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.accessioned2018-12-11T16:57:09Z
dc.date.available2018-12-11T16:57:09Z
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 120μm, 70μm and 20μm. 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.affiliationDepartamento de Engenharia Elétrica da Faculdade de Engenharia de Bauru, UNESP
dc.description.affiliationUnespDepartamento de Engenharia Elétrica da Faculdade de Engenharia de Bauru, UNESP
dc.format.extent62-68
dc.identifierhttp://dx.doi.org/10.1109/TLA.2015.7040629
dc.identifier.citationIEEE Latin America Transactions, v. 13, n. 1, p. 62-68, 2015.
dc.identifier.doi10.1109/TLA.2015.7040629
dc.identifier.file2-s2.0-84923199108.pdf
dc.identifier.issn1548-0992
dc.identifier.lattes1455400309660081
dc.identifier.orcid0000-0002-9934-4465
dc.identifier.scopus2-s2.0-84923199108
dc.identifier.urihttp://hdl.handle.net/11449/171785
dc.language.isoeng
dc.relation.ispartofIEEE Latin America Transactions
dc.relation.ispartofsjr0,253
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectadvanced ceramics
dc.subjectANFIS
dc.subjectCeramic grinding
dc.subjectGRNN
dc.subjectRBF
dc.titleNeural tool condition estimation in the grinding of advanced ceramicsen
dc.typeArtigo
unesp.author.lattes1455400309660081[3]
unesp.author.orcid0000-0002-9934-4465[3]

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