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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Digital twin prototype can simulate manufacturing scenarios and perform validation testing, evaluation and quality control prior to actual manufacturing, reducing production costs and operational time by identifying flaws or risks before production.
“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]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe development and widespread implementation of digital twins depend on advances in research and enabling technologies such as AI, ML, big data, 5G communication and cloud/edge computing.
“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]
Section 4.2.1, “Novelty of Technology” (page not available), says that AI, machine learning, big data and cloud computing help make digital twins possible, and that further research and improved infrastructure are needed for implementation. Section 4 also links digital twins’ evolution to advances in their component technologies. The copy does not identify 5G communication or edge computing as dependencies, so the full list is not supported. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe proposed AFRL approach used DT to replicate aircraft physical and mechanical characteristics to forecast fatigue or structural cracks and extend operational lifespan.
“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]
The continuous transfer of information between DT and its physical counterpart enables ongoing validation and optimization of the system's processes.
“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]
The modern state of the art classifies and defines DTs based on the application domain and their levels of integration.
“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]
High costs of implementation due to the increased amount of sensors and computational resources needed are a challenge for digital twin implementation.
“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]
Issues related to data, including trust, privacy, cybersecurity, convergence and governance, acquisition and large-scale analysis, are a challenge for digital twin implementation.
“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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- 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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