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

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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.description57th CIRP Conference on Manufacturing Systems 2024, 29-31 May 2024, Póvoa de Varzim, Portugal
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
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.publisherElsevier
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 Engineering
dc.subject40 Engineering
dc.subjectMachine Learning and Artificial Intelligence
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.subjectData Science
dc.subject9 Industry, Innovation and Infrastructure
dc.subject4014 Manufacturing engineering
dc.titleEnhancing sustainability in manufacturing through cognitive digital twins powered by generative artificial intelligence
dc.typeArticle
dcterms.coveragePóvoa de Varzim, Portugal
dcterms.dateAccepted2024
dcterms.temporal.endDate31-May-2024
dcterms.temporal.startDate29-May-2024

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