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An end-to-end big data analytics platform for IoT-enabled smart factories : a case study of battery module assembly system for electric vehicles
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Kahveci, Sinan, Alkan, Bugra, Ahmad, Mus’ab H., Ahmad, Bilal and Harrison, Robert (2022) An end-to-end big data analytics platform for IoT-enabled smart factories : a case study of battery module assembly system for electric vehicles. Journal of Manufacturing Systems, 63 . pp. 214-223. doi:10.1016/j.jmsy.2022.03.010 ISSN 0278-6125.
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Official URL: https://doi.org/10.1016/j.jmsy.2022.03.010
Abstract
Within the concept of factories of the future, big data analytics systems play a critical role in supporting decision-making at various stages across enterprise processes. However, the design and deployment of industry-ready, lightweight, modular, flexible, and cost efficient big data analytics solutions remains one of the main challenges towards the Industry 4.0 enabled digital transformation. This paper presents an end-to-end IoT-based big data analytics platform that consists of five interconnected layers and several components for data acquisition, integration, storage, analytics and visualisation purposes. The platform architecture benefits from state-of-the-art technologies and integrates them in a systematic and interoperable way with clear information flows. The developed platform has been deployed in an electric vehicle battery module assembly automation system designed by the Automation Systems Group at the University of Warwick, the UK. The developed proof-of-concept solution demonstrates how a wide variety of tools and methods can be orchestrated to work together aiming to support decision-making and to improve both process and product qualities in smart manufacturing environments.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
SWORD Depositor: | Library Publications Router | ||||||||
Library of Congress Subject Headings (LCSH): | Big data , Information visualization , Internet of things , Industry 4.0 , Electric vehicles -- Batteries | ||||||||
Journal or Publication Title: | Journal of Manufacturing Systems | ||||||||
Publisher: | Elsevier | ||||||||
ISSN: | 0278-6125 | ||||||||
Official Date: | April 2022 | ||||||||
Dates: |
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Volume: | 63 | ||||||||
Page Range: | pp. 214-223 | ||||||||
DOI: | 10.1016/j.jmsy.2022.03.010 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 27 May 2022 | ||||||||
Date of first compliant Open Access: | 30 May 2022 |
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