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Digital Twin: Technology Evolution Stages and Implementation Layers With Technology Elements

delete2022-01-01
delete36
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OA
AI
D
Deuk‐Young Jeong
M
Myung-Sun Baek
T
Tae-Beom Lim
Y
Yong‐Woon Kim
S
Se-Han Kim
Y
Yong-Tae Lee
W
Woo-Sug Jung
I
In-Bok Lee *
DOI:10.1109/ACCESS.2022.3174220delete
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Abstract

Abstract

En 中文
A digital twin has recently received considerable attention in various industry domains. The digital twin replicates physical objects (e.g., people, objects, spaces, systems, and processes) in the real world into digital objects in the digital world. It also provides various simulations to solve problems in the real world or to improve situational operations. Therefore, the digital twin is a convergence of various technologies, such as advanced machine-learning algorithms, data analytics, super-resolution visualization and modeling, and simulation. Because the digital twin is a complicated technology, a step-by-step implementation that includes many technology elements should be considered to create a digital twin model. In this study, implementation layers are introduced to guide practical implementations of the digital twin. In addition, technology elements were suggested for the presented implementation layers. Because the suggested technology elements include clear technology definitions, various application domains (e.g., energy, transportation, logistics, environment, manufacturing, and smart cities) can easily utilize the introduced implementation layers and technology elements according to the intended purpose. Furthermore, this paper describes the evolution of digital twins. Digital twin technology has evolved continuously since 2002, when the digital twin concept was first introduced. In the described evolution levels, we show the future aspects of digital twin technology, according to the technological evolution direction. Therefore, the digital twin model can be efficiently created by considering the evolution direction and future aspects by using the suggested digital twin evolution levels.
Keywords:
Digital twin
Analytical models
Synchronization
Simulation
Optimization
Licenses
Data models
Digital twin
digital twin technology evolution
implementation layer
technology elements

Journal

IEEE Access cover
IEEE Access
IF:
3.6
Papers:
9.8W
Citations:
29.4W

Organization

S
seoul national university (snu)
Scholars:
7.2W
Papers: 6.6W
Citations: 86