Skip to main content
Springer Nature Link
Log in

Handling Uncertainties with and Within Digital Twins

  • Conference paper
  • First Online:
Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future (SOHOMA 2022)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1083))

  • 719 Accesses

Abstract

The Digital Twin (DT) is often used in environments characterized by uncertainty and complexity, where operating conditions are prone to variability based on external and internal factors. Thus, the literature about DT emphasizes the importance, limitations, and absence of uncertainty quantification. However, there is no explicit review discussing uncertainty in complex systems and within the digital twin model. Such an explicit review could improve the conception, construction, and utilization of DT in environments that are both dynamic and stochastic. Thus, this article aims to (1) describe how a DT can help manage uncertainties in a dynamic system, and (2) explain how DT should deal with uncertainties inside the model.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Wagg, D., Worden, K., Barthorpe, R., Gardner, P.: Digital twins: state-of-the-art future directions for modelling and simulation in engineering dynamics applications. ASCE-ASME J. Risk Uncertainty Eng. Syst. Part B Mech. Eng. 6 (2020). https://doi.org/10.1115/1.4046739

  2. Rosen, R., von Wichert, G., Lo, G., Bettenhausen, K.D.: About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine 48(3), 567–572 (2015)

    Article  Google Scholar 

  3. Knapp, G.L., et al.: Building blocks for a digital twin of additive manufacturing. Acta Mater. 135, 390–399 (2017). https://doi.org/10.1016/j.actamat.2017.06.039

    Article  Google Scholar 

  4. Cerrone, A., Hochhalter, J., Heber, G., Ingraffea, A.: On the effects of modeling as-manufactured geometry: toward digital twin. Int. J. Aerosp. Eng. vol. 2014 (2014). https://doi.org/10.1155/2014/439278

  5. Seshadri, B.R., Krishnamurthy, T.: Structural health management of damaged aircraft structures using the digital twin concept (2017). https://doi.org/10.2514/6.2017-1675

  6. Li, C., Mahadevan, S., Ling, Y., Choze, S., Wang, L.: Dynamic bayesian network for aircraft wing health monitoring digital twin. AIAA J. 55, 1–12 (2017). https://doi.org/10.2514/1.J055201

    Article  Google Scholar 

  7. Islavath, S.R., Deb, D., Kumar, H.: Life cycle analysis and damage prediction of a longwall powered support using 3D numerical modelling techniques. Arab. J. Geosci. 12(14), 1–15 (2019). https://doi.org/10.1007/s12517-019-4574-y

    Article  Google Scholar 

  8. Erol, T., Mendi, A.F., Doğan, D.: The digital twin revolution in healthcare. In: 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1–7 (2020). https://doi.org/10.1109/ISMSIT50672.2020.9255249

  9. Karakra, A., Fontanili, F., Lamine, E., Lamothe, J.: HospiT’Win: a predictive simulation-based digital twin for patients pathways in hospital. In: 2019 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 1–4 (2019). https://doi.org/10.1109/BHI.2019.8834534

  10. Patrone, C., Galli, G., Revetria, R.: A state of the art of digital twin and simulation supported by data mining in the healthcare sector. In: Advancing Technology Industrialization Through Intelligent Software Methodologies, Tools and Techniques, pp. 605–615 (2019). https://doi.org/10.3233/FAIA190084

  11. Ullah, A.S.: Modeling and simulation of complex manufacturing phenomena using sensor signals from the perspective of Industry 4.0. Adv. Eng. Inform. 39, 1–13 (2019)

    Google Scholar 

  12. Semeraro, C., Lezoche, M., Panetto, H., Dassisti, M.: Digital twin paradigm: a systematic literature review. Comput. Ind. 130, 103469 (2021). https://doi.org/10.1016/j.compind.2021.103469

    Article  Google Scholar 

  13. Liu, Z., Meyendorf, N., Mrad, N.: The role of data fusion in predictive maintenance using digital twin. AIP Conf. Proc. 1949(1), 020023 (2018). https://doi.org/10.1063/1.5031520

    Article  Google Scholar 

  14. 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). https://doi.org/10.1007/s00170-018-1617-6

