. J. Prod. Res., 2018, vol. 57, no. 12, pp. 3892–3902.  https://doi.org/10.1080/00207543.2018.1497819

    Article  Google Scholar 

  13. Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., and Nee, A.Y.C., Digital twin-driven product design framework, Int. J. Prod. Res., 2018, vol. 56, no. 5, pp. 1934–1959. https://doi.org/10.1080/00207543.2017.1394587

    Article  Google Scholar 

  14. Brenner, B. and Hummel, V., Digital twin as enabler for an innovative digital shopfloor management system in the ESB Logistics Learning Factory at Reutlingen - University, Procedia Manuf., 2017, vol. 9, pp. 198–205. https://doi.org/10.1016/j.promfg.2017.04.039

    Article  Google Scholar 

  15. Yang, X., Malak, R.C., Lauer, C., Weidig, C., Hagen, H., Hamann, B., Aurich, J.C., and Kreylos, O., Manufacturing system design with virtual factory tools, Int. J. Comput. Integr. Manuf., 2015, vol. 28, no. 1, pp. 25–40. https://doi.org/10.1080/0951192X.2013.800948

    Article  Google Scholar 

  16. Shvedenko, V.N., Shchekochikhin, O.V., and Cherkasova, N.V., Search for the architectural solution of information software of the digital twins of the complex system, Nauchn.-Tekhn. Inform., Ser. 2. Protsessy Sist., 2020, no. 4, pp. 18–21. https://doi.org/10.36535/0548-0027-2020-04-3

  17. Timofeev, D.N. and Shvedenko, V.V., The method of constructing the metric system of monitoring and control interacting technical, economic and organizational subsystem, Instrum. Syst.: Monit., Control Diagn., 2017, vol. 12, pp. 53–71.

    Google Scholar 

  18. Garrigue, J., A certified implementation of ML with structural polymorphism, Programming Languages and Systems, Ueda, K., Ed., Lecture Notes in Computer Science, vol. 6461, Berlin: Springer, 2010, pp. 360–375. https://doi.org/10.1007/978-3-642-17164-2_25

    Book  Google Scholar 

  19. Garrigue, J., Simple type inference for structural polymorphism, in The Ninth Int. Workshop on Foundations of Object-Oriented Languages, Portland, Oregon, 2002.

  20. Gesbert, N., Genevès, P., and Layaïda, N., Parametric polymorphism and semantic subtyping: the logical connection, ACM SIGPLAN Not., 2011, vol. 46, no. 9, pp. 107–116. https://doi.org/10.1145/2034574.2034789

    Article  MATH  Google Scholar 

  21. Huang, B., Research article: An object model with parametric polymorphism for dynamic segmentation, Int. J. Geogr. Inf. Sci., 2003, vol. 17, no. 4, pp. 343–360. https://doi.org/10.1080/1365881031000072654

    Article  Google Scholar 

  22. Zhang, C., Zhou, G., Lu, Q., and Chang, F., Graph-based knowledge reuse for supporting knowledge-driven decision-making in new product development, Int. J. Prod. Res., 2017, vol. 55, no. 23, pp. 7187–7203.  https://doi.org/10.1080/00207543.2017.1351643

    Article  Google Scholar 

  23. Shvedenko, V.N., Shvedenko, V.V., and Shchekochikhin, O.V., Using structural and parametric polymorphism in the creation of digital twins, Autom. Doc. Math. Linguist., 2019, vol. 53, no. 2, pp. 81–48.  https://doi.org/10.3103/S0005105519020055

    Article  Google Scholar 

  24. Shvedenko, V.N., Shvedenko, V.V., and Shchekochikhin, O.V., Using structural polymorphism in creating process-based management information systems, Autom. Doc. Math. Linguist., 2018, vol. 52, no. 6, pp. 290–296.  https://doi.org/10.3103/S0005105518060031

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. All-Russian Institute for Scientific and Technical Information, Russian Academy of Sciences, Moscow, Russia

    V. N. Shvedenko

  2. OOO T-Innovatik, St. Petersburg, Russia

    V. V. Shvedenko

  3. Department for Information Security, Kostroma State University, Kostroma, Russia

    O. V. Shchekochikhin

Authors
  1. V. N. Shvedenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. V. Shvedenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. O. V. Shchekochikhin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to V. N. Shvedenko, V. V. Shvedenko or O. V. Shchekochikhin.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shvedenko, V.N., Shvedenko, V.V. & Shchekochikhin, O.V. A Process Control Methodology Based on Digital Twins of Production System Objects. Autom. Doc. Math. Linguist. 55, 210–218 (2021). https://doi.org/10.3103/S0005105521050046

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0005105521050046

Keywords:

Search

Navigation