Show simple item record

dc.contributor.authorNakai, Mauricio E. [UNESP]
dc.contributor.authorGuillardi Júnior, Hildo [UNESP]
dc.contributor.authorSpadotto, Marcelo M. [UNESP]
dc.contributor.authorAguiar, Paulo R. [UNESP]
dc.contributor.authorBianchi, Eduardo C. [UNESP]
dc.date.accessioned2014-05-27T11:26:15Z
dc.date.available2014-05-27T11:26:15Z
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.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.relation.ispartofProceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2011
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.typeTrabalho apresentado em evento
dcterms.licensehttp://www.actapress.com/privacy.aspx#copyrighttitle
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.identifier.scopus2-s2.0-84883526299
unesp.campusUniversidade Estadual Paulista (UNESP), Faculdade de Engenharia, Baurupt
Localize o texto completo

Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record