Enhancing sustainability in manufacturing through cognitive digital twins powered by generative artificial intelligence

Simple item page
dc.contributor.authorAssad, Fadi
dc.contributor.authorPatsavellas, John
dc.contributor.authorSalonitis, Konstantinos
dc.date.accessioned2024-12-13T10:41:42Z
dc.date.available2024-12-13T10:41:42Z
dc.date.freetoread2024-12-13
dc.date.issued2024-11
dc.date.pubOnline2024-11-27
dc.description.abstractThe rise of Industry 4.0 has brought new advancements in manufacturing, with a focus on integrating digital technologies to optimise processes and increase sustainability. Cognitive Digital Twins (CDTs) are emerging as a powerful paradigm in this area. They leverage advanced analytics, artificial intelligence (AI), and machine learning to create dynamic, real-time representations of physical manufacturing systems. This paper explores how CDTs can improve sustainability within the manufacturing sector. It proposes integrating generative artificial intelligence (GenAI) into the platforms that operate these digital twins to grant them cognitive capabilities. The work introduces a method for mapping and integrating energy consumption data to an Internet of Things (IoT) platform that includes the digital twin and a generative AI language model, such as ChatGPT. This proposed approach serves as a stepping stone towards unlocking the full potential of CDTs. It empowers manufacturers to achieve higher levels of sustainability and environmental responsibility.
dc.description.conferencename57th CIRP Conference on Manufacturing Systems 2024
dc.description.journalNameProcedia CIRP
dc.format.extentpp. 677-682
dc.identifier.citationAssad F, Patsavellas J, Salonitis K. (2024) Enhancing sustainability in manufacturing through cognitive digital twins powered by generative artificial intelligence. Procedia CIRP, Volume 130, November 2024, pp. 677-682. 57th CIRP Conference on Manufacturing Systems 2024, 29-31 May 2024, Póvoa de Varzim, Portugalen_UK
dc.identifier.elementsID559986
dc.identifier.issn2212-8271
dc.identifier.urihttps://doi.org/10.1016/j.procir.2024.10.147
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23259
dc.identifier.volumeNo130
dc.languageEnglish
dc.language.isoen
dc.publisherElsevieren_UK
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S2212827124013040?via%3Dihub
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject4014 Manufacturing Engineeringen_UK
dc.subject40 Engineeringen_UK
dc.subjectMachine Learning and Artificial Intelligenceen_UK
dc.subjectNetworking and Information Technology R&D (NITRD)en_UK
dc.subjectData Scienceen_UK
dc.subject9 Industry, Innovation and Infrastructureen_UK
dc.titleEnhancing sustainability in manufacturing through cognitive digital twins powered by generative artificial intelligenceen_UK
dc.typeArticle
dcterms.coveragePóvoa de Varzim, Portugal
dcterms.dateAccepted2024
dcterms.temporal.endDate31 May 2024
dcterms.temporal.startDate29 May 2024

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Enhancing_sustainability_in_manufacturing-2024.pdf
Size:
1 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Plain Text
Description:
Download

Collections

Staff publications (SATM)