Neural tool condition estimation in the grinding of advanced ceramics

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dc.contributor.authorNakai, M. E. [UNESP]
dc.contributor.authorJunior, H. G. [UNESP]
dc.contributor.authorAguiar, P. R. [UNESP]
dc.contributor.authorBianchi, E. C. [UNESP]
dc.contributor.authorSpatti, D. H. [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2015-10-21T21:08:04Z
dc.date.available2015-10-21T21:08:04Z
dc.date.issued2015-01-01
dc.description.abstractCeramic parts are increasingly replacing metal parts due to their excellent physical, chemical and mechanical properties, however they also make them difficult to manufacture by traditional machining methods. The developments carried out in this work are used to estimate tool wear during the grinding of advanced ceramics. The learning process was fed with data collected from a surface grinding machine with tangential diamond wheel and alumina ceramic test specimens, in three cutting configurations: with depths of cut of 120 mu m, 70 mu m and 20 mu m. The grinding wheel speed was 35m/s and the table speed 2.3m/s. Four neural models were evaluated, namely: Multilayer Perceptron, Radial Basis Function, Generalized Regression Neural Networks and the Adaptive Neuro-Fuzzy Inference System. The models'performance evaluation routines were executed automatically, testing all the possible combinations of inputs, number of neurons, number of layers, and spreading. The computational results reveal that the neural models were highly successful in estimating tool wear, since the errors were lower than 4%.en
dc.description.affiliationUnespDepartamento de Engenharia Elétrica da Faculdade de Engenharia de Bauru, UNESP, Bauru SP, Brasil
dc.description.affiliationUnespDepartamento de Engenharia Mecânica da Faculdade de Engenharia de Bauru, UNESP, Bauru SP, Brasil
dc.format.extent62-68
dc.identifierhttp://ieeexplore.ieee.org/xpl/abstractAuthors.jsp?reload=true&arnumber=7040629
dc.identifier.citationIeee Latin America Transactions. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 13, n. 1, p. 62-68, 2015.
dc.identifier.issn1548-0992
dc.identifier.lattes1455400309660081
dc.identifier.lattes1099152007574921
dc.identifier.orcid0000-0002-9934-4465
dc.identifier.urihttp://hdl.handle.net/11449/129454
dc.identifier.wosWOS:000349781600009
dc.language.isopor
dc.publisherIeee-inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Latin America Transactions
dc.relation.ispartofjcr0.502
dc.relation.ispartofsjr0,253
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.subjectCeramic grindingen
dc.subjectRBFen
dc.subjectGRNNen
dc.subjectANFISen
dc.subjectAdvanced ceramicsen
dc.titleNeural tool condition estimation in the grinding of advanced ceramicsen
dc.typeArtigo
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIeee-inst Electrical Electronics Engineers Inc
unesp.author.lattes1455400309660081[3]
unesp.author.lattes1099152007574921
unesp.author.orcid0000-0002-9934-4465[3]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Engenharia, Baurupt

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Artigos - Engenharia Elétrica - FEB
Artigos - Engenharia Mecânica - FEB