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dc.contributor.authorBustillo Iglesias, Andrés 
dc.contributor.authorSedano, Javier
dc.contributor.authorCuriel Herrera, Leticia Elena 
dc.contributor.authorVillar, José Ramón
dc.contributor.authorCorchado, Emilio 
dc.date.accessioned2023-02-07T12:06:46Z
dc.date.available2023-02-07T12:06:46Z
dc.date.issued2009
dc.identifier.isbn978-3-540-93905-4
dc.identifier.isbn978-3-540-93904-7
dc.identifier.issn1867-5662
dc.identifier.urihttp://hdl.handle.net/10259/7411
dc.description.abstractIn this paper we present a soft computing system developed to optimize the laser milling manufacture of high value steel components, a relatively new and interesting industrial technique. This multidisciplinary study is based on the application of neural projection models in conjunction with identification systems, in order to find the optimal operating conditions in this industrial issue. Sensors on a laser milling centre capture the data used in this industrial case study defined under the frame of a machine-tool that manufactures steel components like high value molds and dies. The presented model is based on a two-phase application. The first phase uses a neural projection model capable of determine if the data collected is informative enough based on the existence of internal patterns. The second phase is focus on identifying a model for the laser-milling process based on low-order models such as Black Box ones. The whole system is capable of approximating the optimal form of the model. Finally, it is shown that the Box-Jenkins algorithm, which calculates the function of a linear system from its input and output samples, is the most appropriate model to control such industrial task for the case of steel components.en
dc.description.sponsorshipThis research has been partially supported through project BU006A08 of JCyL and through project CIT-020000-2008-2 of Spanish Ministry of Education and Innovation.es
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherSpringeres
dc.relation.ispartofComputer Recognition Systems 3, p. 601–609es
dc.subjectTest Pieceen
dc.subjectAngle Erroren
dc.subjectWall Angleen
dc.subjectSteel Componenten
dc.subjectSoft Computing Modelen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleA Soft Computing System for Modelling the Manufacture of Steel Componentsen
dc.typeinfo:eu-repo/semantics/bookPartes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1007/978-3-540-93905-4_70es
dc.identifier.doi10.1007/978-3-540-93905-4_70
dc.relation.projectIDinfo:eu-repo/grantAgreement/Junta de Castilla y León//BU006A08//Arquitectura distribuida inteligente para la gestión de entornos de dependencia y hospitalarios/es
dc.relation.projectIDinfo:eu-repo/grantAgreement/MICINN//CIT-020000-2008-2/ES/Desarrollo de un sistema experto para el ajuste automático de fresadoras (QUICK)/es
dc.identifier.essn1867-5670
dc.volume.number57es
dc.page.initial601es
dc.page.final609es
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones


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