Skip to main content
SpringerLink
Log in

Digital Twins: Benefits, Applications and Development Process

  • Conference paper
  • First Online:
Progress in Artificial Intelligence (EPIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14116))

Included in the following conference series:

  • 486 Accesses

Abstract

Digital twin technology has gained considerable traction in recent years, with diverse applications spanning multiple sectors. However, due to the inherent complexity and substantial costs associated with constructing digital twins, systematic development methodologies are essential for fully capitalizing on their benefits. Therefore, this paper firstly provides an exhaustive synthesis of related literature, highlighting: (1) ten core advantages of implementing digital twin technology; (2) five primary domains in which digital twin applications have been prevalently employed; and (3) ten principal objectives of digital twin applications. Subsequently, we propose a seven-step digital twin application development process, encompassing: (i) Digital Twin Purposing; (ii) Digital Twin Scoping; (iii) Physical Twin Modeling; (iv) Calibration and Validation; (v) Application Logic Development; (vi) External System Integration; and (vii) Deployment and Operation. This structured approach aims to demystify the intrinsic complexity of twinned systems, ensuring that the deployment of digital twin-based solutions effectively addresses the target problem while maximizing the derived benefits.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gelernter, D.: Mirror worlds—or the day software puts the universe in a shoehox. (1991)

    Google Scholar 

  2. Grieves, M.W.: Virtually intelligent product systems: digital and physical twins. In: Complex systems engineering: theory and practice, pp. 175–200. American Institute of Aeronautics and Astronautics, Inc (2002)

    Google Scholar 

  3. 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 2012

    Google Scholar 

  4. Semeraro, C., et al.: Digital twin paradigm: A systematic literature review. Comput. Ind. 130, 103469 (2021)

    Article  Google Scholar 

  5. Lehner, D., et al.: AML4DT: A model-driven framework for developing and maintaining digital twins with automation ML. In: 2021 26th IEEE international conference on emerging technologies and factory automation (ETFA). pp. 1–8 (2021)

    Google Scholar 

  6. Kannan, K., Arunachalam, N.: A digital twin for grinding wheel: an information sharing platform for sustainable grinding process. J. Manuf. Sci. Eng.-Trans. ASME 141, 14 (2019)

    Article  Google Scholar 

  7. Warke, V., et al.: Sustainable development of smart manufacturing driven by the digital twin framework: a statistical analysis. Sustainability 13, 49 (2021)

    Article  Google Scholar 

  8. Mudassar, R., et al.: Digital twin-based smart manufacturing system for project-based organizations: A conceptual framework. In: Proceedings of international conference on computers and industrial engineering, CIE. Wuhan, China (2019)

    Google Scholar 

  9. Zhang, J., et al.: Bi-level dynamic scheduling architecture based on service unit digital twin agents. J. Manuf. Syst. 60, 59–79 (2021)

    Article  Google Scholar 

  10. Lim, K.Y.H., et al.: Digital twin architecture and development trends on manufacturing topologies. In: Intelligent systems reference library, pp. 259–286. Singapore (2021)

    Google Scholar 

  11. Süve, M.F., et al.: Predictive maintenance framework for production environments using digital twin. In: Lecture notes in networks and systems. Istanbul, Turkey (2022)

    Google Scholar 

  12. Leung, E.K.H., Lee, C.K.H., Ouyang, Z.: From traditional warehouses to Physical Internet hubs: A digital twin-based inbound synchronization framework for PI-order management. Int. J. Prod. Econ., 244, (2022)

    Google Scholar 

  13. Zhang, C., et al.: A data- and knowledge-driven framework for digital twin manufacturing cell. In: 11th CIRP conference on industrial product-service systems. Elsevier, Xian, Peoples R China (2019)

    Google Scholar 

  14. Rafsanjani, H.N., Nabizadeh, A.H.: Towards digital architecture, engineering, and construction (AEC) industry through virtual design and construction (VDC) and digital twin. Energy Built Environ., (2021)

    Google Scholar 

  15. Renaud, G., Liao, M., Bombardier, Y.: Demonstration of an airframe digital twin framework using a CF-188 full-scale component test. Lect. Notes Mech. Eng., 176–186 (2020)

    Google Scholar 

  16. Chevallier, Z., Finance, B., Boulakia, B.C.: A reference architecture for smart building digital twin. In: CEUR workshop proceedings. Nanterre, France (2020)

    Google Scholar 

  17. Li, X., et al.: Sustainable business model based on digital twin platform network: the inspiration from Haier’s case study in China. Sustainability 12, 26 (2020)

    Google Scholar 

  18. Camacho, F.D., et al.: Validation through a digital twin of a Stewart platform with irregular geometry with 6 DOF for simulation of a transport vehicle. In: 16th IEEE international conference on automation science and engineering (CASE). IEEE, Sangolqui, Ecuador (2020)

    Google Scholar 

  19. Guo, D.Q., et al.: A framework for personalized production based on digital twin, blockchain and additive manufacturing in the context of Industry 4.0. In: 16th IEEE international conference on automation science and engineering (CASE). IEEE, Guangdong, Peoples R China (2020)

