Cargando…
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...
Autores principales: | , , , |
---|---|
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 |
_version_ | 1784588948618608640 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8540280 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT bamunuarachchidinithi digitaltwinssupportingefficientdigitalindustrialtransformation AT georgakopoulosdimitrios digitaltwinssupportingefficientdigitalindustrialtransformation AT banerjeeabhik digitaltwinssupportingefficientdigitalindustrialtransformation AT jayaramanpremprakash digitaltwinssupportingefficientdigitalindustrialtransformation |