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Challenges in Digital Twin Development for Cyber-Physical Production Systems

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Cyber Physical Systems. Model-Based Design (CyPhy 2018, WESE 2018)

Abstract

The recent advancement of information and communication technology makes digitalisation of an entire manufacturing shop-floor possible where physical processes are tightly intertwined with their cyber counterparts. This led to an emergence of a concept of digital twin, which is a realistic virtual copy of a physical object. Digital twin will be the key technology in Cyber-Physical Production Systems (CPPS) and its market is expected to grow significantly in the coming years. Nevertheless, digital twin is still relatively a new concept that people have different perspectives on its requirements, capabilities, and limitations. To better understand an effect of digital twin’s operations, mitigate complexity of capturing dynamics of physical phenomena, and improve analysis and predictability, it is important to have a development tool with a strong semantic foundation that can accurately model, simulate, and synthesise the digital twin. This paper reviews current state-of-art on tools and developments of digital twin in manufacturing and discusses potential design challenges.

This work was supported by Delta-NTU Corporate Lab for Cyber-Physical Systems with funding support from Delta Electronics Inc. and the National Research Foundation (NRF) Singapore under the Corp Lab@University Scheme.

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References

  1. Alam, K.M., El Saddik, A.: C2PS: a digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access 5, 2050–2062 (2017)

    Article  Google Scholar 

  2. Alur, R., Dill, D.: The theory of timed automata. In: de Bakker, J.W., Huizing, C., de Roever, W.P., Rozenberg, G. (eds.) REX 1991. LNCS, vol. 600, pp. 45–73. Springer, Heidelberg (1992). https://doi.org/10.1007/BFb0031987

    Chapter  Google Scholar 

  3. Berry, G., Gonthier, G.: The ESTEREL synchronous programming language: design, semantics. Implement. Sci. Comput. Program. 19(2), 87–152 (1992)

    Article  Google Scholar 

  4. Bliudze, S., Furic, S., Sifakis, J., Viel, A.: Rigorous design of cyber-physical systems. Softw. Syst. Model. 1–24 (2017)

    Google Scholar 

  5. Blochwitz, T., et al.: The functional mockup interface for tool independent exchange of simulation models. In: Proceedings of the 8th International Modelica Conference, Technical University, Dresden, Germany, pp. 105–114. No. 063, Linköping University Electronic Press (2011)

    Google Scholar 

  6. Brooks, C., et al.: Heterogeneous concurrent modeling and design in Java. Introduction to Ptolemy II, vol. 1. Technical report, Department of Electrical Engineering and Computer Science, California University, Berkeley (2008)

    Google Scholar 

  7. Fehling, R.: A concept of hierarchical Petri nets with building blocks. In: Rozenberg, G. (ed.) ICATPN 1991. LNCS, vol. 674, pp. 148–168. Springer, Heidelberg (1993). https://doi.org/10.1007/3-540-56689-9_43

    Chapter  Google Scholar 

  8. Fritzson, P.: Principles of Object-Oriented Modeling and Simulation with Modelica 2.1. Wiley (2010)

    Book  Google Scholar 

  9. Generic Electric: Digital Wind Farm. https://www.ge.com/renewableenergy/wind-energy/technology/digital-wind-farm

  10. Glaessgen, E., Stargel, D.: The digital twin paradigm for future NASA and US air force vehicles. In: 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, p. 1818 (2012)

    Google Scholar 

  11. Grieves, M., Vickers, J.: Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Kahlen, F.-J., Flumerfelt, S., Alves, A. (eds.) Transdisciplinary Perspectives on Complex Systems. LNCS, pp. 85–113. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-38756-7_4

    Chapter  Google Scholar 

  12. Henzinger, T.A.: The theory of hybrid automata. In: Inan, M.K., Kurshan, R.P. (eds.) Verification of Digital and Hybrid Systems, pp. 265–292. Springer, Heidelberg (2000). https://doi.org/10.1007/978-3-642-59615-5_13

    Chapter  Google Scholar 

  13. Hoare, C.A.R., Jifeng, H.: Unifying Theories of Programming, vol. 14. Prentice Hall Englewood Cliffs, Upper Saddle River (1998)

    MATH  Google Scholar 

  14. Jensen, K., Kristensen, L.M.: Colored petri nets: a graphical language for formal modeling and validation of concurrent systems. Commun. ACM 58(6), 61–70 (2015)

    Article  Google Scholar 

  15. Kagermann, H., Helbig, J., Hellinger, A., Wahlster, W.: Recommendations for implementing the strategic initiative INDUSTRIE 4.0: securing the future of German manufacturing industry. Final report of the Industrie 4.0 working group. Forschungsunion (2013)

    Google Scholar 

  16. Larsen, P.G., et al.: Integrated tool chain for model-based design of cyber-physical systems: the INTO-CPS project. In: 2016 2nd International Workshop on Modelling, Analysis, and Control of Complex CPS (CPS Data), pp. 1–6. IEEE (2016)

    Google Scholar 

  17. Lee, E.A.: Cyber physical systems: design challenges. In: 2008 11th IEEE International Symposium on Object Oriented Real-Time Distributed Computing (ISORC), pp. 363–369. IEEE (2008)

