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A DMAIC integrated fuzzy FMEA model

2021-04-20Artigo científico Acesso aberto
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dc.contributor.authorGodina, Radu
dc.contributor.authorSilva, Beatriz Gomes Rolis
dc.contributor.authorEspadinha-Cruz, Pedro
dc.contributor.institutionUNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial
dc.contributor.institutionDEMI - Departamento de Engenharia Mecânica e Industrial
dc.contributor.pblMDPI - Multidisciplinary Digital Publishing Institute
dc.date.accessioned2021-08-28T00:13:22Z
dc.date.available2021-08-28T00:13:22Z
dc.date.issued2021-04-20
dc.description
dc.description.abstractThe growing competitiveness in the automotive industry and the strict standards to which it is subject, require high quality standards. For this, quality tools such as the failure mode and effects analysis (FMEA) are applied to quantify the risk of potential failure modes. However, for qualitative defects with subjectivity and associated uncertainty, and the lack of specialized technicians, it revealed the inefficiency of the visual inspection process, as well as the limitations of the FMEA that is applied to it. The fuzzy set theory allows dealing with the uncertainty and subjectivity of linguistic terms and, together with the expert systems, allows modeling of the knowledge involved in tasks that require human expertise. In response to the limitations of FMEA, a fuzzy FMEA system was proposed. Integrated in the design, measure, analyze, improve and control (DMAIC) cycle, the proposed system allows the representation of expert knowledge and improves the analysis of subjective failures, hardly detected by visual inspection, compared to FMEA. The fuzzy FMEA system was tested in a real case study at an industrial manufacturing unit. The identified potential failure modes were analyzed and a fuzzy risk priority number (RPN) resulted, which was compared with the classic RPN. The main results revealed several differences between both. The main differences between fuzzy FMEA and classical FMEA come from the non-linear relationship between the variables and in the attribution of an RPN classification that assigns linguistic terms to the results, thus allowing a strengthening of the decision-making regarding the mitigation actions of the most “important” failure modes.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent2668241
dc.identifier.doi10.3390/app11083726
dc.identifier.issn2076-3417
dc.identifier.otherPURE: 32502915
dc.identifier.otherPURE UUID: 47addf2f-51a1-4c17-97c4-d162f49556dc
dc.identifier.otherScopus: 85105159746
dc.identifier.otherWOS: 000644009000001
dc.identifier.otherORCID: /0000-0002-5337-0633/work/99080658
dc.identifier.otherORCID: /0000-0003-1244-5624/work/205572251
dc.identifier.urihttp://hdl.handle.net/10362/123281
dc.identifier.urlhttps://www.scopus.com/pages/publications/85105159746
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/157435/PT
dc.subjectAutomotive industry
dc.subjectDMAIC
dc.subjectFMEA
dc.subjectFuzzy FMEA
dc.subjectPotential failure
dc.subjectGeneral Materials Science
dc.subjectInstrumentation
dc.subjectGeneral Engineering
dc.subjectProcess Chemistry and Technology
dc.subjectComputer Science Applications
dc.subjectFluid Flow and Transfer Processes
dc.titleA DMAIC integrated fuzzy FMEA modelen
dc.title.subtitleA case study in the automotive industryen
dc.typejournal article
degois.publication.issue8
degois.publication.titleApplied Sciences
degois.publication.volume11
dspace.entity.typePublication
oaire.awardNumber157435
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/157435/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication16c5d08f-ac19-4f03-bd7f-bb873378a0db
relation.isProjectOfPublication.latestForDiscovery16c5d08f-ac19-4f03-bd7f-bb873378a0db

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