Digital replica of a living or non-living physical entity
"Digital engineering" redirects here. For the company, see Digital Engineering, Inc.
A digital twin is a digital model of an intended or actual real-world physical product, system, or process (a physical twin) that serves as the effectively indistinguishable digital counterpart of it for practical purposes, such as simulation, integration, testing, monitoring, and maintenance.[1][2][3] The digital twin is the underlying premise for Product Lifecycle Management[4] and exists throughout the entire lifecycle of the physical entity it represents. Since information is detailed, the digital twin representation is determined by the value-based use cases it is created to implement. The digital twin can exist before the physical entity, as for example with virtual prototyping. The use of a digital twin in the creation phase allows the intended entity's entire lifecycle to be modeled and simulated.[5] A digital twin of an existing entity may be used in real time and regularly synchronized with the corresponding physical system.
Though the concept originated earlier (as a natural aspect of computer simulation generally), the first practical definition of a digital twin originated from NASA in an attempt to improve the physical-model simulation of spacecraft in 2010.[6] Digital twins are the result of continual improvement in product design and engineering. Product drawings and engineering specifications have progressed from handmade drafting to computer-aided drafting/computer-aided design to model-based systems engineering and strict link to signal from the physical counterpart.
In the 2010s and 2020s, manufacturing industries began moving beyond digital product definition to extending the digital twin concept to the entire manufacturing process. Doing so allows the benefits of virtualization to be extended to domains such as inventory management including lean manufacturing, machinery crash avoidance, tooling design, troubleshooting, and preventive maintenance. Digital twinning therefore allows extended reality and spatial computing to be applied not just to the product itself but also to all of the business processes that contribute toward its production.
^Moi, Torbjørn; Cibicik, Andrej; Rølvåg, Terje (2020-05-01). "Digital twin based condition monitoring of a knuckle boom crane: An experimental study". Engineering Failure Analysis. 112: 104517. doi:10.1016/j.engfailanal.2020.104517. hdl:11250/2650461. ISSN 1350-6307.
^Haag, Sebastian; Anderl, Reiner (2018-01-01). "Digital twin – Proof of concept". Manufacturing Letters. Industry 4.0 and Smart Manufacturing. 15: 64–66. doi:10.1016/j.mfglet.2018.02.006. ISSN 2213-8463.
^Boschert, Stefan; Rosen, Roland (2016), Hehenberger, Peter; Bradley, David (eds.), "Digital Twin—The Simulation Aspect", Mechatronic Futures: Challenges and Solutions for Mechatronic Systems and their Designers, Cham: Springer International Publishing, pp. 59–74, doi:10.1007/978-3-319-32156-1_5, ISBN 978-3-319-32156-1, retrieved 2024-03-16
^Grieves, Michael (October 31, 2002). "Completing the Cycle: Using PLM Information in the Sales and Service Functions". Retrieved November 19, 2022.
^Grieves, Michael (October 5, 2015). "Can the digital twin transform manufacturing". World Economic Forum Emerging Technologies. Retrieved November 19, 2022.
^Elisa Negri (2017). "A review of the roles of Digital Twin in CPS-based production systems". Procedia Manufacturing. 11: 939–948. doi:10.1016/j.promfg.2017.07.198. hdl:11311/1049863. S2CID 115508540.
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