Cited by 1,619Digital Transformation in IndustryApplied Advanced TechnologiesBIM and Construction Integration

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Enabling technologies and tools for digital twin

Qi, Qinglin, Tao, Fei, Hu, Tianliang, Anwer, Nabil, Liu, Ang, Wei, Yongli, Wang, Lihui, Nee, A.Y.C.·2021·Journal of Manufacturing Systems

DOI 10.1016/j.jmsy.2019.10.001

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Self-citation
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Self-citation
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Self-citation
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Show all 52 references

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

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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
ORCID: Qi, Qinglin
Tao, Fei
Hu, Tianliang
Anwer, Nabil
Liu, Ang
Wei, Yongli
Wang, Lihui
Nee, A.Y.C.
Venue: Journal of Manufacturing Systems
Publisher: Elsevier BV
Published: 2021-01
Volume: 58
Pages: 3-21
ISSN: 0278-6125 (ISSN-L)
DOI: 10.1016/j.jmsy.2019.10.001
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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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