    Google Scholar 

  20. Khan, A., et al.: Toward smart manufacturing using spiral digital twin framework and twinchain. IEEE Trans. Industr. Inf. 18, 1359–1366 (2022)

    Article  Google Scholar 

  21. Mourtzis, D., et al.: Equipment design optimization based on digital twin under the framework of zero-defect manufacturing. In: 2nd international conference on industry 4.0 and smart manufacturing (ISM). Elsevier Science Bv, Rion, Greece (2020)

    Google Scholar 

  22. Li, X.X., et al.: Framework for manufacturing-tasks semantic modelling and manufacturing-resource recommendation for digital twin shop-floor. J. Manuf. Syst. 58, 281–292 (2021)

    Article  Google Scholar 

  23. Deac, G.C., et al.: Machine vision inmanufacturing processes and the digital twin ofmanufacturing architectures. In: Annals of DAAAM and proceedings of the international daaam symposium. (2017)

    Google Scholar 

  24. Scime, L., Singh, A., Paquit, V.: A scalable digital platform for the use of digital twins in additive manufacturing. Manuf. Lett., (2021)

    Google Scholar 

  25. Qamsane, Y., et al.: A unified digital twin framework for real-time monitoring and evaluation of smart manufacturing systems. In: 15th IEEE international conference on automation science and engineering (IEEE CASE). IEEE, USA (2019)

    Google Scholar 

  26. Friederich, J., et al.: A framework for data-driven digital twins for smart manufacturing. Comput. Ind., 136, (2022)

    Google Scholar 

  27. Gopalakrishnan, S., Hartman, N.W., Sangid, M.D.: Model-based feature information network (MFIN): A digital twin framework to integrate location-specific material behavior within component design, manufacturing, and performance analysis. Integr. Mater. Manuf. Innov. 9, 394–409 (2020)

    Article  Google Scholar 

  28. Nie, Q.W., et al.: A multi-agent and internet of things framework of digital twin for optimized manufacturing control. Int. J. Comput. Integr. Manuf., 22

    Google Scholar 

  29. Göppert, A., et al.: Pipeline for ontology-based modeling and automated deployment of digital twins for planning and control of manufacturing systems. J. Intell. Manuf., (2021)

    Google Scholar 

  30. Zhang, G., et al.: An architecture based on digital twins for smart power distribution system. In: 2020 3rd international conference on artificial intelligence and big data (ICAIBD). pp. 29–33 (2020)

    Google Scholar 

  31. Niu, W., et al.: Power grid planning framework and application prospects based on digital twin. In: The 10th renewable power generation conference (RPG 2021). pp. 672–677 (2021)

    Google Scholar 

  32. Zhang, M., et al.: Equipment energy consumption management in digital twin shop-floor: a framework and potential applications. In: 15th IEEE international conference on networking, sensing and control (ICNSC). IEEE, Beijing, Peoples R China (2018)

    Google Scholar 

  33. Aliyu, H.O., et al.: Digital twin framework for holistic and prognostic analysis of the Nigerian electricity supply industry: A proposal. In: 5th IEEE annual international conference on information communications technology and society (ICTAS). IEEE, Minna, Nigeria (2021)

    Google Scholar 

  34. Yu, Q., et al.: Research of digital twin in power system optimization-take offshore platform for example. In: IET conference publications. Beijing, China (2020)

    Google Scholar 

  35. Wu, J., et al.: Research and design of a digital twin-based enterprise architecture digital control platform for provincial electrical power company. In: 2021 6th international conference on control, robotics and cybernetics (CRC). (2021)

    Google Scholar 

  36. Zhang, H., et al.: Hybrid data-physics based digital twin modeling framework for the power system of bobsleigh and tobogganing venue for Beijing winter Olympics. In: 2021 6th international conference on power and renewable energy (ICPRE). (2021)

    Google Scholar 

  37. Perabo, F., et al.: Digital twin modelling of ship power and propulsion systems: application of the open simulation platform (OSP). In: IEEE 29th international symposium on industrial electronics (ISIE). IEEE, Trondheim, Norway (2020)

    Google Scholar 

  38. Chen, C., et al.: A conceptual framework for estimating building embodied carbon based on digital twin technology and life cycle assessment. Sustainability 13, (2021)

    Google Scholar 

  39. Clausen, A., et al.: A digital twin framework for improving energy efficiency and occupant comfort in public and commercial buildings. Energy Inform., 4, (2021)

    Google Scholar 

  40. Wang, W., et al.: Digital twin-based framework for green building maintenance system. In: 2020 IEEE international conference on industrial engineering and engineering management (IEEM). pp. 1301–1305 (2020)

    Google Scholar 

  41. Raes, L., et al.: DUET: A framework for building secure and trusted digital twins of smart cities. IEEE Internet Comput., 1 (2021)

