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dc.contributor.authorMURUA, MAIALEN
dc.contributor.authorVEIGA SUAREZ, FERNANDO
dc.contributor.authorORTEGA LALMOLDA, JUAN ANTONIO
dc.contributor.authorPenalva Oscoz, Mariluz
dc.contributor.authorDIEZ OLIVAN, ALBERTO
dc.date.accessioned2020-09-14T15:08:32Z
dc.date.available2020-09-14T15:08:32Z
dc.date.issued2020-09
dc.identifier.citationMURUA, MAIALEN, FERNANDO VEIGA SUAREZ, JUAN ANTONIO ORTEGA LALMOLDA, Mariluz Penalva Oscoz, and ALBERTO DIEZ OLIVAN. “MOTOR DE ANÁLISIS BASADO EN TÉCNICAS DE APRENDIZAJE AUTOMÁTICO PARA LA IDENTIFICACIÓN DE VARIABLES CRÍTICAS EN PROCESOS MULTIETAPA: APLICACIÓN A LA INSTALACIÓN DE REMACHES CIEGOS.” DYNA INGENIERIA E INDUSTRIA 95, no. 1 (2020): 534–540. doi:10.6036/9403.en
dc.identifier.issn0012-7361en
dc.identifier.urihttp://hdl.handle.net/11556/979
dc.description.abstractQuality control in manufacturing is a recurrent topic as the ultimate goals are to produce high quality products with less cost. Mostly, the problems related to manufacturing processes are addressed focusing on the process itself putting aside other operations that belong to the part’s history. This research work presents a Machine Learning-based analysis engine for non-expert users which identifies relationships among variables throughout the manufacturing line. The developed tool was used to analyze the installation of blind fasteners in aeronautical structures, with the aim of identifying critical variables for the quality of the installed fastener, throughout the fastening and drilling stages. The results provide evidence that drilling stage affects to the fastening, especially to the formed head’s diameter. Also, the most critical phase in fastening, which is when the plastic deformation occurs, was identified. The results also revealed that the chosen process parameters, thickness of the plate and the faster type influence on the quality of the installed fastener.en
dc.description.sponsorshipThis project has received funding from the European Union’s 2020 research and innovation program under grant agreements No 686827 and No 723698.en
dc.language.isoengen
dc.publisherFederacion de Asociaciones de Ingenieros Industriales de Españaen
dc.titleMACHINE LEARNING-BASED ANALYSIS ENGINE TO IDENTIFY CRITICAL VARIABLES IN MULTI-STAGE PROCESSES: APPLICATION TO THE INSTALLATION OF BLIND FASTENERSen
dc.title.alternativeMOTOR DE ANÁLISIS BASADO EN TÉCNICAS DE APRENDIZAJE AUTOMÁTICO PARA LA IDENTIFICACIÓN DE VARIABLES CRÍTICAS EN PROCESOS MULTIETAPA: APLICACIÓN A LA INSTALACIÓN DE REMACHES CIEGOSen
dc.typejournal articleen
dc.identifier.doi10.6036/9403en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/686827/EU/BLINDFAST: INNOVATIVE BLIND FASTENER MONITORING TECHNOLOGY FOR QUALITY CONTROL/BLINDFASTen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/723698/EU/Integrated Zero Defect Manufacturing Solution for High Value Adding Multi-stage Manufacturing Systems/ForZDMen
dc.rights.accessRightsopen accessen
dc.subject.keywordsAnalysis Engineen
dc.subject.keywordsMulti-Stage Processesen
dc.subject.keywordsCritical Variablesen
dc.subject.keywordsMachine Learningen
dc.subject.keywordsBlind Fastenersen
dc.identifier.essn1989-1490en
dc.issue.number1en
dc.journal.titleDYNA INGENIERIA E INDUSTRIAen
dc.page.final540en
dc.page.initial534en
dc.volume.number95en


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