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dc.contributor.authorVillar, José R.
dc.contributor.authorCorchado, Emilio 
dc.contributor.authorSedano, Javier
dc.contributor.authorCuriel Herrera, Leticia Elena 
dc.contributor.authorVillar, José Ramón
dc.date.accessioned2024-02-02T12:21:01Z
dc.date.available2024-02-02T12:21:01Z
dc.date.issued2012-07
dc.identifier.issn0266-4720
dc.identifier.urihttp://hdl.handle.net/10259/8559
dc.description.abstractThis interdisciplinary research is based on the application of unsupervized connectionist architectures in conjunction with modelling systems and on the determining of the optimal operating conditions of a new high precision industrial process known as laser milling. Laser milling is a relatively new micro-manufacturing technique in the production of high-value industrial components. The industrial problem is defined by a data set relayed through standard sensors situated on a laser-milling centre, which is a machine tool for manufacturing high-value micro-moulds, micro-dies and micro-tools. The new three-phase industrial system presented in this study is capable of identifying a model for the laser-milling process based on low-order models. The first two steps are based on the use of unsupervized connectionist models. The first step involves the analysis of the data sets that define each case study to identify if they are informative enough or if the experiments have to be performed again. In the second step, a feature selection phase is performed to determine the main variables to be processed in the third step. In this last step, the results of the study provide a model for a laser-milling procedure based on low-order models, such as black-box, in order to approximate the optimal form of the laser-milling process. The three-step model has been tested with real data obtained for three different materials: aluminium, cooper and hardened steel. These three materials are used in the manufacture of micro-moulds, micro-coolers and micro-dies, high-value tools for the medical and automotive industries among others. As the model inputs are standard data provided by the laser-milling centre, the industrial implementation of the model is immediate. Thus, this study demonstrates how a high precision industrial process can be improved using a combination of artificial intelligence and identification techniques.en
dc.description.sponsorshipThis research has been partially supported throughtheJuntadeCastillayLeo´n’sproject BU006A08andtheSpanishMinistryofScience and Technology’s project [TIN2008-06681C06-04].Theauthorswouldalsoliketothank themanufacturer of components for vehicle interiors,GrupoAntolin Ingenierıa, S.A. as partoftheMAGNO2008-1028.CENITProject fundedbytheSpanishMinistryofScienceand Innovation.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherBlackwell Publishingen
dc.relation.ispartofExpert Systems. 2012, V. 29, n. 3, p. 276-299en
dc.subjectUnsupervized learningen
dc.subjectExploratory projection pursuiten
dc.subjectModelling systemsen
dc.subjectIndustrial applicationsen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleOptimizing the operating conditions in a high precision industrial process using soft computing techniquesen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/closedAccesses
dc.relation.publisherversionhttps://doi.org/10.1111/j.1468-0394.2011.00588.xes
dc.identifier.doi10.1111/j.1468-0394.2011.00588.x
dc.identifier.essn1468-0394
dc.journal.titleExpert Systemsen
dc.volume.number29es
dc.issue.number3es
dc.page.initial276es
dc.page.final299es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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