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dc.contributor.authorMartins, Cesar H.R. [UNESP]
dc.contributor.authorAguiar, Paulo R. [UNESP]
dc.contributor.authorFrech Jr., Arminio [UNESP]
dc.contributor.authorBianchi, Eduardo C. [UNESP]
dc.date.accessioned2014-05-27T11:30:44Z
dc.date.available2014-05-27T11:30:44Z
dc.date.issued2013-09-24
dc.identifierhttp://dx.doi.org/10.3182/20130619-3-RU-3018.00222
dc.identifier.citationIFAC Proceedings Volumes (IFAC-PapersOnline), p. 1524-1529.
dc.identifier.issn1474-6670
dc.identifier.urihttp://hdl.handle.net/11449/76632
dc.description.abstractGrinding is a workpiece finishing process for advanced products and surfaces. However, the constant friction between workpiece and grinding wheel causes the latter to lose its sharpness, thereby impairing the result of the grinding process. When this occurs, the dressing process is essential to sharpen the worn grains of the grinding wheel. The dressing conditions strongly influence the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The purpose of this study was to classify the wear condition of a single-point dresser using intelligent systems whose inputs were obtained by digitally processing acoustic emission signals. Two multilayer perceptron (MLP) neural networks were compared for their classification ability, one using the root mean square (RMS) statistics and another the ratio of power (ROP) statistics as input. In this study, it was found that the harmonic content of the acoustic emission signal is influenced by the condition of the dresser, and that the condition of the tool under study can be classified by using the aforementioned statistics to feed a neural network. © IFAC.en
dc.format.extent1524-1529
dc.language.isoeng
dc.relation.ispartofIFAC Proceedings Volumes (IFAC-PapersOnline)
dc.sourceScopus
dc.subjectAcoustic emission
dc.subjectDresser wear
dc.subjectDressing operation
dc.subjectMultilayer perceptron
dc.subjectNeural network
dc.subjectAcoustic emission signal
dc.subjectClassification ability
dc.subjectFinishing process
dc.subjectGrinding operations
dc.subjectHarmonic contents
dc.subjectMulti layer perceptron
dc.subjectMultilayer perceptron neural networks
dc.subjectNeural networks model
dc.subjectAcoustic emissions
dc.subjectGrinding (machining)
dc.subjectGrinding wheels
dc.subjectIntelligent systems
dc.subjectManufacture
dc.subjectNeural networks
dc.titleNeural networks models for wear patterns recognition of single-point dresseren
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.description.affiliationElectrical Engineering Department UNESP - Univ. Estadual Paulista Faculty of Engineering, Av. Luiz E. C. Coube, 14-01, CEP 17033-360, Bauru-SP
dc.description.affiliationUnespElectrical Engineering Department UNESP - Univ. Estadual Paulista Faculty of Engineering, Av. Luiz E. C. Coube, 14-01, CEP 17033-360, Bauru-SP
dc.identifier.doi10.3182/20130619-3-RU-3018.00222
dc.rights.accessRightsAcesso aberto
dc.identifier.scopus2-s2.0-84884299018
unesp.campusUniversidade Estadual Paulista (UNESP), Faculdade de Engenharia, Baurupt
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