Digital Twin: Enabling Technologies, Challenges and Open Research

Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is b...

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Veröffentlicht in:IEEE access Jg. 8; S. 108952 - 108971
Hauptverfasser: Fuller, Aidan, Fan, Zhong, Day, Charles, Barlow, Chris
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
applications
Artificial intelligence
Computational modeling
Data analysis
Data models
deep learning
Digital twins
enabling technologies
Industrial applications
industrial Internet of Things (IIoT)
Internet of Things
Internet of Things (IoT)
literature review
machine learning
Manufacturing
Smart cities
Virtual environments
ISSN:2169-3536, 2169-3536
Online-Zugang:Volltext
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Zusammenfassung:Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these areas and the current state of research. The paper provides an assessment of the enabling technologies, challenges and open research for Digital Twins.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2998358