    Article  Google Scholar 

  15. Zhang, M., Selic, B., Ali, S., Yue, T., Okariz, O., Norgren, R.: Understanding uncertainty in cyber-physical systems: a conceptual model. In: Wąsowski, A., Lönn, H. (eds.) ECMFA 2016. LNCS, vol. 9764, pp. 247–264. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42061-5_16

    Chapter  Google Scholar 

  16. Schleich, B., Anwer, N., Mathieu, L., Wartz, S.: Shaping the digital twin for design and production engineering, CIRP Ann. 66, 141–144 (2017). https://doi.org/10.1016/j.cirp.2017.04.040

  17. Morse, E., et al.: Tolerancing: managing uncertainty from conceptual design to final product. CIRP Ann. 67(2), 695–717 (2018). https://doi.org/10.1016/j.cirp.2018.05.009

    Article  Google Scholar 

  18. Walker, W.E., et al.: Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integr. Assess. 4(1), 5–17 (2003). https://doi.org/10.1076/iaij.4.1.5.16466

    Article  Google Scholar 

  19. Pistikopoulos, E.N.: Uncertainty in process design and operations. Comput. Chem. Eng. 19, 553–563 (1995). https://doi.org/10.1016/0098-1354(95)87094-6

    Article  Google Scholar 

  20. Zonderland, M.E., Boucherie, R.J., Litvak, N., Vleggeert-Lankamp, C.L.A.M.: Planning and scheduling of semi-urgent surgeries. Health Care Manag Sci. 13(3), 256–267 (2010). https://doi.org/10.1007/s10729-010-9127-6

    Article  Google Scholar 

  21. Milliken, F.J.: Three types of perceived uncertainty about the environment: state effect, and response uncertainty. Acad. Manage. Rev. 12(1), 133–143 (1987). https://doi.org/10.5465/amr.1987.4306502

    Article  Google Scholar 

  22. Bradac, J.J.: Theory comparison: uncertainty reduction, problematic integration, uncertainty management, and other curious constructs. J. Commun. 51(3), 456–476 (2001). https://doi.org/10.1111/j.1460-2466.2001.tb02891.x

    Article  Google Scholar 

  23. Purdy, G.: ISO 31000:2009 - setting a new standard for risk management. Risk Anal. 30(6), 881–886 (2010). https://doi.org/10.1111/j.1539-6924.2010.01442.x

    Article  Google Scholar 

  24. Silva, A.A., Ferreira, F.C.M.: Uncertainty, flexibility and operational performance of companies: modelling from the perspective of managers. Acad. Manag. Rev. 18, 11–38 (2017). https://doi.org/10.1590/1678-69712017/administracao.v18n4p11-38

    Article  Google Scholar 

  25. Lin, L., Bao, H., Dinh, N.: Uncertainty quantification and software risk analysis for digital twins in the nearly autonomous management and control systems: a review. Ann. Nucl. Energy 160, 108362 (2021). https://doi.org/10.1016/j.anucene.2021.108362

    Article  Google Scholar 

  26. Mullins, J., Ling, Y., Mahadevan, S., Sun, L., Strachan, A.: Separation of aleatory and epistemic uncertainty in probabilistic model validation. Reliab. Eng. Syst. Saf. 147, 49–59 (2016). https://doi.org/10.1016/j.ress.2015.10.003

    Article  Google Scholar 

  27. Graves, S.C.: Uncertainty and production planning. In: Kempf, K.G., Keskinocak, P., Uzsoy, R. (eds.) Planning Production and Inventories in the Extended Enterprise. ISORMS, vol. 151, pp. 83–101. Springer, New York (2011). https://doi.org/10.1007/978-1-4419-6485-4_5

    Chapter  Google Scholar 

  28. Angkiriwang, R., Pujawan, I.N., Santosa, B.: Managing uncertainty through supply chain flexibility: reactive vs. proactive approaches. Prod. Manuf. Res. 2(1), 50–70 (2014). https://doi.org/10.1080/21693277.2014.882804

  29. Zhu, S., Fan, W., Yang, S., Pei, J., Pardalos, P.M.: Operating room planning and surgical case scheduling: a review of literature. J. Comb. Optim. 37(3), 757–805 (2018). https://doi.org/10.1007/s10878-018-0322-6

