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
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“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
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Self-citationDigital Twin in manufacturing: A categorical literature review and classification
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Self-citationThe Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles
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Data-driven smart manufacturing
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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
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Self-citationA Review of the Roles of Digital Twin in CPS-based Production Systems
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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
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- 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
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Self-citationHu, F., Bi, S., & Zhu, Y. (2024) · Mathematics · doi:10.3390/math12020355 · Cited by 2
Sustainable Manufacturing: Digital Twinning for a Mechanical Assembly Production Line
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Biswas, R., & Roy, U. (2024) · International Journal of Information Technology Research and Applications · doi:10.59461/ijitra.v3i1.87 · Cited by 2
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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
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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.
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