The DOI matches its Crossref record; 144 of the 144 references it cites were found in a registry or an index.
Digital twin modeling
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
“ACB simulation models have evolved significantly since the early 2000s [90] but their most recent iteration has noticed fewer modifications.”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [90]
Citations
Cites
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,548
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: 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
Digital twin: values, challenges and enablers from a modeling perspective
Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952
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
Lu, Y., Liu, C., Wang, K. I., Huang, H., & Xu, X. W. (2019) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101837 · Cited by 1,753
Enabling technologies and tools for digital twin
Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang, L., & Nee, A. Y. C. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2019.10.001 · Cited by 1,579
Self-citationA survey on digital twin: definitions, characteristics, applications, and design implications
Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019) · IEEE Access · doi:10.1109/access.2019.2953499 · Cited by 1,480
The ‘Digital Twin’ to enable the vision of precision cardiology
Acero, J. C., Margara, F., Marciniak, M., Rodero, C., Lončarić, F., Feng, Y., Gilbert, A., Fernandes, J. F., Bukhari, S. H. A., Wajdan, A., et al. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehaa159 · Cited by 987
Digital Twin for maintenance: A literature review
Errandonea, I., Beltrán, S., & Arrizabalaga, S. (2020) · Computers in Industry · doi:10.1016/j.compind.2020.103316 · Cited by 819
Research works printed but not linked: 16
- Tao, 2019, Five-dimension digital twin model and its ten applications, CIMS, 25, 1
- Tao, 2021, Theory of digital twin modeling and its application, CIMS, 27, 1
- Santagati, 2020, Historical sentient – building information model: a digital twin for the management of museum collections in historical architectures. The International Archives of the Photogrammetry, Remote Sens Spat Inf Sci
- Wang, 2020, Dynamic data driven modeling and simulation method for digital twin, J Syst Eng Electron, 42, 2779
- Laukotka, 2020, Use of digital twins overarching life cycle phases: Model-based product family development using the aircraft cabins as example, Z für Wirtsch Fabr, 115, 101
- Wang, 2019, Models and implementation of digital twin based spacecraft system engineering, CIMS, 25, 1348
- Hou, 2021, Modeling and control of digital twin-based aircraft assembly state inspection robot, CIMS, 27, 981
- Zhu, 2021, Construction of extended collision warning system digital twin conceptual scheme based on TRIZ functional modeling, CIMS, 27, 337
- Li, 2021, Construction and application of CMF design service model for industrial products driven by digital twins, CIMS, 27, 307
- Li M., Ma Y., Yin Z., Wang C. Structural design of digital twin laboratory model based on instruments sharing platform. In: Proceedings of the 2022 34th Chinese Control and Decision Conference. 〈https://doi.org/10.26914/c.cnkihy.2020.033937〉. · DOI
- Zhang, 2017, Method for assembly interference detection based on MRT, CIMS, 23, 1253
- Werner, 2018, Data-consistent model engineering of production-centric digital twins conceptualization of a data structure and model management approach for digital twins based on semantic web technologies, 19 Leitkongress der Mess- und Autom: Seamless Converg Autom IT: Autom, 183
- Zheng, 2021, Aircraft final assembly line modeling based on digital twin, J Zhejiang Univ Eng Sci, 55, 05
- Lozoya-Santos J.D.J., Ferreira A.C., Martínez A.V. Digital twin model proposal for an automotive chassis. In: Proceedings of the 2019 30th International Conference on Adaptive Structures and Technologies.
- Ding, 2019, Digital twin-based multi-dimensional and multi-scale modeling of smart manufacturing spaces, CIMS, 25, 1491
- Jatale A. Reduced order models (ROMs) for simulation democratization and digital twins. In Proceedings of the 2018 10th AIChE Southwest Process Technology Conference.
Cited by
Sorted by citations
Digital twin in healthcare: Recent updates and challenges
Sun, T., He, X., & Li, Z. (2023) · Digital Health · doi:10.1177/20552076221149651 · Cited by 421
Liu, X., Jiang, D., Tao, B., Xiang, F., Jiang, G., Sun, Y., Kong, J., & Li, G. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.101876 · Cited by 369
Advancements and challenges of digital twins in industry
Tao, F., Zhang, H., & Zhang, C. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00603-w · Cited by 303
The Application of Artificial Intelligence and Big Data in the Food Industry
Ding, H., Tian, J., Yu, W., Wilson, D. I., Young, B. R., Cui, X., Xin, X., Wang, Z., & Li, W. (2023) · Foods · doi:10.3390/foods12244511 · Cited by 263
Digital twin: Data exploration, architecture, implementation and future
Dihan, M. S., Akash, A. I., Tasneem, Z., Das, P., Das, S. K., Islam, M. R., Islam, M. M., Badal, F. R., Ali, M. F., Ahamed, M. H., et al. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e26503 · Cited by 250
Metadata
- Authors
- Tao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P.
- Affiliations
- Beihang University (CN)
- Hong Kong Polytechnic University (HK)
- Corresponding author
- Qi, Qinglin
- Venue
- Journal of Manufacturing Systems
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 64
- Pages
- 372-389
- ISSN
- 0278-6125 (ISSN-L)
- OpenAlex
- W4285098908
- Cited by
- 1,255
- References
- 141
- Citations by year
- 2026: 290 · 2025: 437 · 2024: 347 · 2023: 166 · 2022: 15
- FWCI
- 110.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Manufacturing Systems (publishedVersion)
- Funders
- National Natural Science Foundation of China (52005024, G-YZ3N)
- 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, digital twin modeling, enabling technologies, digital transformation
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.
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