Anfis applied to the prediction of surface roughness in grinding of advanced ceramics

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorNakai, Mauricio E.
dc.creatorGuillardi Júnior, Hildo
dc.creatorSpadotto, Marcelo M.
dc.creatorAguiar, Paulo R.
dc.creatorBianchi, Eduardo C.
dc.date2014-05-27T11:26:15Z
dc.date2016-10-25T18:35:57Z
dc.date2014-05-27T11:26:15Z
dc.date2016-10-25T18:35:57Z
dc.date2011-12-01
dc.date.accessioned2017-04-06T01:54:58Z
dc.date.available2017-04-06T01:54:58Z
dc.identifierProceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2011, p. 329-334.
dc.identifierhttp://hdl.handle.net/11449/72896
dc.identifierhttp://acervodigital.unesp.br/handle/11449/72896
dc.identifier10.2316/P.2011.716-005
dc.identifier2-s2.0-84883526299
dc.identifierhttp://dx.doi.org/10.2316/P.2011.716-005
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/893729
dc.descriptionThis 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.
dc.languageeng
dc.relationProceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2011
dc.rightsinfo:eu-repo/semantics/closedAccess
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 ceramics
dc.typeOtro


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