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Data Analytics and AI for Quality Assurance in Manufacturing: Challenges and Opportunities

2024Conference paper Restricted access
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dc.contributor.authorCatti, Paolo
dc.contributor.authorFreitas, Artur
dc.contributor.authorPereira, Eliseu
dc.contributor.authorGonçalves, Gil
dc.contributor.authorLopes, Rui Pedro
dc.contributor.authorNikolakis, Nikolaos
dc.contributor.authorAlexopoulos, Kosmas
dc.date.accessioned2025-01-30T11:11:47Z
dc.date.available2025-01-30T11:11:47Z
dc.date.issued2024
dc.description.abstractData analytics and Artificial Intelligence (AI) have emerged as essen- tial tools in manufacturing over recent years, providing better insight into pro- duction systems. Their importance can be highlighted by the way it can transform quality control, from prescriptive to proactive. Data analytics combined with AI can identify abnormal trends and patterns in huge amounts of data, that could uncover potential defects and allow pre-emptive action to minimize or even pre- vent these from happening. A direct effect of this is the contribution to waste reduction, as well as saving time and resources. While data in a digital factory is ample and the resources for developing artificial intelligence applications are ac- cessible, the implementation of accurate, robust, standard, and economically vi- able quality monitoring and assessment approaches is not straightforward. This is also strengthened by the scarce skillset in today’s manufacturing companies in this area. In this study, the capabilities and potential of data analytics combined with AI are reviewed with a focus on manufacturing. The implementation chal- lenges posed for a practitioner, as well as the benefits of implementing a solution for a manufacturer using data analytics and AI for quality assessment are dis- cussed, based on real-world experiences from existing production environments. Lastly, a learning approach utilizing a high-fidelity digital twin at its core is pre- sented which a practitioner can utilize to create, test and continuously improve a predictive analytics model.pt_PT
dc.description.sponsorshipThis work was partially supported by the HORIZON-CL4-2021-TWIN-TRANSITION-01 openZDM project, under Grant Agreement No. 101058673.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationCatti, Paolo; Freitas, Artur; Pereira, Eliseu; Gonçalves, Gil; Lopes, Rui Pedro; Nikolakis, Nikolaos; Alexopoulos, Kosmas (2024). Data Analytics and AI for Quality Assurance in Manufacturing: Challenges and Opportunities. In 14th International Conference on Learning Factories, CLF 2024. Cham: Springer Nature, p. 205-212. ISBN 978-3-031-65410-7pt_PT
dc.identifier.doi10.1007/978-3-031-65411-4_25pt_PT
dc.identifier.issn978-3-031-65410-7
dc.identifier.issn978-3-031-65411-4
dc.identifier.urihttp://hdl.handle.net/10198/31099
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relation101058673pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAnalyticspt_PT
dc.subjectArtificial intelligencept_PT
dc.subjectManufacturingpt_PT
dc.subjectQuality assurancept_PT
dc.titleData Analytics and AI for Quality Assurance in Manufacturing: Challenges and Opportunitiespt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.endPage212pt_PT
oaire.citation.startPage205pt_PT
oaire.citation.title14th International Conference on Learning Factories, CLF 2024pt_PT
oaire.citation.volume1059pt_PT
person.familyNameLopes
person.givenNameRui Pedro
person.identifier.ciencia-id8E14-54E4-4DB5
person.identifier.orcid0000-0002-9170-5078
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicatione1e64423-0ec8-46ee-be96-33205c7c98a9
relation.isAuthorOfPublication.latestForDiscoverye1e64423-0ec8-46ee-be96-33205c7c98a9

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