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.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.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
Boschert S., Rosen R (2016) Digital twin—the simulation aspect. In: Hehenberger P, Bradley D (eds) Mechatronic futures. Springer, Cham, pp 59–74
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
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
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
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
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
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
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
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
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
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
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
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)
Tao F, Cheng Y, Zhang L et al (2017b) Advanced manufacturing systems: socialization characteristics and trends. J Intell Manuf 28(5):1079–1094
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)
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
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
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)
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
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)
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-018-0911-3