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Digital Twin: Origin to Future
Open accessAbstract
Digital Twin (DT) refers to the virtual copy or model of any physical entity (physical twin) both of which are interconnected via exchange of data in real time. Conceptually, a DT mimics the state of its physical twin in real time and vice versa. Application of DT includes real-time monitoring, designing/planning, optimization, maintenance, remote access, etc. Its implementation is expected to grow exponentially in the coming decades. The advent of Industry 4.0 has brought complex industrial systems that are more autonomous, smart, and highly interconnected. These systems generate considerable amounts of data useful for several applications such as improving performance, predictive maintenance, training, etc. A sudden influx in the number of publications related to ‘Digital Twin’ has led to confusion between different terminologies related to the digitalization of industries. Another problem that has arisen due to the growing popularity of DT is a lack of consensus on the description of DT as well as so many different types of DT, which adds to the confusion. This paper intends to consolidate the different types of DT and different definitions of DT throughout the literature for easy identification of DT from the rest of the complimentary terms such as ‘product avatar’, ‘digital thread’, ‘digital model’, and ‘digital shadow’. The paper looks at the concept of DT since its inception to its predicted future to realize the value it can bring to certain sectors. Understanding the characteristics and types of DT while weighing its pros and cons is essential for any researcher, business, or sector before investing in the technology.
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“In accordance with DT characteristics, the DTP is able to simulate manufacturing scenarios and perform validation testing, evaluation and even quality control testing prior to the actual manufacturing process itself.This approach effectively reduces production costs and operational time by identifying flaws or possible risks of the physical twin before production.”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [10]
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“It is important to note that the development and widespread implementation of DTs depend on the advances in research and enabling technologies such as AI, ML, big data, 5G communication and cloud/edge computing [10,83].”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [10]
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“The proposed approach involved utilizing DT technology to replicate the aircraft's physical and mechanical characteristics, aiming to forecast potential fatigue or structural cracks.This strategy would effectively extend the operational lifespan of the aircraft [21].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [21]
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“Furthermore, the continuous transfer of information between DT and its physical counterpart enables ongoing validation and optimization of the system's processes [21].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [21]
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“However, the modern state of the art classifies and defines DTs based on the application domain [10] and their levels of integration.”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [10]
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“High costs of implementation due to the increased amount of sensors and computational resources needed [10,18].”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [10]
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“Issues related to data (trust, privacy, cybersecurity, convergence and governance, acquisition and large-scale analysis) [10].”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [10]
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Citations
Cites
Digital Twin in Industry: State-of-the-Art
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2873186 · Cited by 4,391
Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems
Grieves, M., & Vickers, J. (2017) · Transdisciplinary Perspectives on Complex Systems · doi:10.1007/978-3-319-38756-7_4 · Cited by 3,727
Digital Twin in manufacturing: A categorical literature review and classification
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.08.474 · Cited by 3,612
Digital twin-driven product design, manufacturing and service with big data
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0233-1 · Cited by 2,920
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“…literature have referred to DT as a virtual or digital model [21,23,27–35], layout [36], counterpart [7,9,35,37], doppelganger [38], clone [39], footprint [40], software analogue [41], representation [42–46], information constructs [2,47], or simulation [20,26,48–51] of its physical counterpart.”
BackgroundInfluentialReview of digital twin about concepts, technologies, and industrial applications
Liu, M., Fang, S., Dong, H., & Xu, C. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.06.017 · Cited by 2,075
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Qi, Q., & Tao, F. (2018) · IEEE Access · doi:10.1109/access.2018.2793265 · Cited by 1,716
A Review of the Roles of Digital Twin in CPS-based Production Systems
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“…[45], [49], [51], [52], [59], [64], [65] Machining [17], [38], [49], [52], [63] Digital Twin [6], [8], [12], [24], [28], [33], [36], [43], [45], [46], [47], [53], [54], [57], [62], [63], [65] IIoT / IoT [32], [42], [66] CPS [20], [31], [54], [73] Human robot collaboration [40] Product design…”
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Towards a lightweight ROS–CoppeliaSim simulator for the BEATRIX robotic education platform
Alagarraja, J., Rubio-Solis, A., & Martínez-Hernández, U. (2026) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii64115.2026.11404596
“…Engineering, Faculty of Engineering and Design, University of Bath, Claverton Down, Bath, UK { ja2351, u.martinez } @bath.ac.uk 2 Adrian is with the Hamlyn Centre for Robotic Surgery, Imperial College London, London, UK a.rubiosolis@imperial.ac.uk wear and tear on the hardware [8], [9], [10].”
