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Digital Twin and Education in Manufacturing

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The Digital Twin

Abstract

Learning Factories (LFs) enable learning in a factory environment, and – due to the possibility of experiential learning – in manufacturing they are considered the most promising approach to acquire the skills necessary to succeed in the increasingly complex and technologically driven workplace, political, and social arenas of the twenty-first century. Due to the modelling capabilities at the basis of this technology, Digital Twin (DT) can support the implementation of LFs..

In this chapter, the role of DT in manufacturing education is explored through two illustrative examples. Here, the DT technology is utilized to build digital LFs adopted for learning purposes. The first example shows a virtual flow shop that allows students to learn about: (i) Scheduling; (ii) Condition-based Maintenance; (iii) Internet of Things. Whereas in the second example, Virtual Commissioning (VC) is utilized to virtually verify the PLC (Programmable Logic Controller) code before its deployment, allowing students to learn both PLC programming and code verification techniques. The implemented teaching activities were targeted both to students from university and vocational schools. Furthermore, they dealt with different phases of the lifecycle of manufacturing processes. Throughout this chapter, it will be demonstrated that the application of the DT technology to LFs enables the building of a flexible teaching environment that can be customized based on the type of students and the competences that must be taught.

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Notes

  1. 1.

    https://xcelgo.com/experior/

  2. 2.

    Sanchez-Londono, D., Barbieri, G., & Garces, K. (2022). XWare: a Middleware for Smart Retrofitting in Maintenance. IFAC-PapersOnLine, 55(19), 109–114.

  3. 3.

    https://xcelgo.com/more-schools-use-digital-twins/

References

  1. Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019). A survey on digital twin: Definitions, characteristics, applications, and design implications. IEEE Access, 7, 167653–167671.

    Google Scholar 

  2. Liu, M., Shuiliang, F., Huiyue, D., & Cunzhi, X. (2021). Review of digital twin about concepts, technologies, and industrial applications. Journal of Manufacturing Systems, 58, 346–361.

    Google Scholar 

  3. Lu, Y., Chao, L., Kai Wang, I. K., Huiyue, H., & Xun, X. (2020). Digital twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-Integrated Manufacturing, 61, 101837.

    Google Scholar 

  4. Liljaniemi, A., & Paavilainen, H. (2020). Using digital twin technology in engineering education – Course concept to explore benefits and barriers. Open Engineering, 10(1), 377–385.

    Google Scholar 

  5. Nikolaev, S., Gusev, M., Padalitsa, D., Mozhenkov, E., Mishin, S., & Uzhinsky, I. (2018). Implementation of “digital twin” concept for modern project-based engineering education. In IFIP international conference on product lifecycle management.

    Google Scholar 

  6. Rassudov, L., & Korunets, A. (2020). COVID-19 pandemic challenges for engineering education. In XI international conference on electrical power drive systems (ICEPDS).

    Google Scholar 

  7. Sepasgozar, S. M. (2020). Digital twin and web-based virtual gaming technologies for online education: A case of construction management and engineering. Applied Sciences, 10(13), 4678.

    Google Scholar 

  8. Hamid, M. H. M. I., Masrom, M., & Salim, K. R. (2014). Review of learning models for production based education training in technical education. In International conference on teaching and learning in computing and engineering.

    Google Scholar 

  9. Hempen, S., Wischniewski, S., Maschek, T., & Deuse, J. (2010). Experiential learning in academic education: A teaching concept for efficient work system design. In 4th workshop of the special interest group on experimental interactive learning in industrial management.

    Google Scholar 

  10. Plorin, D., & Müller, E. (2013). Developing an ambient assisted living environment applying the advanced learning factory. In International simulation and gaming association conference.

    Google Scholar 

  11. Barbieri, G., Garces, K., Abolghasem, S., Martinez, S., Pinto, M. F., Andrade, G., Castro, F., & Jimenez, F. (2021). An engineering multidisciplinary undergraduate specialty with emphasis in society 5.0. International Journal of Engineering Education, 37(3), 744–760.

    Google Scholar 

  12. Lamancusa, J. S., Zayas, J. L., Soyster, A. L., Morell, L., & Jorgensen, J. (2008). 2006 Bernard M. Gordon Prize Lecture*: The learning factory: Industry‐partnered active learning. Journal of Engineering Education, 97(1), 5–11.

    Google Scholar 

  13. R. S. (1988). Außerbetriebliche CIM-Schulung in der Lernfabrik. In Produktionsforum’88 (pp. 581–601).

    Google Scholar 

  14. Alptekin, S., Pouraghabagher, R., McQuaid, P., & Waldorf, D. (2001). Teaching factory. In Annual conference.

    Google Scholar 

  15. Wagner, U., AlGeddawy, T., ElMaraghy, H., & Mÿller, E. (2012). The state-of-the-art and prospects of learning factories. Procedia CiRP, 3, 109–114.