    Article  MATH  Google Scholar 

  30. Escobet, T., Bregon, A., Pulido, B., Puig, V. (eds.): Fault Diagnosis of Dynamic Systems. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17728-7

    Book  Google Scholar 

  31. Kennedy, M., O’Hagan, A.: Bayesian calibration of computer models. J. Roy. Stat. Soc. B 63, 425–464 (2001). https://doi.org/10.1111/1467-9868.00294

    Article  MATH  Google Scholar 

  32. Der Kiureghian, A., Ditlevsen, O.: Aleatory or epistemic? Does it matter? Struct. Saf. 31(2), 105–112 (2009)

    Article  Google Scholar 

  33. Begg, S.H., Welsh, M.B., Bratvold, R.B.: Uncertainty vs. variability: what’s the difference and why is it important? (2014). https://doi.org/10.2118/169850-MS

  34. Agnusdei, G.P., Elia, V., Gnoni, M.G.: A classification proposal of digital twin applications in the safety domain. Comput. Ind. Eng. 154 (2021). https://doi.org/10.1016/j.cie.2021.107137

  35. Bouloiz, H., Garbolino, E., Tkiouat, M., Guarnieri, F.: A system dynamics model for behavioral analysis of safety conditions in a chemical storage unit. Saf. Sci. 58, 32–40 (2013). https://doi.org/10.1016/j.ssci.2013.02.013

    Article  Google Scholar 

  36. Varshney, K.R.: Engineering safety in machine learning. In: 2016 Information Theory and Applications Workshop (ITA), pp. 1–5 (2016). https://doi.org/10.1109/ITA.2016.7888195

  37. Ritto, T.G., Rochinha, F.A.: Digital twin, physics-based model, and machine learning applied to damage detection in structures. Mech. Syst. Signal Process. 155, 107614 (2021). https://doi.org/10.1016/j.ymssp.2021.107614

    Article  Google Scholar 

  38. Karve, P.M., Guo, Y., Kapusuzoglu, B., Mahadevan, S., Haile, M.A.: Digital twin approach for damage-tolerant mission planning under uncertainty. Eng. Fract. Mech. 225, 106766 (2020). https://doi.org/10.1016/j.engfracmech.2019.106766

    Article  Google Scholar 

  39. 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, American Institute of Aeronautics and Astronautics (2017). https://doi.org/10.2514/6.2017-1566

  40. Dröder, K., Bobka, P., Germann, T., Gabriel, F., Dietrich, F.: A machine learning-enhanced digital twin approach for human-robot-collaboration. Procedia CIRP 76, 187–192 (2018). https://doi.org/10.1016/j.procir.2018.02.010

    Article  Google Scholar 

  41. Priyanka, E.B., Thangavel, S., Gao, X.-Z., Sivakumar, N.S.: Digital twin for oil pipeline risk estimation using prognostic and machine learning techniques. J. Ind. Inf. Integr. 100272 (2021). https://doi.org/10.1016/j.jii.2021.100272

  42. Yan, Q., Wang, H., Wu, F.: Digital twin-enabled dynamic scheduling with preventive maintenance using a double-layer Q-learning algorithm. Comput. Oper. Res. 144, 105823 (2022). https://doi.org/10.1016/j.cor.2022.105823

    Article  MATH  Google Scholar 

  43. Zhang, M., Tao, F., Nee, A.Y.C.: Digital Twin Enhanced Dynamic Job-Shop Scheduling. J. Manuf. Syst. 58, 146–156 (2021). https://doi.org/10.1016/j.jmsy.2020.04.008

    Article  Google Scholar 

  44. Negri, E., Pandhare, V., Cattaneo, L., Singh, J., Macchi, M., Lee, J.: Field-synchronized Digital Twin framework for production scheduling with uncertainty. J. Intell. Manuf. 32(4), 1207–1228 (2020). https://doi.org/10.1007/s10845-020-01685-9

    Article  Google Scholar 

  45. Luo, D., Thevenin, S., Dolgui, A.: A digital twin-driven methodology for material resource planning under uncertainties. In: Dolgui, A., Bernard, A., Lemoine, D., vonCieminski, G., Romero, D. (eds.) APMS 2021. IAICT, vol. 630, pp. 321–329. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85874-2_34

