Digital Twin: Enabling Technologies, Challenges and Open Research

Цифровой двойник: обеспечивающие технологии, проблемы и открытые направления исследований
Zhong Fan, Aidan Fuller, Charles Day, Chris Barlow
2020-01-01

Artificial IntelligenceDigital TwinIndustry 4.0Internet of Things (IoT)Smart cities
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.
1
Artificial intelligence and the Internet of Things are identified as key enabling technologies for Digital Twin systems.
2
Digital Twin technology is characterized by bidirectional, seamless data integration between a physical machine and its virtual counterpart.
3
Industry 4.0 advancements have accelerated Digital Twin development, particularly within manufacturing applications.
4
The paper reviews Digital Twin literature and categorizes recent research across manufacturing, healthcare, and smart-city domains.
5
The review assesses major Digital Twin challenges and identifies open research directions.

Digital Twin technology (integration of physical and virtual machines/data)

Enabling technologies, applications, challenges, and open research directions for Digital Twins

Publication Details
Publication Date
2020-01-01
Journal
Publisher
ISSN
Cited by
2821
Access Type
Author Information
Authors
Zhong Fan
Aidan Fuller
Charles Day
Chris Barlow
Explore further
Open the scid.ai AI chat with a ready-made request: it will find papers on a similar topic and help build a literature review.
Find similar papers in the chat →
Make a presentation
100%