    Google Scholar 

  16. Sudhoff, M., Prinz, C., & Kuhlenkötter, B. (2020). A systematic analysis of learning factories in Germany-concepts, production processes, didactics. Procedia Manufacturing, 45, 114–120.

    Google Scholar 

  17. Wienbruch, T., Leineweber, S., Kreimeier, D., & Kuhlenkötter, B. (2018). Evolution of SMEs towards Industrie 4.0 through a scenario based learning factory training. Procedia Manufacturing, 23, 141–146.

    Google Scholar 

  18. Abele, E., Metternich, J., Tisch, M., Chryssolouris, G., Sihn, W., ElMaraghy, H., Hummel, V., & Ranz, F. (2015). Learning factories for research, education, and training. Procedia CiRp, 32, 1–6.

    Google Scholar 

  19. Abele, E. (2016). Learning factory. CIRP Encyclopedia of Production Engineering.

    Google Scholar 

  20. Abele, E., Chryssolouris, G., Sihn, W., Metternich, J., ElMaraghy, H., Seliger, G., Sivard, G., ElMaraghy, W., Hummel, V., Tisch, M., & Seifermann, S. (2017). Learning factories for future oriented research and education in manufacturing. CIRP Annals, 66(2), 803–826.

    Google Scholar 

  21. Andrés, M., Álvaro, G., & Julián, M. (2019). Advantages of learning factories for production planning based on shop floor simulation: A step towards smart factories in Industry 4.0. In World conference on engineering education (EDUNINE).

    Google Scholar 

  22. Haghighi, A., Shariatzadeh, N., Sivard, G., Lundholm, T., & Eriksson, Y. (2014). Digital learning factories: Conceptualization, review and discussion. In 6th Swedish production symposium.

    Google Scholar 

  23. Al-Geddawy, T. (2020). A digital twin creation method for an opensource low-cost changeable learning factory. Procedia Manufacturing, 51, 1799–1805.

    Google Scholar 

  24. Protic, A., Jin, Z., Marian, R., Abd, K., Campbell, D., & Chahl, J. (2020). Implementation of a bi-directional digital twin for Industry 4 labs in academia: A solution based on OPC UA. In IEEE international conference on industrial engineering and engineering management (IEEM).

    Google Scholar 

  25. Brenner, B., & Hummel, V. (2017). Digital twin as enabler for an innovative digital shopfloor management system in the ESB Logistics Learning Factory at Reutlingen-University. Procedia Manufacturing, 9, 198–205.

    Google Scholar 

  26. Ralph, B. J., Schwarz, A., & Stockinger, M. (2020). An implementation approach for an academic learning factory for the metal forming industry with special focus on digital twins and finite element analysis. Procedia Manufacturing, 45, 253–258.

    Google Scholar 

  27. Hänggi, R., Nyffenegger, F., Ehrig, F., Jaeschke, P., & Bernhardsgrütter, R. (2020). Smart learning factory–network approach for learning and transfer in a digital & physical set up. In IFIP international conference on product lifecycle management.

    Google Scholar 

  28. Uhlemann, T. H. J., Schock, C., Lehmann, C., Freiberger, S., & Steinhilper, R. (2017). The digital twin: Demonstrating the potential of real time data acquisition in production systems. Procedia Manufacturing, 9, 113–120.

    Google Scholar 

  29. Grube, D., Malik, A. A., & Bilberg, A. (2019). SMEs can touch Industry 4.0 in the smart learning factory. Procedia Manufacturing, 31, 219–224.

    Google Scholar 

  30. Martinez, S., Mariño, A., Sanchez, S., Montes, A. M., Triana, J. M., Barbieri, G., Abolghasem, S., Vera, J., & Guevara, M. (2021). A digital twin demonstrator to enable flexible manufacturing with robotics: A process supervision case study. Production & Manufacturing Research.

    Google Scholar 

  31. Umeda, Y., Ota, J., Shirafuji, S., Kojima, F., Saito, M., Matsuzawa, H., & Sukekawa, T. (2020). Exercise of digital kaizen activities based on ‘digital triplet’ concept. Procedia Manufacturing, 45, 325–330.

    Google Scholar 

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

    Google Scholar 

  33. Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital twin in manufacturing: A categorical literature review and classification. IFAC-Papers Online, 51, 1016–1022.

    Google Scholar 

  34. Biesinger, F., & Weyrich, M. (2019). The facets of digital twins in production and the automotive industry. In 23rd international conference on mechatronics technology (ICMT).

    Google Scholar 

  35. Post, F. H., & Van Walsum, T. (1993). Fluid flow visualization. In Focus on scientific visualization (pp. 1–40). Springer.

    Google Scholar 

  36. Bei, Y., & Fregly, B. J. (2004). Multibody dynamic simulation of knee contact mechanics. Medical Engineering & Physics, 26, 777–789.

