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Implementation specifics and application potential of digital twins of technological systems

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Abstract

The paper presents the main approaches to digital twin formation taking into account many years of research. A concept for the creation of digital twins is proposed, with regard to the evolution of the current state of the object. A digital twin is an innovative concept that is becoming increasingly relevant with the development of the fourth industrial revolution and the transition to intelligent manufacturing. The paper provides an analysis of works in the field of digital twins, considers specific features of the concept application in manufacturing, and analyzes the main problems associated with its practical implementation and their possible solutions. The work places particular emphasis on the features of the multiphysics model formation, the specifics of working with large arrays of heterogeneous data for digital twins, and the requirements for cyber-physical technological systems that are their prototypes.

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References

  1. Rosen R, Von Wichert G, Lo G, Bettenhausen KD (2015) About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine, Elsevier Ltd., pp 567–572

  2. Glaessgen EH, D. S. Stargel, The digital twin paradigm for future NASA and US Air Force vehicles In 53rd Struct. Dyn. Mater. Conf. Special Session: Digital Twin, Honolulu, US, pp. 1-14, 2012.

  3. Grieves MW (2005) Product lifecycle management: the new paradigm for enterprises. Int J Prod Dev 2(1-2):71–84

    Article  Google Scholar 

  4. M.W. Grieves, “Product specification management (PSM): enabling manufacturing quality whitepaper by Dr. Michael Grieves,” 2008.

  5. Grieves MW (2011) Virtually perfect: driving innovative and lean products through product lifecycle management. Space Coast Press

  6. G.N. Schroeder, C. Steinmetz, C.E. Pereira, E.D. B., Digital twin data modeling with automationml and a communication methodology for data exchange, IFAC-PapersOnLine, Elsevier B.V., pp. 12–17, 2016.

  7. Tao F, Zhang M (2017) Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access 5:20418–20427

    Article  Google Scholar 

  8. Grieves M, Vickers J (2016) Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. Springer International Publishing

  9. Tuegel EJ, Ingraffea AR, Eason TG, Spottswood SM (2011) Reengineering aircraft structural life prediction using a digital twin. Int J Aerospace Eng:1–14

  10. Madni AM, Madni CC, Lucero SD (2019) Leveraging digital twin technology in model-based systems engineering. Systems 7(1):7

    Article  Google Scholar 

  11. Schluse M, Priggemeyer M, Atorf L, Rossmann J (2018) Experimentable digital twins—streamlining simulation-based systems engineering for industry 4.0. IEEE Transac Indust Inform 14(4):1722–1731

    Article  Google Scholar 

  12. Stark R, Kind S, Neumeyer S (2017) Innovations in digital modeling for next generation manufacturing system design. CIRP Ann 66(1):169–172

    Article  Google Scholar 

  13. Zhang H, Zhang G, Yan Q (2018) Dynamic resource allocation optimization for digital twin-driven smart shopfloor. In: 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), pp 1–5

  14. Zheng Y, Yang S, Cheng H (2019) An application framework of digital twin and its case study. J Ambient Intell Humaniz Comput 10:1141–1153

    Article  Google Scholar 

  15. Lee J, Bagheri B, Kao H-A (2015) A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf Lett 3:18–23

    Article  Google Scholar 

  16. Maio MD et al (2018) Closed-loop systems engineering (CLOSE): integrating experimentable digital twins with the model-driven engineering process. In: 2018 IEEE International Systems Engineering Symposium (ISSE), Rome, vol 2018, pp 1–8

  17. Min Q, Lu Y, Liu Z, Su C, Wang B (2019) Machine learning based digital twin framework for production optimization in petrochemical industry. Int J Inf Manag 49:502–519

    Article  Google Scholar 

  18. Liu Z, Meyendorf N, Mrad N (1949) The role of data fusion in predictive maintenance using digital twin. AIP Confer Proc 020023:2018

    Google Scholar 

  19. K. Reifsnider, P. Majumdar, Multiphysics stimulated simulation digital twin methods for fleet management, 54th AIAA/ASME/ASCE/AHS/ASC Structures. Structural Dynamics and Materials Conference, pp. 1578, 2013.

