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Digital Twins Supporting Efficient Digital Industrial Transformation

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 digit...

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Detalles Bibliográficos
Autores principales: Bamunuarachchi, Dinithi, Georgakopoulos, Dimitrios, Banerjee, Abhik, Jayaraman, Prem Prakash
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8540280/
https://www.ncbi.nlm.nih.gov/pubmed/34696042
http://dx.doi.org/10.3390/s21206829
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author Bamunuarachchi, Dinithi
Georgakopoulos, Dimitrios
Banerjee, Abhik
Jayaraman, Prem Prakash
author_facet Bamunuarachchi, Dinithi
Georgakopoulos, Dimitrios
Banerjee, Abhik
Jayaraman, Prem Prakash
author_sort Bamunuarachchi, Dinithi
collection PubMed
description 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.
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spelling pubmed-85402802021-10-24 Digital Twins Supporting Efficient Digital Industrial Transformation Bamunuarachchi, Dinithi Georgakopoulos, Dimitrios Banerjee, Abhik Jayaraman, Prem Prakash Sensors (Basel) Article 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. MDPI 2021-10-14 /pmc/articles/PMC8540280/ /pubmed/34696042 http://dx.doi.org/10.3390/s21206829 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bamunuarachchi, Dinithi
Georgakopoulos, Dimitrios
Banerjee, Abhik
Jayaraman, Prem Prakash
Digital Twins Supporting Efficient Digital Industrial Transformation
title Digital Twins Supporting Efficient Digital Industrial Transformation
title_full Digital Twins Supporting Efficient Digital Industrial Transformation
title_fullStr Digital Twins Supporting Efficient Digital Industrial Transformation
title_full_unstemmed Digital Twins Supporting Efficient Digital Industrial Transformation
title_short Digital Twins Supporting Efficient Digital Industrial Transformation
title_sort digital twins supporting efficient digital industrial transformation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8540280/
https://www.ncbi.nlm.nih.gov/pubmed/34696042
http://dx.doi.org/10.3390/s21206829
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