Cited by 1,255Digital Transformation in IndustryApplied Advanced TechnologiesBIM and Construction Integration

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Digital twin modeling

Tao, Fei, Xiao, Bin, Qi, Qinglin, Cheng, Jiangfeng, Ji, Ping·2022·Journal of Manufacturing Systems

DOI 10.1016/j.jmsy.2022.06.015

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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-citation
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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
Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues
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
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Self-citation
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The ‘Digital Twin’ to enable the vision of precision cardiology
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Digital Twin for maintenance: A literature review
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Show all 141 references

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.

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Authors: Tao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P.
Affiliations: Beihang University (CN)
Hong Kong Polytechnic University (HK)
Corresponding author: Qi, Qinglin
ORCID: Tao, Fei
Xiao, Bin
Qi, Qinglin
Cheng, Jiangfeng
Ji, Ping
Venue: Journal of Manufacturing Systems
Publisher: Elsevier BV
Published: 2022-07
Volume: 64
Pages: 372-389
ISSN: 0278-6125 (ISSN-L)
DOI: 10.1016/j.jmsy.2022.06.015
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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

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