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Digital Twin in Industry: State-of-the-Art

IEEE Transactions on Industrial Informatics · 2019 · Vol. 15(4) · pp. 2405–2415
Fei TaoHe ZhangAng LiuA.Y.C. Nee

Abstract

Digital twin (DT) is one of the most promising enabling technologies for realizing smart manufacturing and Industry 4.0. DTs are characterized by the seamless integration between the cyber and physical spaces. The importance of DTs is increasingly recognized by both academia and industry. It has been almost 15 years since the concept of the DT was initially proposed. To date, many DT applications have been successfully implemented in different industries, including product design, production, prognostics and health management, and some other fields. However, at present, no paper has focused on the review of DT applications in industry. In an effort to understand the development and application of DTs in industry, this paper thoroughly reviews the state-of-the-art of the DT research concerning the key components of DTs, the current development of DTs, and the major DT applications in industry. This paper also outlines the current challenges and some possible directions for future work.

Digital Transformation in IndustryAdditive Manufacturing and 3D Printing TechnologiesFlexible and Reconfigurable Manufacturing SystemsIndustry 4.0PrognosticsKey (lock)Cyber-physical systemState (computer science)Manufacturing engineeringComputer scienceManufacturingProduct (mathematics)Systems engineering

Funding

  • National Natural Science Foundation of China
  • Beijing Nova Program
Citations
3,580
FWCI
249.40
field-weighted impact
References
54
Percentile
100%
vs. same field & year
Citations per year
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References
Digital twin-driven product design, manufacturing and service with big data
The International Journal of Advanced Manufacturing Technology · 2017 · 2,689 citations
Experimentable Digital Twins—Streamlining Simulation-Based Systems Engineering for Industry 4.0
IEEE Transactions on Industrial Informatics · 2018 · 495 citations
Digital twin-driven product design framework
International Journal of Production Research · 2018 · 1,119 citations
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