El repositorio se encuentra temporalmente en labores de mantenimiento.

Show simple item record

dc.contributor.authorPereda, María
dc.contributor.authorSantos Martín, José Ignacio 
dc.contributor.authorMartín, Óscar
dc.contributor.authorGalán Ordax, José Manuel 
dc.date.accessioned2016-02-12T12:38:23Z
dc.date.available2016-02-12T12:38:23Z
dc.date.issued2015-11
dc.identifier.issn1362-1718
dc.identifier.urihttp://hdl.handle.net/10259/3928
dc.description.abstractIn this work, several of the most popular and state-of-the-art classification methods are compared as pattern recognition tools for classification of resistance spot welding joints. Instead of using the result of a non-destructive testing technique as input variables, classifiers are trained directly with the relevant welding parameters, i.e. welding current, welding time and the type of electrode (electrode material and treatment). The algorithms are compared in terms of accuracy and area under the receiver operating characteristic (ROC) curve metrics, using nested cross-validation. Results show that although there is not a dominant classifier for every specificity/sensitivity requirement, support vector machines using radial kernel, boosting and random forest techniques obtain the best performance overallen
dc.description.sponsorshipSpanish MICINN Project CSD2010-00034 (SimulPast CONSOLIDER-INGENIO 2010) and by the Junta de Castilla y León GREX251-2009en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherManey Publishingen
dc.relation.ispartofScience and technology of welding and joining. 2015, V. 20, n. 8, p. 679-685en
dc.subjectResistance spot weldingen
dc.subjectClassificationen
dc.subjectPattern recognitionen
dc.subjectQuality controlen
dc.subjectSupport vector machinesen
dc.subjectRandom foresten
dc.subjectArtificial neural networksen
dc.subject.otherEmpresas-Gestiónes
dc.subject.otherIndustrial managementen
dc.titleDirect quality prediction in resistance spot welding process: Sensitivity, specificity and predictive accuracy comparative analysisen
dc.typeArtículoes
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.relation.publisherversionhttp://www.tandfonline.com/doi/full/10.1179/1362171815Y.0000000052
dc.identifier.doi10.1179/1362171815Y.0000000052
dc.relation.projectIDinfo:eu-repo/grantAgreement/MICINN/CSD2010-00034
dc.relation.projectIDinfo:eu-repo/grantAgreement/JCyL/GREX251-2009
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersionen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record