    Google Scholar 

  37. Pandolfi, A., & Ortiz, M. (2002). An efficient adaptive procedure for three-dimensional fragmentation simulations. Engineering with Computers, 18, 148–159.

    Google Scholar 

  38. Schiehlen, W. (1997). Multibody system dynamics: Roots and perspectives. Multibody System Dynamics, 1, 149–188.

    MathSciNet  MATH  Google Scholar 

  39. Hübner, B., Walhorn, E., & Dinkler, D. (2004). A monolithic approach to fluid–structure interaction using space–time finite elements. Computer Methods in Applied Mechanics and Engineering, 193, 2087–2104.

    MATH  Google Scholar 

  40. O’Brien, J. S., Julien, P. Y., & Fullerton, W. T. (1993). Two-dimensional water flood and mudflow simulation. Journal of Hydraulic Engineering, 119, 244–261.

    Google Scholar 

  41. Sherman, W. (2003). Understanding virtual reality: Interface, application, and design. Morgan Kaufmann.

    Google Scholar 

  42. Soete, N., Claeys, A., Hoedt, S., Mahy, B., & Cottyn, J. (2015). Towards mixed reality in SCADA applications. IFAC-Papers Online, 48, 2417–2422.

    Google Scholar 

  43. Havard, V., Jeanne, B., Lacomblez, M., & Baudry, D. (2019). Digital twin and virtual reality: A co-simulation environment for design and assessment of industrial workstations. Production & Manufacturing Research, 7, 472–489.

    Google Scholar 

  44. Wursthorn, S., Coelho, A. H., & Staub, G. (2004). Applications for mixed reality. In XXth ISPRS congress, Istanbul, Turkey.

    Google Scholar 

  45. Cipresso, P., Giglioli, I. A. C., Raya, M. A., & Riva, G. (2018). The past, present, and future of virtual and augmented reality research: A network and cluster analysis of the literature. Frontiers in Psychology, 9, 2086.

    Google Scholar 

  46. Matuszka, T., Gombos, G., & Kiss, A. (2013). A new approach for indoor navigation using semantic webtechnologies and augmented reality. In International conference on virtual, augmented and mixed reality.

    Google Scholar 

  47. Barbieri, G., Bertuzzi, A., Capriotti, A., Ragazzini, L., Gutierrez, D., Negri, E., & Fumagalli, L. (2021). A virtual commissioning based methodology to integrate digital twins into manufacturing systems. Production Engineering, 15, 397–412.

    Google Scholar 

  48. Pinedo, M. (2016). Scheduling: Theory, algorithms, and systems. Springer.

    MATH  Google Scholar 

  49. Mitchell, M. (1998). An introduction to genetic algorithms. MIT Press.

    MATH  Google Scholar 

  50. Li, R., Verhagen, W. J., & Curran, R. (2020). A systematic methodology for prognostic and health management system architecture definition. Reliability Engineering & System Safety, 193, 106598.

    Google Scholar 

  51. Barbieri, G., Sanchez-Londoño, D., Cattaneo, L., Fumagalli, L., & Romero, D. (2020). A case study for problem-based learning education in fault diagnosis assessment. IFAC-Papers Online, 53, 107–112.

    Google Scholar 

  52. Borgia, E. (2014). The internet of things vision: Key features, applications and open issues. Computer Communications, 54, 1–31.

    Google Scholar 

  53. Romero, N., Medrano, R., Garces, K., Sanchez-Londono, D., & Barbieri, G. (2021). XRepo 2.0: A big data information system for education in prognostics and health management. International Journal of Prognostics and Health Management, 12.

    Google Scholar 

  54. Ardila, A., Martinez, F., Garces, K., Barbieri, G., Sanchez-Londono, D., Caielli, A., Cattaneo, L., & Fumagalli, L. (2020). XRepo-towards an information system for prognostics and health management analysis. Procedia Manufacturing, 42, 146–153.

    Google Scholar 

  55. Lee, C. G., & Park, S. C. (2014). Survey on the virtual commissioning of manufacturing systems. Journal of Computational Design and Engineering, 1(3), 213–222.

    Google Scholar 

  56. Hofmann, W., Langer, S., Lang, S., & Reggelin, T. (2017). Integrating virtual commissioning based on high level emulation into logistics education. Procedia Engineering, 178, 24–32.

    Google Scholar 

  57. Mortensen, S. T., & Madsen, O. (2018). A virtual commissioning learning platform. Procedia Manufacturing, 23, 93–98.

    Google Scholar 

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Correspondence to Giacomo Barbieri .

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Barbieri, G., Sanchez-Londoño, D., Gutierrez, D.A., Vigon, R., Negri, E., Fumagalli, L. (2023). Digital Twin and Education in Manufacturing. In: Crespi, N., Drobot, A.T., Minerva, R. (eds) The Digital Twin. Springer, Cham. https://doi.org/10.1007/978-3-031-21343-4_35

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  • DOI: https://doi.org/10.1007/978-3-031-21343-4_35

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