The DOI matches its Crossref record, with a small difference in title or year; 51 of the 52 references it cites were found in a registry or an index.
Enabling technologies and tools for digital twin
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Furthermore, DTs improve business performance by streamlining operations and accelerating the achievement of value and competitive advantages [8].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [8]
In the citing paper
“Due to the different formats, standards and protocols, current tools are not integrated and simultaneously used for a specific objective [7]”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [7]
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
Self-citationDigital 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
Self-citationThe 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
Data-driven smart manufacturing
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.01.006 · Cited by 1,934
Self-citationDeep learning for smart manufacturing: Methods and applications
Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.01.003 · Cited by 1,740
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
Self-citationA 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
Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019) · Engineering · doi:10.1016/j.eng.2019.01.014 · Cited by 1,589
Self-citationAbout The Importance of Autonomy and Digital Twins for the Future of Manufacturing
Rosen, R., von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.06.141 · Cited by 1,474
Research works printed but not linked: 3
- Tao, 2017, Digital twin workshop: a new paradigm for future workshop, Comput Integr Manuf Syst, 23, 1
- Tao, 2019, Five-dimension digital twin model and its ten applications, Comput Integr Manuf Syst, 25, 1
- Zhang, 2019, A simulation-based approach for plant layout design and production planning, J Ambient Intell Humaniz Comput, 10, 1217, 10.1007/s12652-018-0687-5 · DOI
Cited by
Sorted by citations
Review 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
Tao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.06.015 · Cited by 1,255
Self-citationDigital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects
Mihai, S., Yaqoob, M., Hưng, Đ. V., Davis, W., Towakel, P., Raza, M., Karamanoglu, M., Barn, B. S., Shetve, D., Prasad, R. V., et al. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3208773 · Cited by 1,195
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
Introducing digital twins to agriculture
Pylianidis, C., Osinga, S., & Athanasiadis, I. N. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105942 · Cited by 753
Metadata
- Authors
- Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang, L., & Nee, A. Y. C.
- Affiliations
- Beihang University (CN)
- Shandong University (CN)
- Université Paris-Sud (FR)
- UNSW Sydney (AU)
- KTH Royal Institute of Technology (SE)
- National University of Singapore (SG)
- Corresponding author
- Tao, Fei
- Venue
- Journal of Manufacturing Systems
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 58
- Pages
- 3-21
- ISSN
- 0278-6125 (ISSN-L)
- OpenAlex
- W2982689699
- Cited by
- 1,579
- References
- 52
- Citations by year
- 2026: 221 · 2025: 299 · 2024: 351 · 2023: 318 · 2022: 205 · 2021: 122
- FWCI
- 72.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Manufacturing Systems (publishedVersion)
- HAL (submittedVersion)
- Funders
- National Basic Research Program of China
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
- Physical Sciences › Engineering › Building and Construction › BIM and Construction Integration
- Keywords
- digital twin, enabling technologies, sensor data, data integration, digital twin implementation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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