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Exploring automotive quality correlations through explainable machine learning what-if simulation

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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
dc.contributor.authorOliveira Júnior, Alexandre de
dc.contributor.authorCalvo-Rolle, JoséLuis
dc.contributor.authorPires, Rui
dc.contributor.authorLeitão, Paulo
dc.date.accessioned2026-04-10T13:20:12Z
dc.date.available2026-04-10T13:20:12Z
dc.date.issued2025
dc.description.abstractHigh-dimensional variability in manufacturing processes presents significant challenges for quality control, demanding predictive strategies capable of capturing complex parameter dependencies. Machine learning (ML) offers robust mechanisms for this purpose, but reliance on black-box models often limits interpretability and hinders producing stakeholders’ identification of meaningful correlations for model optimization. This paper introduces an interactive what-if simulation platform designed to explore structural quality correlations in automotive assembly through explainable ML techniques, enhancing transparency and enabling uncertainty quantification. The platform is based on a modular Digital Twin (DT) architecture aligned with the ISO 23247 standard, guiding expert and non-expert users through correlation-driven feature selection, regression modelling and SHapley Additive exPlanations (SHAP) based post-hoc explanations. A case study using real inspection data from a vehicle assembly line demonstrates the tool’s capacity to support variable relevance assessment, dimensionality reduction, and model interpretability. Furthermore, an uncertainty-aware SHAP analysis enhances confidence in the model’s prediction stability, reinforcing the platform’s suitability for quality-driven decision support and integration into future DT ecosystems.por
dc.description.sponsorshipThis work was partially supported by the HORIZONCL4-2021-TWIN-TRANSITION-01 openZDM project under Grant Agreement No. 101058673 and was also supported by national funds through FCT/MCTES (PIDDAC): CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and UIDP/05757/2020 DOI: 10.54499/UIDP/05757/2020); and SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020). The author Alexandre O. J´unior thanks FCT, Portugal, for the PhD grant BD/03967/2023 (DOI: 10.54499/2023.03967.BD).
dc.identifier.citationOliveira Júnior, Alexandre de; Calvo-Rolle; José Luis; Pires, Rui; Leitao, Paulo (2025). Exploring automotive quality correlations through explainable machine learning what-if simulation. In IECON 2025–51st Annual Conference of the IEEE Industrial Electronics Society. Madrid, Spain. p. 1-7. ISBN I979-8-3315-9681-1. DOI: 10.1109/IECON58223.2025.11221373
dc.identifier.doi10.1109/IECON58223.2025.11221373
dc.identifier.isbn979-8-3315-9681-1
dc.identifier.urihttp://hdl.handle.net/10198/36497
dc.language.isoeng
dc.peerreviewedyes
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectExplainable Machine Learning
dc.subjectCorrelation Analysis
dc.subjectUncertainty-aware
dc.subjectSimulation
dc.subjectIndustrial Metaverse
dc.subjectDigital Twin
dc.titleExploring automotive quality correlations through explainable machine learning what-if simulationeng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardNumberUIDB/05757/2020
oaire.awardNumberLA/P/0007/2020
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.conferenceDate2025
oaire.citation.conferencePlaceMadrid, Spain
oaire.citation.titleIECON 2025 – 51st Annual Conference of the IEEE Industrial Electronics Society
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameOliveira Júnior
person.familyNameLeitão
person.givenNameAlexandre de
person.givenNamePaulo
person.identifierhttps://scholar.google.com/citations?user=WurUUUUAAAAJ&hl=pt-PT&oi=ao
person.identifierA-8390-2011
person.identifier.ciencia-id7311-463F-3FA3
person.identifier.ciencia-id8316-8F13-DA71
person.identifier.orcid0000-0002-7009-3965
person.identifier.orcid0000-0002-2151-7944
person.identifier.scopus-author-id57426745400
person.identifier.scopus-author-id35584388900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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relation.isAuthorOfPublication68d9eb25-ad4f-439b-aeb2-35e8708644cc
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