    Google Scholar 

  42. Laamarti, F., et al.: An ISO/IEEE 11073 standardized digital twin framework for health and well-being in smart cities. IEEE Access 8, 105950–105961 (2020)

    Article  Google Scholar 

  43. Ruohomaki, T., et al.: Smart city platform enabling digital twin. In: 9th international conference on intelligent systems (IS), pp. 155–161. IEEE, Helsinki, Finland (2018)

    Google Scholar 

  44. Meta, I., et al.: The camp nou stadium as a testbed for city physiology: a modular framework for urban digital twins. Complexity, 2021. (2021)

    Google Scholar 

  45. Belfadel, A., et al.: Towards a digital twin framework for adaptive last mile city logistics. In: 2021 6th international conference on smart and sustainable technologies (SpliTech). pp. 1–6 (2021)

    Google Scholar 

  46. El Azzaoui, A., et al.: Blockchain-based secure digital twin framework for smart healthy city. Lect. Notes Electr. Eng. 716, 107–113 (2021)

    Article  Google Scholar 

  47. Park, K.T., Son, Y.H., Noh, S.D.: The architectural framework of a cyber physical logistics system for digital-twin-based supply chain control. Int. J. Prod. Res. 59, 5721–5742 (2021)

    Article  Google Scholar 

  48. Pan, Y.H., et al.: Digital twin based real-time production logistics synchronization system in a multi-level computing architecture. J. Manuf. Syst. 58, 246–260 (2021)

    Article  Google Scholar 

  49. Marmolejo-Saucedo, J.A.: Digital twin framework for large-scale optimization problems in supply chains: a case of packing problem. Mob. Netw. & Appl., 17

    Google Scholar 

  50. Howard, D.A., et al.: Greenhouse industry 4.0—digital twin technology for commercial greenhouses. Energy Inform., 4(2), 37 (2021)

    Google Scholar 

  51. Clausen, A., et al.: A digital twin framework for improving energy efficiency and occupant comfort in public and commercial buildings. Energy Inform. 4(2), 40 (2021)

    Article  Google Scholar 

  52. Sørensen, J.V., Ma, Z., Jørgensen, B.N.: Potentials of game engines for wind power digital twin development: an investigation of the Unreal Engine. Energy Inform. 5(4), 39 (2022)

    Article  Google Scholar 

  53. Clausen, C.S.B., Ma, Z.G., Jørgensen, B.N.: Can we benefit from game engines to develop digital twins for planning the deployment of photovoltaics? Energy Inform. 5(4), 42 (2022)

    Article  Google Scholar 

  54. Howard, D.A., Ma, Z., Jørgensen, B.N.: Digital twin framework for energy efficient greenhouse industry 4.0. in ambient intelligence—Software and applications. 2021. Springer International Publishing, Cham (2021)

    Google Scholar 

  55. Howard, D.A., Ma, Z., Jørgensen, B.N.: A case study of digital twin for greenhouse horticulture production flow. In: 2022 IEEE 2nd international conference on digital twins and parallel intelligence (DTPI). (2022)

    Google Scholar 

  56. Howard, D.A., et al.: Data architecture for digital twin of commercial greenhouse production. In: 2020 RIVF international conference on computing and communication technologies (RIVF). (2020)

    Google Scholar 

  57. Værbak, M., et al.: Agent-based modelling of demand-side flexibility adoption in reservoir pumping. In: 2019 IEEE sciences and humanities international research conference (SHIRCON). (2019)

    Google Scholar 

Download references

Acknowledgments

The work presented in this paper is part of the Greenhouse Industry 4.0 project, funded by the Danish Energy Agency (EUDP, Project no 64019–0018) and part of the IEA IETS Annex Task XVIII: Digitalization, Artificial Intelligence and Related Technologies for Energy Efficiency and GHG Emissions Reduction in Industry project, funded by EUDP (project number: 134–21010).

Author information

Authors and Affiliations

  1. SDU Center for Energy Informatics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark

    Bo Nørregaard Jørgensen, Daniel Anthony Howard, Christian Skafte Beck Clausen & Zheng Ma

Authors
  1. Bo Nørregaard Jørgensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Anthony Howard
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christian Skafte Beck Clausen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zheng Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zheng Ma .

Editor information

Editors and Affiliations

  1. Lucy Family Institute for Data and Society, Notre Dame, IN, USA

    Nuno Moniz

  2. GECAD, Polytechnic of Porto, Porto, Portugal

    Zita Vale

  3. GRIA—LIACC, University of Azores, Ponta Delgada, Portugal

    José Cascalho

  4. CISUC, University of Coimbra, Coimbra, Portugal

    Catarina Silva

  5. IEETA, University of Aveiro, Aveiro, Portugal

    Raquel Sebastião

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

Jørgensen, B.N., Howard, D.A., Clausen, C.S.B., Ma, Z. (2023). Digital Twins: Benefits, Applications and Development Process. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14116. Springer, Cham. https://doi.org/10.1007/978-3-031-49011-8_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-49011-8_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49010-1

  • Online ISBN: 978-3-031-49011-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation