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dc.contributor.authorNakai, Mauricio E.-
dc.contributor.authorGuillardi Júnior, Hildo-
dc.contributor.authorSpadotto, Marcelo M.-
dc.contributor.authorAguiar, Paulo R.-
dc.contributor.authorBianchi, Eduardo C.-
dc.date.accessioned2014-05-27T11:26:15Z-
dc.date.accessioned2016-10-25T18:35:57Z-
dc.date.available2014-05-27T11:26:15Z-
dc.date.available2016-10-25T18:35:57Z-
dc.date.issued2011-12-01-
dc.identifierhttp://dx.doi.org/10.2316/P.2011.716-005-
dc.identifier.citationProceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2011, p. 329-334.-
dc.identifier.urihttp://hdl.handle.net/11449/72896-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/72896-
dc.description.abstractThis paper introduces a methodology for predicting the surface roughness of advanced ceramics using Adaptive Neuro-Fuzzy Inference System (ANFIS). To this end, a grinding machine was used, equipped with an acoustic emission sensor and a power transducer connected to the electric motor rotating the diamond grinding wheel. The alumina workpieces used in this work were pressed and sintered into rectangular bars. Acoustic emission and cutting power signals were collected during the tests and digitally processed to calculate the mean, standard deviation, and two other statistical data. These statistics, as well the root mean square of the acoustic emission and cutting power signals were used as input data for ANFIS. The output values of surface roughness (measured during the tests) were implemented for training and validation of the model. The results indicated that an ANFIS network is an excellent tool when applied to predict the surface roughness of ceramic workpieces in the grinding process.en
dc.format.extent329-334-
dc.language.isoeng-
dc.sourceScopus-
dc.subjectAcoustic emission-
dc.subjectANFIS-
dc.subjectCutting power-
dc.subjectGrinding-
dc.subjectNeural network-
dc.subjectSurface roughness-
dc.subjectAcoustic emission sensors-
dc.subjectAdaptive neuro-fuzzy inference system-
dc.subjectDiamond grinding wheel-
dc.subjectPower transducers-
dc.subjectStandard deviation-
dc.subjectStatistical datas-
dc.subjectAcoustic emission testing-
dc.subjectAcoustic emissions-
dc.subjectArtificial intelligence-
dc.subjectCeramic materials-
dc.subjectForecasting-
dc.subjectGrinding (machining)-
dc.subjectNeural networks-
dc.subjectSintered alumina-
dc.subjectSintering-
dc.subjectSoft computing-
dc.titleAnfis applied to the prediction of surface roughness in grinding of advanced ceramicsen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationDepartment of Electrical School of Engineering - FEB Universidade Estadual Paulista (UNESP), Av. Luiz Edmundo Carrijo Coube, 14-01, Vargem Limpa, Cep 17033-360, Bauru-SP-
dc.description.affiliationDepartment of Mechanical Engineering School of Engineering - FEB Universidade Estadual Paulista (UNESP), Av. Luiz Edmundo Carrijo Coube, 14-01, Vargem Limpa, Cep 17033-360, Bauru-SP-
dc.description.affiliationUnespDepartment of Electrical School of Engineering - FEB Universidade Estadual Paulista (UNESP), Av. Luiz Edmundo Carrijo Coube, 14-01, Vargem Limpa, Cep 17033-360, Bauru-SP-
dc.description.affiliationUnespDepartment of Mechanical Engineering School of Engineering - FEB Universidade Estadual Paulista (UNESP), Av. Luiz Edmundo Carrijo Coube, 14-01, Vargem Limpa, Cep 17033-360, Bauru-SP-
dc.identifier.doi10.2316/P.2011.716-005-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofProceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2011-
dc.identifier.scopus2-s2.0-84883526299-
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