The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.
Applications of Digital Twin across Industries: A Review
Open accessAbstract
One of the most promising technologies that is driving digitalization in several industries is Digital Twin (DT). DT refers to the digital replica or model of any physical object (physical twin). What differentiates DT from simulation and other digital or CAD models is the automatic bidirectional exchange of data between digital and physical twins in real-time. The benefits of implementing DT in any sector include reduced operational costs and time, increased productivity, better decision making, improved predictive/preventive maintenance, etc. As a result, its implementation is expected to grow exponentially in the coming decades as, with the advent of Industry 4.0, products and systems have become more intelligent, relaying on collection and storing incremental amounts of data. Connecting that data effectively to DTs can open up many new opportunities and this paper explores different industrial sectors where the implementation of DT is taking advantage of these opportunities and how these opportunities are taking the industry forward. The paper covers the applications of DT in 13 different industries including the manufacturing, agriculture, education, construction, medicine, and retail, along with the industrial use case in these industries.
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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 · cited 1×
“Thus, the information which could improve the next generation product/system is often lost when the product is retired [67].”
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 · cited 1×
“A DT can help in (i) production planning and control by planning and executing the orders automatically and improving decision support by means of a detailed diagnosis, (ii) maintenance by evaluating and analyzing machine conditions, identifying any changes in the production system and its effects, and taking anticipa…”
Digital twin: Enabling technologies, challenges and open research
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020) · IEEE Access · doi:10.1109/access.2020.2998358 · Cited by 2,965
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
The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles
Glaessgen, E., & Stargel, D. (2012) · 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA · doi:10.2514/6.2012-1818 · Cited by 2,404 · cited 1×
“Any damage detected by DT could be resolved by activating in situ repairs or recommending appropriate changes to the mission, resulting in a longer lifespan and a higher success rate of the mission [45].”
Review of digital twin about concepts, technologies, and industrial applications
Liu, M., Fang, S., Dong, H., & Xu, C. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.06.017 · Cited by 1,875
Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison
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
Negri, E., Fumagalli, L., & Macchi, M. (2017) · Procedia Manufacturing · doi:10.1016/j.promfg.2017.07.198 · Cited by 1,669
Lu, Y., Liu, C., Wang, K. I.-K., Huang, H., & Xu, X. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101837 · Cited by 1,624
Cited by
Guo, S., West, A., Papuga, J., Theodossiades, S., & Jiang, J. (2025) · Sensors · doi:10.3390/s25216531 · Cited by 4
Metadata
- Authors
- Singh, M., Srivastava, R., Fuenmayor, E., Kuts, V., Qiao, Y., Murray, N., & Devine, D.
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2022-06-04
- Volume
- 12
- Issue
- 11
- Pages
- 5727
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4281750882
- Cited by
- 355
- References
- 90
- FWCI
- 33.46
- Type
- journal article
- Language
- en
- Open access
- gold
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- Singh, M., Srivastava, R., Fuenmayor, E., Kuts, V., Qiao, Y., Murray, N., & Devine, D. (2022). Applications of Digital Twin across Industries: A Review. Applied Sciences. https://doi.org/10.3390/app12115727
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