  20. Intercax, “Syndeia”, http://intercax.com/products/syndeia/, accessed: 2016-05-22.

  21. No Magic, “Cameo Systems Modeler”, https://www.nomagic.com/products/cameo-systems-modeler.

  22. Mathworks, “Matlab”, https://ch.mathworks.com/discovery/digital-twin.

  23. Šormaz D, Sarkar A (2019) SIMPM—upper-level ontology for manufacturing process plan network generation. Robot Comput Integr Manuf 55:183–198

    Article  Google Scholar 

  24. Milosevic M, Lukic D, Antic A, Lalic B, Ficko M, Simunovic G (2017) e-CAPP: a distributed collaborative system for internet-based process planning. J Manuf Syst 42:210–223

    Article  Google Scholar 

  25. Tao F, Cheng J, Qi Q, Zhang M, Zhang H, Sui F (2018) Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 94(4):1–14

    Google Scholar 

  26. Li J, Tao F, Cheng Y, Zhao L (2015) Big data in product lifecycle management. Int J Adv Manuf Technol 81:667–684

    Article  Google Scholar 

  27. X. Dong, E. Gabrilovich, et al, Knowledge vault: a web-scale approach to probabilistic knowledge fusion, The 20th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp. 601–610, 2014

  28. He Y, Guo J, Zheng X (2018) From surveillance to digital twin: challenges and recent advances of signal processing for industrial internet of things. IEEE Signal Process Mag 35(5):120–129

    Article  Google Scholar 

  29. Cheng Y, Chen K, Sun H, Zhang Y, Tao F (2018) Data and knowledge mining with big data towards smart production. J Ind Inf Integr 9:1–13

    Google Scholar 

  30. Madni AM (2014) Expanding stakeholder participation in upfront system engineering through storytelling in virtual worlds. Syst Eng 18:16–27

    Article  Google Scholar 

  31. Madni M, Spraragen M, Madni CC (2014) Exploring and assessing complex systems’ behavior through model-driven storytelling. In: 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 1008–1013

  32. A.A. Krasovsky Handbook on the automatic control theory - M .: Nauka, 1987 .-- 712 p.

  33. Tsypkin Ya.Z. Fundamentals of information theory of identification - M .: Nauka, 1984 .-- 320 p.

  34. Dassault Systemes, “3DEXPERIENCE”, https://www.3ds.com/ru/produkty-i-uslugi/3dexperience/

  35. ANSYS, “ANSYS Twin Builder”, https://cae-expert.ru/product/ansys-twin-builder

  36. Oracle, “Oracle cloud”, https://www.oracle.com/cloud/

  37. SAP SE, “SAP Cloud Platform”, https://www.sap.com/cis/products/cloud-platform.html

  38. Bosch, Bosch IoT Suite, https://www.bosch-iot-suite.com/capabilities-bosch-iot-suite/

  39. Microsoft, “Azure Digital Twins”, https://azure.microsoft.com/ru-ru/services/digital-twins/

  40. General Electric, “Predix”, https://www.ge.com/digital/iiot-platform

  41. Siemens, “Siemens PLM Software”, https://www.plm.automation.siemens.com/global/en/

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Acknowledgements

The authors express their gratitude to the Russian Science Foundation, through a grant of which (Project № 20-19-00299) the present study was carried out.

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Russian Science Foundation (Project № 20-19-00299)

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Correspondence to Marina Brovkova.

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Brovkova, M., Molodtsov, V. & Bushuev, V. Implementation specifics and application potential of digital twins of technological systems. Int J Adv Manuf Technol 117, 2279–2286 (2021). https://doi.org/10.1007/s00170-021-07141-z

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