From PC to AI Through Digital Twins - A glance into the Race for Technological Ideology
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Extending the TOE Framework for Digital Twin Adoption in Latin American Project Management
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“The interviews were transcribed and studied using NVivo 14 [12].”
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A feature-based taxonomy for smart systems development
Özkaya, M., Turunc, A., & Cobanoglu, U. (2026) · Internet of Things · doi:10.1016/j.iot.2026.102017
Schwede, C., Schipper, L., & Cirullies, J. (2026) · International Conference on Computers in Management and Business (ICCMB) · doi:10.1145/3802463.3802479
Liu, W., Tong, B., Liu, H., Xu, Y., Fang, L., Zhou, H., Wu, Y., & Kong, J. (2026) · Journal of Loss Prevention in the Process Industries · doi:10.1016/j.jlp.2026.106105
Layered Depth Image-Based Lightweight 3D Model Compression for Digital Twins
Wang, J., Qian, C., Zhou, Y., Xie, W., & Zhang, H. (2026) · International Conference on Signal Image Processing and Communication (ICSIPC) · doi:10.1109/icsipc69751.2026.11584149
“Digital twin technology has emerged as a transformative approach for equipment lifecycle management, enabling real-time monitoring, fault diagnosis, and maintenance optimization through virtual representations of physical assets [1] .”
Zhao, F. (2026) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2026-99-1804
Nandi, S., Mukherjee, M., & Das, S. K. (2026) · Computers & Industrial Engineering · doi:10.1016/j.cie.2026.112251 · Cited by 1
Fashal, N. A., Khayat, G. A. E., & Abdul-Kader, H. M. (2026) · IEEE International Conference on Smart Sustainable Systems for Computer and Engineering Applications (3SCEA) · doi:10.1109/3scea68071.2026.11602910
“According to the Grave ’ s framework, a digital twin consists of a physical entity, a virtual model, and the mechanisms for linking them graphically in a two-way interaction cycle [3, 8].”
Babu, A. V. S. (2026) · Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · doi:10.1177/09544062261469000
Mosavisadr, S. M., Kheirabadi, M., & Naeen, H. M. (2026) · The Journal of Engineering · doi:10.1049/tje2.70211
Digital Twins in Renewable Energy: An Overview
Abd-Rabou, M. G. E.-M., Saleh, S. M., & Samy, M. M. (2026) · ICEENG International Conference for Innovations in Power and Control Systems (IPCS) · doi:10.1109/ipcs69631.2026.11604236
“Global efforts to hostilities climate variance and usher in a more sustainable and equal future are led by renewable energy [4, 5].”
Zehra, F., Rivera, B., Zaman, U. K. U., Butt, S. U., & Qureshi, A. J. (2026) · Smart and Sustainable Manufacturing Systems · doi:10.1520/ssms20240040
Aerodynamic Design Optimization, AI Creation Systems Applications, Aerospace Engineering
Nithya, S., Sundharesan, R., Aparna, N., & Chandra Bose, G. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-3280-0.ch004
Ferré, C. T., Cabello, R., Folch‐Millán, P., Popescu, A. E. P., Bonet, J., & Bringué, R. (2026) · Computer Applications in Engineering Education · doi:10.1002/cae.70241
“A DT, to be accurate, must accomplish the following points [14]: First, high fidelity ensures that the virtual model mirrors its physical counterpart in both structure and behavior.”
InfluentialAkyildiz, Y. E., Demirkesen, S., Korkmaz, M., Uysal, F., & Tasdemir, E. (2026) · Sustainability · doi:10.3390/su18157597
See, Spawn, Synchronize: A Digital Twin Pipeline for Smart Production Cells
Herrmann, M., Strazdas, D., & Al-Hamadi, A. (2026) · Machines · doi:10.3390/machines14090972
A Multidimensional Activity Theory Framework for Human–Computer Interaction with Digital Twins
Adeel, A., & Apperley, M. D. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2701963
Bacakoğlu, S. S., & Özdemir, E. (2026) · Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi · doi:10.25092/baunfbed.1892890
Digital twins in geriatrics and gerontology: Applications, challenges, and future directions
Faronbi, G. O., James, E., Matope, E. M., Bamidele, D. B., Alabi, J. O., & Olawade, D. B. (2026) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2026.106371
Theoretical Foundations and Practical Values: A Comprehensive Review of Digital Twin
Aridi, R., Aridi, M., Bigaud, D., & Lemenand, T. (2026) · Digital twins and applications. · doi:10.1049/dgt2.70046
Keep Fresh Digital Twins in UAV-Assisted IoT Networks by Exploiting Data Correlations
Shen, Q., Li, J., Peng, J., Xu, Z., Zhao, C., Zhou, P., Liang, W., Jia, X., Das, S. K., & Xu, W. (2026) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/2575-8411.2026.00019
“1: A DT-powered intelligent IoT network for monitoring forest fires and vice versa [12], [20], [26].”
