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A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics
Article

Digital Twins Supporting Efficient Digital Industrial Transformation

Department of Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn 3122, Australia
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Author to whom correspondence should be addressed.
Academic Editor: Sisi Zlatanova
Sensors 2021, 21(20), 6829; https://doi.org/10.3390/s21206829
Received: 29 August 2021 / Revised: 29 September 2021 / Accepted: 11 October 2021 / Published: 14 October 2021
(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)
Industry 4.0 applications help digital industrial transformation to be achieved through smart, data-driven solutions that improve production efficiency, product consistency, preventive maintenance, and the logistics of industrial applications and related supply chains. To enable and accelerate digital industrial transformation, it is vital to support cost-efficient Industry 4.0 application development. However, the development of such Industry 4.0 applications is currently expensive due to the limitations of existing IoT platforms in representing complex industrial machines, the support of only production line-based application testing, and the lack of cost models for application cost/benefit analysis. In this paper, we propose the use of Cyber Twins (CTs), an extension of Digital Twins, to support cost-efficient Industry 4.0 application development. CTs provide semantic descriptions of the machines they represent and incorporate machine simulators that enable application testing without any production line risk and cost. This paper focuses on CT-based Industry 4.0 application development and the related cost models. Via a case study of a CT-based Industry 4.0 application from the dairy industry, the paper shows that CT-based Industry 4.0 applications can be developed with approximately 60% of the cost of IoT platform-based application development. View Full-Text
Keywords: cyber twins; digital twins; Industry 4.0 cost model cyber twins; digital twins; Industry 4.0 cost model
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MDPI and ACS Style

Bamunuarachchi, D.; Georgakopoulos, D.; Banerjee, A.; Jayaraman, P.P. Digital Twins Supporting Efficient Digital Industrial Transformation. Sensors 2021, 21, 6829. https://doi.org/10.3390/s21206829

AMA Style

Bamunuarachchi D, Georgakopoulos D, Banerjee A, Jayaraman PP. Digital Twins Supporting Efficient Digital Industrial Transformation. Sensors. 2021; 21(20):6829. https://doi.org/10.3390/s21206829

Chicago/Turabian Style

Bamunuarachchi, Dinithi, Dimitrios Georgakopoulos, Abhik Banerjee, and Prem P. Jayaraman 2021. "Digital Twins Supporting Efficient Digital Industrial Transformation" Sensors 21, no. 20: 6829. https://doi.org/10.3390/s21206829

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