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Digital Twins Approach for Sustainable Industry

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Advanced Information Systems Engineering Workshops (CAiSE 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 451))

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Abstract

Sustainable industry is a part of The European Green Deal, which aims to achieve the EU’s climate and environmental goals based on the circular economy. Digital twins are important technologies for realizing industry 4.0 and related sectors. In this paper, we looked at building the DTs for manufacturing, healthcare and construction industrial sectors in Industry 4.0 architecture to realize a sustainable industry.

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References

  1. EU Homepage. https://ec.europa.eu/info/research-and-innovation/research-area/industrial-research-and-innovation/industry-50_en. Accessed 14 Mar 2022

  2. 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, pp. 85–113. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-38756-7_4

    Chapter  Google Scholar 

  3. Fuller, A., Fan, Z., Day, C., Barlow, C.: Digital twin: enabling technologies, challenges and open research. IEEE Access 8, 108952–108971 (2020)

    Article  Google Scholar 

  4. He, B., Bai, K.J.: Digital twin-based sustainable intelligent manufacturing: a review. Adv. Manuf. 9(1), 1–21 (2021)

    Article  Google Scholar 

  5. Croatti, A., Gabellini, M., Montagna, S., Ricci, A.: On the integration of agents and digital twins in healthcare. J. Med. Syst. 44(9), 1–8 (2020)

    Article  Google Scholar 

  6. YingLiu, L.Z., et al.: A novel cloud-based framework for the elderly healthcare services using digital twin. IEEE Access 7(2019), 49088–49101 (2019)

    Google Scholar 

  7. Opoku, D.G.J., Perera, S., Osei-Kyei, R., Rashidi, M.: Digital twin application in the construction industry: a literature review. J. Build. Eng. 40, 102726 (2021)

    Article  Google Scholar 

  8. Sacks, R., Brilakis, I., Pikas, E., Xie, H.S., Girolami, M.: Construction with digital twin information systems. Data-Centric Engineering, 1 (2020)

    Google Scholar 

  9. Cimino, C., Negri, E., Fumagalli, L.: Review of digital twin applications in manufacturing. Comput. Ind. 113, 103130 (2019)

    Article  Google Scholar 

  10. Tao, F., Zhang, H., Liu, A., Nee, A.Y.: Digital twin in industry: state-of-the-art. IEEE Trans. Industr. Inf. 15(4), 2405–2415 (2018)

    Article  Google Scholar 

  11. Tao, F., Zhang, M., Nee, A.Y.C.: Digital Twin Driven Smart Manufacturing. Academic Press, Cambridge (2019)

    Google Scholar 

  12. 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 (2017). https://doi.org/10.1007/s00170-017-0233-1

    Article  Google Scholar 

  13. Luo, W., Hu, T., Zhang, C., Wei, Y.: Digital twin for CNC machine tool: modeling and using strategy. J. Ambient. Intell. Humaniz. Comput. 10(3), 1129–1140 (2018). https://doi.org/10.1007/s12652-018-0946-5

    Article  Google Scholar 

  14. Liu, C., Vengayil, H., Lu, Y., Xu, X.: A cyber-physical machine tools platform using OPC UA and MTConnect. J. Manuf. Syst. 51, 61–74 (2019)

    Article  Google Scholar 

  15. Havard, V., Jeanne, B., Lacomblez, M., Baudry, D.: Digital twin and virtual reality: a co-simulation environment for design and assessment of industrial workstations. Prod. Manuf. Res. 7(1), 472–489 (2019)

    Google Scholar 

  16. Söderberg, R., Wärmefjord, K., Carlson, J.S., Lindkvist, L.: toward a digital twin for real-time geometry assurance in individualized production. CIRP Ann. 66(1), 137–140 (2017)

    Article  Google Scholar 

  17. Zhang, H., Liu, Q., Chen, X., Zhang, D., Leng, J.: A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. IEEE Access 5, 26901–26911 (2017)

    Article  Google Scholar 

  18. Fan, Y., et al.: A digital-twin visualized architecture for flexible manufacturing system. J. Manuf. Syst. 60, 176–201 (2021)