    Chapter  Google Scholar 

  46. Ghosh, A.K., Ullah, A.S., Kubo, A.: Hidden Markov model-based digital twin construction for futuristic manufacturing systems. AIEDAM 33(3), 317–331 (2019). https://doi.org/10.1017/S089006041900012X

    Article  Google Scholar 

  47. Iñigo, B., Colinas-Armijo, N., LópezdeLacalle, L.N., Aguirre, G.: Digital twin-based analysis of volumetric error mapping procedures. Precis. Eng. 72, 823–836 (2021). https://doi.org/10.1016/j.precisioneng.2021.07.017

    Article  Google Scholar 

  48. Wang, J., Ye, L., Gao, R.X., Li, C., Zhang, L.: Digital Twin for rotating machinery fault diagnosis in smart manufacturing. Int. J. Prod. Res. 57(12), 3920–3934 (2019). https://doi.org/10.1080/00207543.2018.1552032

    Article  Google Scholar 

  49. Sapronov, A., et al.: Tuning hybrid distributed storage system digital twins by reinforcement learning. Adv. Syst. Sci. Appl. 18(4), Art. no 4 (2018). https://doi.org/10.25728/assa.2018.18.4.660

  50. Cronrath, C., Aderiani, A.R., Lennartson, B.: Enhancing digital twins through reinforcement learning. In: 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), pp. 293–298 (2019). https://doi.org/10.1109/COASE.2019.8842888

  51. Müller, M.S., Jazdi, N., Weyrich, M.: Self-improving models for the intelligent digital twin: towards closing the reality-to-simulation gap. IFAC-PapersOnLine 55(2), 126–131 (2022). https://doi.org/10.1016/j.ifacol.2022.04.181

    Article  Google Scholar 

  52. Alves de Araujo Junior, C.A., et al.: Digital twins of the water cooling system in a power plant based on fuzzy logic. Sensors 21(20) (2021). https://doi.org/10.3390/s21206737

  53. Luo, W., Hu, T., Zhu, W., Tao, F.: Digital twin modeling method for CNC machine tool, p. 4 (2018). https://doi.org/10.1109/ICNSC.2018.8361285

  54. Sleiti, A.K., Kapat, J.S., Vesely, L.: Digital twin in energy industry: proposed robust digital twin for power plant and other complex capital-intensive large engineering systems. Energy Rep. 8, 3704–3726 (2022). https://doi.org/10.1016/j.egyr.2022.02.305

    Article  Google Scholar 

  55. Balta, E.C., Tilbury, D.M., Barton, K.: A Digital twin framework for performance monitoring and anomaly detection in fused deposition modeling. In: 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), Vancouver, BC, Canada, pp. 823–829 (2019). https://doi.org/10.1109/COASE.2019.8843166

  56. Millwater, H., Ocampo, J., Crosby, N.: Probabilistic methods for risk assessment of airframe DT structure. Eng. Fract. Mech. vol. 221 (2019). https://doi.org/10.1016/j.engfracmech.2019.106674

Download references

Author information

Authors and Affiliations

  1. Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, 44000, Nantes, France

    Farah Abdoune & Olivier Cardin

  2. Department of Industrial Engineering, Toulouse University, IMT Mines Albi, Route de Teillet, 81013, Albi Cedex 9, France

    Leah Rifi & Franck Fontanili

Authors
  1. Farah Abdoune
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leah Rifi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Franck Fontanili
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Olivier Cardin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Olivier Cardin .

Editor information

Editors and Affiliations

  1. Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania

    Theodor Borangiu

  2. LAMIH UMR CNRS 8201, Université Polytechnique Hauts de France UPHF, Valenciennes Cedex 9, France

    Damien Trentesaux

  3. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politecnico de Bragança, Bragança, Portugal

    Paulo Leitão

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Abdoune, F., Rifi, L., Fontanili, F., Cardin, O. (2023). Handling Uncertainties with and Within Digital Twins. In: Borangiu, T., Trentesaux, D., Leitão, P. (eds) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2022. Studies in Computational Intelligence, vol 1083. Springer, Cham. https://doi.org/10.1007/978-3-031-24291-5_10

Download citation

Publish with us

Policies and ethics