The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
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
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Cited by
Sorted by citations
Digital Twin Technology Challenges and Applications: A Comprehensive Review
Botín-Sanabria, D. M., Mihaita, A.-S., Peimbert-García, R. E., Ramírez-Moreno, M. A., Ramírez-Mendoza, R. A., & Lozoya-Santos, J. D. J. (2022) · Remote Sensing · doi:10.3390/rs14061335 · Cited by 878
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Singh, M., Srivastava, R., Fuenmayor, E. A., Kuts, V., Qiao, Y., Murray, N., & Devine, D. M. (2022) · Applied Sciences · doi:10.3390/app12115727 · Cited by 376
“To get a better understanding of DT, our previous review paper [2] can be referred to, which talks about the definitions of DT across the literature, its characteristics, types, advantages, and challenges.”
InfluentialSelf-citationDigital twin for smart manufacturing, A review
Soori, M., Behrooz Arezoo, B., & Dastres, R. (2023) · Sustainable Manufacturing and Service Economics · doi:10.1016/j.smse.2023.100017 · Cited by 411
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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