    Article  Google Scholar 

  19. Lu, Y., Liu, C., Kevin, I., Wang, K., Huang, H., Xu, X.: Digital Twin-driven smart manufacturing: connotation, reference model, applications and research issues. Robot. Comput-Integr. Manuf. 61, 101837 (2020)

    Article  Google Scholar 

  20. Rivera, L.F., Jiménez, M., Angara, P., Villegas, N.M., Tamura, G., Müller, H.A.: Towards continuous monitoring in personalized healthcare through digital twins. In: Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering, pp. 329–335 (2019)

    Google Scholar 

  21. Elayan, H., Aloqaily, M., Guizani, M.: Digital twin for intelligent context-aware IoT healthcare systems. IEEE Internet Things J. 8(23), 16749–16757 (2021)

    Article  Google Scholar 

  22. Lin, Y.C.P., Cheung, W.F.: Developing WSN/BIM-Based environmental monitoring management system for parking garages in smart cities, J. Manag. Eng. 36(3), 04020012 (2020)

    Google Scholar 

  23. Lu, Q., Chen, L., Li, S., Pitt, M.: Semi-automatic geometric digital twinning for existing buildings based on images and CAD drawings, Autom. ConStruct. 115, 103183 (2020)

    Google Scholar 

  24. Machado, C.G., Winroth, M.P., Ribeiro da Silva, E.H.D.: Sustainable manufacturing in Industry 4.0: an emerging research agenda. Int. J. Prod. Res. 58(5), 1462–1484 (2020)

    Google Scholar 

  25. CSREUROPE. https://www.csreurope.org/our-campaign. Accessed 14 Mar 2022

  26. Waibel, M.W., Steenkamp, L.P., Moloko, N., Oosthuizen, G.A.: Investigating the effects of smart production systems on sustainability elements. Procedia Manuf. 8, 731–737 (2017)

    Article  Google Scholar 

  27. Stock, T., Seliger, G.: Opportunities of sustainable manufacturing in industry 4.0. Procedia CIRP 40, 536–541 (2016)

    Google Scholar 

  28. FIWARE: FIWARE Components. https://www.fiware.org/developers/catalogue/. Accessed 14 Mar 2022

  29. Sang, G.M., Lai, X., Vrieze, P., Bai, Y.: Towards predictive maintenance for flexible manufacturing using FIWARE. In: Dupuy-Chessa, S., Proper, H.A. (eds.) CAiSE 2020. LNBIP, vol. 382, pp. 17–28. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49165-9_2

    Chapter  Google Scholar 

  30. Sang, G.M., Xu, L., de Vrieze, P.: A predictive maintenance model for flexible manufacturing in the context of industry 4.0. Frontiers in big Data 4, 1–23 (2021)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computing and Informatics, Bournemouth University, Poole, Bournemouth, B12 5BB, UK

    Lai Xu, Paul de Vrieze & Xin Lu

  2. School of Engineering Science, University of Skövde, 54128, Skövde, Sweden

    Wei Wang

Authors
  1. Lai Xu
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  2. Paul de Vrieze
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  3. Xin Lu
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  4. Wei Wang
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Corresponding author

Correspondence to Lai Xu .

Editor information

Editors and Affiliations

  1. Chalmers | University of Gothenburg, Gothenburg, Sweden

    Jennifer Horkoff

  2. KU Leuven, Brussels, Belgium

    Estefania Serral

  3. Stockholm University, Stockholm, Sweden

    Jelena Zdravkovic

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Xu, L., de Vrieze, P., Lu, X., Wang, W. (2022). Digital Twins Approach for Sustainable Industry. In: Horkoff, J., Serral, E., Zdravkovic, J. (eds) Advanced Information Systems Engineering Workshops. CAiSE 2022. Lecture Notes in Business Information Processing, vol 451. Springer, Cham. https://doi.org/10.1007/978-3-031-07478-3_11

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  • DOI: https://doi.org/10.1007/978-3-031-07478-3_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07477-6

  • Online ISBN: 978-3-031-07478-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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