    Google Scholar 

  18. Li, C., Mahadevan, S., Ling, Y., Wang, L., Choze, S.: A dynamic Bayesian network approach for digital twin. In: 19th AIAA Non-deterministic Approaches Conference, p. 1566 (2017)

    Google Scholar 

  19. Lopez, F., Shao, Y., Mao, Z.M., Moyne, J., Barton, K., Tilbury, D.: A software-defined framework for the integrated management of smart manufacturing systems. Manuf. Lett. 15, 18–21 (2017)

    Article  Google Scholar 

  20. MacDonald, C., Dion, B., Davoudabadi, M.: Creating a digital twin for a pump. ANSYS Advant. Issue 1, 8 (2017)

    Google Scholar 

  21. Magargle, R., et al.: A simulation-based digital twin for model-driven health monitoring and predictive maintenance of an automotive braking system. In: Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, 15–17 May, pp. 35–46. No. 132, Linköping University Electronic Press (2017)

    Google Scholar 

  22. Malik, A., Salcic, Z., Roop, P.S., Girault, A.: SystemJ: a GALS language for system level design. Comput. Lang. Syst. Struct. 36(4), 317–344 (2010)

    Google Scholar 

  23. MarketsandMarkets: Digital twin market by end user (Aerospace and Defense, Automotive and Transportation, Home and Commercial, Electronics and Electricals/Machine Manufacturing, Energy and Utilities, Healthcare, Retail and Consumer Goods), and Geography (August 2017). http://www.reportsnreports.com/reports/1175159-digital-twin-market-by-end-user-aerospace-defense-automotive-transportation-home-commercial-electronics-electricals-machine-manufacturing-energy-utilities-healthcare-retail-consumer-goods-and-ge-st-to-2023.html

  24. Moreno, A., Velez, G., Ardanza, A., Barandiaran, I., de Infante, Á.R., Chopitea, R.: Virtualisation process of a sheet metal punching machine within the industry 4.0 vision. Int. J. Interact. Des. Manuf. (IJIDeM) 11(2), 365–373 (2017)

    Article  Google Scholar 

  25. Negri, E., Fumagalli, L., Macchi, M.: A review of the roles of digital twin in CPS-based production systems. Procedia Manuf. 11, 939–948 (2017)

    Article  Google Scholar 

  26. Peterson, J.L.: Petri Net Theory and the Modeling of Systems. Prentice Hall PTR, Upper Saddle River (1981)

    MATH  Google Scholar 

  27. Potok, M., Chen, C.Y., Mitra, S., Mohan, S.: SDCWorks: a formal framework for software defined control of smart manufacturing systems. In: Proceedings of the 9th ACM/IEEE International Conference on Cyber-Physical Systems, pp. 88–97. IEEE Press (2018)

    Google Scholar 

  28. Qi, Q., Tao, F.: Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access 6, 3585–3593 (2018)

    Article  Google Scholar 

  29. Ramchandani, C.: Analysis of asynchronous concurrent systems by Petri Nets. Technical report, Massachusetts Institute of Technology, Cambridge, Project MAC (1974)

    Google Scholar 

  30. Scaglioni, B., Ferretti, G.: Towards digital twins through object-oriented modelling: a machine tool case study. IFAC-PapersOnLine 613–618 (2018)

    Article  Google Scholar 

  31. Schluse, M., Priggemeyer, M., Atorf, L., Rossmann, J.: Experimentable digital twins-streamlining simulation-based systems engineering for industry 4.0. IEEE Trans. Ind. Inf. 14(4), 1722–1731 (2018)

    Article  Google Scholar 

  32. Siemens: Digital twins bring real-life success. https://www.siemens.com/global/en/home/markets/machinebuilding/references/bausch-stroebel.html

  33. Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui, F.: Digital twin-driven product design, manufacturing and service with big data. Int. J. Adv. Manuf. Technol. 94(9–12), 3563–3576 (2018)

    Article  Google Scholar 

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

    Article  Google Scholar 

  35. Tripakis, S.: Data-driven and model-based design. In: 2018 IEEE Industrial Cyber-Physical Systems (ICPS), pp. 103–108. IEEE (2018)

    Google Scholar 

  36. Vaandrager, F.: Model learning. Commun. ACM 60(2), 86–95 (2017)

    Article  Google Scholar 

  37. Wang, X.V., Kemény, Z., Váncza, J., Wang, L.: Human-robot collaborative assembly in cyber-physical production: classification framework and implementation. CIRP Ann. 66(1), 5–8 (2017)

    Article  Google Scholar 

  38. Zhuang, C., Liu, J., Xiong, H.: Digital twin-based smart production management and control framework for the complex product assembly shop-floor. Int. J. Adv. Manuf. Technol. 96(1–4), 1149–1163 (2018)

    Article  Google Scholar 

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Correspondence to Arvind Easwaran .

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Park, H., Easwaran, A., Andalam, S. (2019). Challenges in Digital Twin Development for Cyber-Physical Production Systems. In: Chamberlain, R., Taha, W., Törngren, M. (eds) Cyber Physical Systems. Model-Based Design. CyPhy WESE 2018 2018. Lecture Notes in Computer Science(), vol 11615. Springer, Cham. https://doi.org/10.1007/978-3-030-23703-5_2

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  • DOI: https://doi.org/10.1007/978-3-030-23703-5_2

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