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An application framework of digital twin and its case study

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Abstract

With the rapid development of virtual technology and data acquisition technology, digital twin (DT) technology was proposed and gradually become one of the key research directions of intelligent manufacturing. However, the research of DT for product life cycle management is still in the theoretical stage, the application framework and application methods are not clear, and the lack of referable application cases is also a problem. In this paper, the related research and application of DT technology are systematically studied. Then the concept and characteristics of DT are interpreted from both broad sense and narrow sense. On this basis, an application framework of DT for product lifecycle management is proposed. In physical space, the total-elements information perception technology of production is discussed in detail. In the information processing layer, three main function modules, including data storage, data processing and data mapping, are constructed. In virtual space, this paper describes the implementation process of full parametric virtual modeling and the construction idea for DT application subsystems. At last, a DT case of a welding production line is built and studied. Meanwhile, the implementation scheme, application process and effect of this case are detail described to provide reference for enterprises.

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References

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

    Article  Google Scholar 

  • Boschert S., Rosen R (2016) Digital twin—the simulation aspect. In: Hehenberger P, Bradley D (eds) Mechatronic futures. Springer, Cham, pp 59–74

    Google Scholar 

  • Canedo A (2016) Industrial IoT lifecycle via digital twins. In: 2016 International conference on hardware/software codesign and system synthesis (CODES+ISSS), Pittsburgh, PA, pp 1

  • Damm M (2017) Industrie 4.0—an overview. https://sec.ipa.go.jp/users/seminar/seminar_yokohama_20170227-03.pdf. Accessed 20 Nov 2017

  • Glaessgen EH, Stargel D (2012) The digital twin paradigm for future NASA and US Air Force vehicles. In: 53rd Structures, structural dynamics, and materials conference: special session on the digital twin. Honolulu, HI, US pp 1–14. IOP Publishing Physics. https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20120008178.pdf

  • Grieves M (2014) Digital twin: manufacturing excellence through virtual factory replication. White paper. Ameritech Corporation, Chicago

    Google Scholar 

  • Grieves M, Vickers J (2017) Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In: Kahlen FJ, Flumerfelt S, Alves A (eds) Transdisciplinary perspectives on complex systems. Springer, Cham, pp 85–113

    Chapter  Google Scholar 

  • Haupert J, Xenia Klinge, Blocher A (2017a) CPS-Based Manufacturing with Semantic Object Memories and Service Orchestration for Industrie 4.0 Applications. In: Jeschke S, Brecher C, Song H, Rawat D (eds) Industrial internet of things. Springer, Cham, pp 85–113

    Google Scholar 

  • Haupert J, Klinge X, Blocher A (2017b) CPS-based manufacturing with semantic object memories and service orchestration for industries 4.0 applications. Industrial internet of things. Springer International Publishing, Basel, pp 203–229

    Google Scholar 

  • Li C, Mahadevan S, Ling Y et al (2017) Dynamic bayesian network for aircraft wing health monitoring digital twin. AIAA J 55(3):930–941

    Article  Google Scholar 

  • Li XX, He FZ, Li WD (2018) A cloud-terminal-based cyber-physical system architecture for energy efficient machining process optimization. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-018-0832-1

    Article  Google Scholar 

  • Pardo N (2015) Digital and physical come together at PTC live global. http://blogs.ptc.com/2015/06/08/digital-and-physical-come-together-at-ptc-live-global/. Accessed 5 May 2018

  • Reifsnider Kl, Majumdar P (2013) Multiphysics stimulated simulation digital twin methods for fleet management. In: 54th AIAA/ASME/ASCE/AHS/ASC Structures, structural dynamics, and materials conference. https://doi.org/10.2514/6.2013-1578

  • Rodič B (2017) Industry 4.0 and the New Simulation Modelling Paradigm. Organizacija 50(3):193–207. https://doi.org/10.1515/orga-2017-0017

    Article  Google Scholar 

  • Rosen R, von Wichert G, Lo G et al (2015) About the importance of autonomy and digital twins for the future of manufacturing. IFAC-Papers Online 48(3):567–572

    Article  Google Scholar 

  • Schleich B, Anwer N, Mathieu L et al (2017) Shaping the digital twin for design and production engineering. CIRP Ann Manuf Technol 66(1):141–144

    Article  Google Scholar 

  • Siano P, Graditi G, Atrigna M, Piccolo A (2013) Designing and testing decision support and energy management systems for smart homes. J Ambient Intell Hum Comput 4(6):651–661

    Article  Google Scholar 

  • Söderberg R, Wärmefjord K, Carlson JS et al (2017) Toward a Digital Twin for real-time geometry assurance in individualized production. CIRP Ann Manuf Technol 66(1):137–140

    Article  Google Scholar 

  • Stackpole B (2015) Digital twins land a role in product design. http://www.digitaleng.news/de/digital-twins-land-a-role-in-product-design/. Accessed 25 May 2018

  • Tao F, Zhang M, Cheng J et al (2017a) Digital twin workshop: a new paradigm for future workshop. Comput Integr Manuf Syst 23(1):1–9 (in Chinese)

    Google Scholar 

  • Tao F, Cheng Y, Zhang L et al (2017b) Advanced manufacturing systems: socialization characteristics and trends. J Intell Manuf 28(5):1079–1094

    Article  Google Scholar 

  • Tao F, Cheng Y, Cheng J et al (2017c) Theories and technologies for cyber-physical fusion in digital twin shop-floor. Comput Integr Manuf Syst 23(8):1603–1611 (in Chinese)

    Google Scholar 

  • Tuegel EJ, Ingraffea AR, Eason TG et al (2011) Reengineering aircraft structural life prediction using a digital twin. Int J Aerosp Engc. https://doi.org/10.1155/2011/154798

    Article  Google Scholar 

  • Wang J, Wang K, Wang Y et al (2018) Deep Boltzmann machine based condition prediction for smart manufacturing. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-018-0794-3

    Article  Google Scholar 

  • Zhang J, Gao L, Qin W et al (2016) Big-data-driven operational analysis and decision-making methodology in intelligent workshop. Comput Integr Manuf Syst 22(5):1220–1228 (in Chinese)

    Google Scholar 

  • Zhang Z, Wang X, Wang X et al (2018) A simulation-based approach for plant layout design and production planning. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-018-0687-5

    Article  Google Scholar 

  • Zhuang C, Liu J, Xiong H et al (2017) Connotation, architecture and trends of product digital twin. Comput Integr Manuf Syst 23(4):753–768 (in Chinese)

    Google Scholar 

Download references

Acknowledgements

This research is funded by the Shanghai Key lab of Advanced Manufacturing Environment, the National Natural Science Foundation of China (Grant no. 51505286), and joint fund for aerospace science and technology.

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Correspondence to Yu Zheng.

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Zheng, Y., Yang, S. & Cheng, H. An application framework of digital twin and its case study. J Ambient Intell Human Comput 10, 1141–1153 (2019). https://doi.org/10.1007/s12652-018-0911-3

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