Integrating Digital Twin and Social Media Analytics to Improve Product Development Processes
Ferraro, E. V., & Valilai, O. F. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-24810-7_12
A framework for selecting large infrastructure projects for digital twin adoption
Schneider, P. A., Stephanie, F. L., & Sharma, R. (2026) · Digital Twin · doi:10.1080/27525783.2026.2694918
Digitalization in Sport: Technological Transformation of Performance, Management, and Engagement
Akkaya, G. (2026) · Studies in big data · doi:10.1007/978-3-032-18562-4_20
Kausar, M. A., Nasar, M., & Nayyer, M. A. (2026) · Next research. · doi:10.1016/j.nexres.2026.102299
Next-Generation Simulation Ecosystems Bridging Digital Twin and Omniverse Technologies
Faraji, A., Arya, S. H., Alirezaei, Z., Mahabadi, H. A., & Mehdizadeh, B. (2026) · Emerging trends in mechatronics · doi:10.1007/978-981-95-8387-4_4
Data, Process, and Data-Driven Representations of Digital Twins in Diabetes: Scoping Review
Cinar, B., van den Boom, L., & Maleshkova, M. (2026) · JMIR Diabetes · doi:10.2196/91729
Digital twins and nanomedicine: Predictive modelling for clinical translation
Chowdhury, S., Sarkar, S., & Das, A. (2026) · Next Nanotechnology · doi:10.1016/j.nxnano.2026.100688 · Cited by 1
Cloud, fog and edge computing for school digital twin
Mohanty, A., Mohapatra, A. G., Mukherjee, A., & Keswani, B. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-41632-3.00005-9
Chaudhary, S., Buragohain, D., Bo, M. B., Khichar, S., Figueroa, R. B., & Wuttisittikulkij, L. (2026) · IEEE Access · doi:10.1109/access.2026.3724852
“The term ‘‘digital twin’’ generally refers to a digital representation of a physical system that may support varying levels of synchronization, simulation, and interaction [4].”
Borjigin, O., Dossick, C. S., Treece, B., Thonstad, T., Bernard, T., & Motley, M. (2026) · Journal of Information Technology in Construction · doi:10.36680/j.itcon.2026.039
The Governance Gap in Contemporary LLM-Based Agentic Systems: A Structural Diagnostic Review
Valdez-Cantú, C., Cantoral-Ceballos, J. A., & Alvarado-Uribe, J. (2026) · AI · doi:10.3390/ai7080322
Metadata
- Authors
- Singh, M., Fuenmayor, E., Hinchy, E., Qiao, Y., Murray, N., & Devine, D.
- Affiliations
- Athlone Institute of Technology (IE)
- University of Limerick (IE)
- Corresponding author
- Devine, Declan
- Venue
- Applied System Innovation
- Publisher
- MDPI AG
- Published
- 2021-05-24
- Volume
- 4
- Issue
- 2
- Pages
- 36
- ISSN
- 2571-5577 (ISSN-L)
- OpenAlex
- W3164800436
- Cited by
- 1,038
- References
- 72
- Citations by year
- 2026: 189 · 2025: 275 · 2024: 260 · 2023: 198 · 2022: 102 · 2021: 14
- FWCI
- 74.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied System Innovation (publishedVersion, cc-by)
- University of Limerick Institutional Repository (University of Limerick) (acceptedVersion, other-oa)
- Arrow@dit (Dublin Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Research@THEA (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by-nc-sa)
- Funders
- Science Foundation Ireland (16/RC/3918)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
- Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Digital Innovation in Industries
- Keywords
- digital twin, physical twin, Industry 4.0, real-time data exchange, predictive maintenance, digital thread, digital models, digital shadow, terminology confusion, industrial digitalization
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