Synonyms
Definition
A digital twin is a digital representation of an active unique product (real device, object, machine, service, or intangible asset) or unique product-service system (a system consisting of a product and a related service) that comprises its selected characteristics, properties, conditions, and behaviors by means of models, information, and data within a single or even across multiple life cycle phases.
Theory and Application
The term digital twin (DT) was coined by Vickers and introduced as “Mirrored Spaces Model” concept by Grieves in the first executive Product Lifecycle Management courses at the University of Michigan in 2002 (Grieves 2005, 2014, 2016). The concept of the DT has historically evolved from the aerospace industry and has since then been translated into many areas. This may also be a cause for the fact that there is no uniform accepted scientific definition of the term, yet. Nevertheless, the subject was researched intensively during...
References
Abramovici M, Göbel JC, Savarino P (2017) Reconfiguration of smart products during their use phase based on virtual product twins. CIRP Ann 66(1):165–168
Bajaj M, Cole B, Zwemer D (2016) Architecture to geometry-integrating system models with mechanical design. In: AIAA SPACE 2016, p 5470
Bazilevs Y, Deng X, Korobenko A, Lanza Di Scalea F, Todd MD, Taylor SG (2015) Isogeometric fatigue damage prediction in large-scale composite structures driven by dynamic sensor data. J Appl Mech 82(9):91008. https://doi.org/10.1115/1.4030795
Bielefeldt B, Hochhalter J, Hartl D (2015) Computationally efficient analysis of SMA sensory particles embedded in complex aerostructures using a substructure approach. In: Proceedings of the ASME conference on smart materials, adaptive structures and intelligent systems, Colorado Springs, Colorado, USA, 21–23 Sept 2015. The American Society of Mechanical Engineers, New York. V001T02A007
Canedo A (2016) Industrial IoT lifecycle via digital twins. In: CODES’16 proceedings of the eleventh IEEE/ACM/IFIP international conference on hardware/software codesign and system synthesis, Pittsburgh, Pennsylvania – 01–07 Oct 2016. ACM, New York. Article No. 29
Gabor T, Belzner L, Kiermeier M, Beck MT, Neitz A (2016) A simulation-based architecture for smart cyber-physical systems. In: 2016 IEEE international conference on autonomic computing (ICAC), Wurzburg, Germany, 17–22 July 2016, pp 374–379
Gockel B, Tudor A, Brandyberry M, Penmetsa R, Tuegel E (2012) Challenges with structural life forecasting using realistic mission profiles. In: 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA, Honolulu, Hawai, 23–26 April
Grieves M (2005) Product lifecycle management. The new paradigm for enterprises. IJPD 2(1/2):71. https://doi.org/10.1504/IJPD.2005.006669
Grieves M (2014) Digital twin. Manufacturing excellence through virtual factory replication. Available online at http://innovate.fit.edu/plm/documents/doc_mgr/912/1411.0_Digital_Twin_White_Paper_Dr_Grieves.pdf, checked on 1/21/2019
Grieves M (2016) Origins of the Digital Twin Concept. Unpublished. https://doi.org/10.13140/RG.2.2.26367.61609
Halstenberg F, Stark R (2018) Digitale Zwillinge. Entwicklungen im Maschinenbau. [Digital Twins – evolution in mechanical engineering]. Unternehmermagazin 66(3/4):18–21. Available online at https://www.unternehmermagazin.de/2018-FlippingBooks/03-04/mobile/index.html#p=19, checked on 1/22/2019. In German
Kraft EM (2016) The air force digital thread/digital twin-life cycle integration and use of computational and experimental knowledge. In: 54th AIAA aerospace sciences meeting, San Diego, CA, USA, p 897
Lee J, Lapira E, Bagheri B, Kao H-A (2013) Recent advances and trends in predictive manufacturing systems in big data environment. Manuf Lett 1(1):38–41
Majumdar PK, Faisal Haider M, Reifsnider K (2013) Multi-physics response of structural composites and framework for modeling using material geometry. In: 54th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference, Boston, MA, 8–11 Apr
Negri E, Fumagalli L, Macchi M (2017) A review of the roles of digital twin in CPS-based production systems. Proc Manuf 11:939–948. https://doi.org/10.1016/j.promfg.2017.07.198
Reifsnider K, 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, Boston, MA, 8–11 Apr
Ríos J, Hernández JC, Oliva M, Mas F (2015) Product Avatar as digital counterpart of a physical individual product. Literature review and implications in an aircraft. In: Transdisciplinary lifecycle analysis of systems. Proceedings of the 22nd ISPE Inc. International conference on concurrent engineering, 20–23 July. IOS Press, Amsterdam/Berlin/Tokyo/Washington, DC
Rosen R, von Wichert G, Lo G, Bettenhausen KD (2015) About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine 48(3):567–572. https://doi.org/10.1016/j.ifacol.2015.06.141
Schleich B, Anwer N, Mathieu L, Wartzack S (2017) Shaping the digital twin for design and production engineering. CIRP Ann 66(1):141–144. https://doi.org/10.1016/j.cirp.2017.04.040
Schluse M, Rossmann J (2016) From simulation to experimentable digital twins. Simulation-based development and operation of complex technical systems. In: Systems engineering (ISSE). 2016 IEEE international symposium on systems engineering, Edinburgh, UK, 3–5 Oct 2016, pp 1–6
Schroeder GN, Steinmetz C, Pereira CE, Espindola DB (2016) Digital twin data modeling with AutomationML and a communication methodology for data exchange. IFAC-PapersOnLine 49(30):12–17. https://doi.org/10.1016/j.ifacol.2016.11.115
Schuh G, Walendzik P, Luckert M, Birkmeier M, Weber A, Blum M (2016) Keine Industrie 4.0 ohne den Digitalen Schatten. [No Industrie 4.0 without digital shadows]. ZWF 111(11):745–748. https://doi.org/10.3139/104.111613
Shafto M, Conroy M, Doyle R, Glaessgen E, Kemp C, LeMoigne J, Wang L (2010) DRAFT modeling, simulation. In: Information technology & processing roadmap technology area, vol 11
Shafto M, Conroy M, Doyle R, Glaessgen E, Kemp C, LeMoigne J, Wang L (2012) Modeling, simulation, information technology & processing roadmap. Available online at https://www.nasa.gov/sites/default/files/501321main_TA11-ID_rev4_NRC-wTASR.pdf, checked on 1/21/2019
Stark R, Kind S, Neumeyer S (2017) Innovations in digital modelling for next generation manufacturing system design. CIRP Ann Manuf Technol 66:169–172. Elsevier. Available online at https://www.researchgate.net/publication/316356906_Innovations_in_digital_modelling_for_next_generation_manufacturing_system_design, checked on 1/29/2019
Tuegel E (2012) The airframe digital twin. Some challenges to realization. In: 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA, Honolulu, Hawai, 23–26 Apr
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Stark, R., Damerau, T. (2019). Digital Twin. In: Chatti, S., Tolio, T. (eds) CIRP Encyclopedia of Production Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35950-7_16870-1
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DOI: https://doi.org/10.1007/978-3-642-35950-7_16870-1
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