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dc.contributor.authorRuiz Lozano, Leandro 
dc.contributor.authorTorres, Manuel 
dc.contributor.authorGómez Vilanova, Alejandro 
dc.contributor.authorDíaz Carrillo, Sebastián 
dc.contributor.authorGonzález Sesma, José Manuel 
dc.contributor.authorCavas Martínez, Francisco 
dc.date.accessioned2021-05-25T08:40:22Z
dc.date.available2021-05-25T08:40:22Z
dc.date.issued2020
dc.identifier.citationRuiz, L.; Torres, M.; Gómez, A.; Díaz, S.; González, J.M.; Cavas, F. Detection and classification of aircraft fixation elements during manufacturing processes using a convolutional neural network. Appl. Sci. 2020, 10, 6856. https://doi.org/10.3390/app10196856es_ES
dc.identifier.issn2076-3417
dc.description.abstractThe aerospace sector is one of the main economic drivers that strengthens our present, constitutes our future and is a source of competitiveness and innovation with great technological development capacity. In particular, the objective of manufacturers on assembly lines is to automate the entire process by using digital technologies as part of the transition toward Industry 4.0. In advanced manufacturing processes, artificial vision systems are interesting because their performance influences the liability and productivity of manufacturing processes. Therefore, developing and validating accurate, reliable and flexible vision systems in uncontrolled industrial environments is a critical issue. This research deals with the detection and classification of fasteners in a real, uncontrolled environment for an aeronautical manufacturing process, using machine learning techniques based on convolutional neural networks. Our system achieves 98.3% accuracy in a processing time of 0.8 ms per image. The results reveal that the machine learning paradigm based on a neural network in an industrial environment is capable of accurately and reliably estimating mechanical parameters to improve the performance and flexibility of advanced manufacturing processing of large parts with structural responsibility.es_ES
dc.description.sponsorshipThis publication was carried out as part of the project Nuevas Uniones de estructuras aeronáuticas reference number IDI-20180754. This project has been supported by the Spanish Ministry of Ciencia e Innovación and Centre for Industrial Technological Development (CDTI).es_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rightsCopyright © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.es_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.titleDetection and classification of aircraft fixation elements during manufacturing processes using a convolutional neural networkes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.subjectAdvanced manufacturinges_ES
dc.subjectIndustry 4.0es_ES
dc.subjectPoduct developmentes_ES
dc.subjectProduct designes_ES
dc.subjectDesign for X methodses_ES
dc.subjectTolerancinges_ES
dc.subject.otherExpresión Gráfica en Ingenieríaes_ES
dc.identifier.urihttp://hdl.handle.net/10317/9393
dc.identifier.doi10.3390/app10196856
dc.identifier.urlhttps://www.mdpi.com/2076-3417/10/19/6856
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.relation.projectIDIDI-20180754es_ES
dc.subject.unesco3310 Tecnología Industriales_ES
dc.subject.unesco3313.15 Diseño de Maquinases_ES
dc.contributor.funderMinisterio de Ciencia e Innovaciónes_ES


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