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dc.contributor.authorPontes, Fabricio Jose [UNESP]
dc.contributor.authorde Paiva, Anderson Paulo
dc.contributor.authorBalestrassi, Pedro Paulo
dc.contributor.authorFerreira, Joao Roberto
dc.contributor.authorda Silva, Messias Borges [UNESP]
dc.date.accessioned2014-05-20T15:31:58Z
dc.date.available2014-05-20T15:31:58Z
dc.date.issued2012-07-01
dc.identifierhttp://dx.doi.org/10.1016/j.eswa.2012.01.058
dc.identifier.citationExpert Systems With Applications. Oxford: Pergamon-Elsevier B.V. Ltd, v. 39, n. 9, p. 7776-7787, 2012.
dc.identifier.issn0957-4174
dc.identifier.urihttp://hdl.handle.net/11449/40985
dc.description.abstractThis work presents a study on the applicability of radial base function (RBF) neural networks for prediction of Roughness Average (R-a) in the turning process of SAE 52100 hardened steel, with the use of Taguchi's orthogonal arrays as a tool to design parameters of the network. Experiments were conducted with training sets of different sizes to make possible to compare the performance of the best network obtained from each experiment. The following design factors were considered: (i) number of radial units. (ii) algorithm for selection of radial centers and (iii) algorithm for selection of the spread factor of the radial function. Artificial neural networks (ANN) models obtained proved capable to predict surface roughness in accurate, precise and affordable way. Results pointed significant factors for network design have significant influence on network performance for the task proposed. The work concludes that the design of experiments (DOE) methodology constitutes a better approach to the design of RBF networks for roughness prediction than the most common trial and error approach. (C) 2012 Elsevier Ltd. All rights reserved.en
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.format.extent7776-7787
dc.language.isoeng
dc.publisherPergamon-Elsevier B.V. Ltd
dc.relation.ispartofExpert Systems with Applications
dc.sourceWeb of Science
dc.subjectRBF neural networksen
dc.subjectTaguchi methodsen
dc.subjectHard turningen
dc.subjectSurface roughnessen
dc.titleOptimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi's orthogonal arraysen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderPergamon-Elsevier B.V. Ltd
dc.contributor.institutionUniversidade Federal de Itajubá (UNIFEI)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.description.affiliationUniversidade Federal de Itajubá (UNIFEI), Inst Ind Engn, BR-37500903 Itajuba, MG, Brazil
dc.description.affiliationSão Paulo State Univ, Fac Engn Guaratingueta, BR-12516410 Guaratingueta, SP, Brazil
dc.description.affiliationUnespSão Paulo State Univ, Fac Engn Guaratingueta, BR-12516410 Guaratingueta, SP, Brazil
dc.identifier.doi10.1016/j.eswa.2012.01.058
dc.identifier.wosWOS:000303281600020
dc.rights.accessRightsAcesso restrito
dc.description.sponsorshipIdFAPEMIG: PE 024/2008
unesp.campusUniversidade Estadual Paulista (UNESP), Faculdade de Engenharia, Guaratinguetápt
dc.identifier.lattes9507655803234261
unesp.author.lattes9507655803234261
dc.relation.ispartofjcr3.768
dc.relation.ispartofsjr1,271
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