Monitoring single-point dressers using fuzzy models

dc.contributor.authorMiranda, H. I. [UNESP]
dc.contributor.authorRocha, C. A. [UNESP]
dc.contributor.authorOliveira, P. [UNESP]
dc.contributor.authorMartins, C. [UNESP]
dc.contributor.authorAguiar, P. R. [UNESP]
dc.contributor.authorBianchi, E. C. [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-12-11T17:25:37Z
dc.date.available2018-12-11T17:25:37Z
dc.date.issued2015-01-01
dc.description.abstractGrinding causes progressive dulling and glazing of the grinding wheel grains and clogging of the voids on the wheel's surface with ground metal dust particles, which gradually increases the grinding forces. The condition of the grains at the periphery of a grinding wheel strongly influences the damage induced in a ground workpiece. Therefore, truing and dressing must be carried out frequently. Dressing is the process of conditioning the grinding wheel surface to reshape the wheel when it has lost its original shape through wear, giving the tool its original condition of efficiency. Despite the very broad range of dressing tools available today, the single-point diamond dresser is still the most widely used dressing tool due to its great versatility. The aim of this work is to predict the wear level of the single-point dresser based on acoustic emission and vibration signals used as input variables for fuzzy models. Experimental tests were performed with synthetic diamond dressers on a surface-grinding machine equipped with an aluminum oxide grinding wheel. Acoustic emission and vibration sensors were attached to the tool holder and the signals were captured at 2MHz. During the tests, the wear of the diamond tip was measured every 20 passes using a microscope with 10 to 100 X magnification. A study was conducted of the frequency content of the signals, choosing the frequency bands that best correlate with the diamond's wear. Digital band-pass filters were applied to the raw signals, after which two statistics were calculated to serve as the inputs for the fuzzy models. The results indicate that the fuzzy models using the aforementioned signal statistics are highly effective for predicting the wear level of the dresser.en
dc.description.affiliationUniv. Estadual Paulista - UNESP - Faculty of Engineering Department of Electrical Engineering
dc.description.affiliationUniv. Estadual Paulista - UNESP - Faculty of Engineering Department of Mechanical Engineering
dc.description.affiliationUnespUniv. Estadual Paulista - UNESP - Faculty of Engineering Department of Electrical Engineering
dc.description.affiliationUnespUniv. Estadual Paulista - UNESP - Faculty of Engineering Department of Mechanical Engineering
dc.format.extent281-286
dc.identifierhttp://dx.doi.org/10.1016/j.procir.2015.06.050
dc.identifier.citationProcedia CIRP, v. 33, p. 281-286.
dc.identifier.doi10.1016/j.procir.2015.06.050
dc.identifier.issn2212-8271
dc.identifier.lattes1455400309660081
dc.identifier.lattes8858800699425352
dc.identifier.orcid0000-0002-9934-4465
dc.identifier.orcid0000-0003-3534-974X
dc.identifier.scopus2-s2.0-84939791160
dc.identifier.urihttp://hdl.handle.net/11449/177466
dc.language.isoeng
dc.relation.ispartofProcedia CIRP
dc.relation.ispartofsjr0,668
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectAcoustic
dc.subjectDressing
dc.subjectFuzzy logic
dc.subjectGrinding
dc.subjectVibration
dc.subjectWear
dc.titleMonitoring single-point dressers using fuzzy modelsen
dc.typeTrabalho apresentado em evento
unesp.author.lattes1455400309660081[5]
unesp.author.lattes8858800699425352[4]
unesp.author.orcid0000-0002-9934-4465[5]
unesp.author.orcid0000-0003-3534-974X[4]

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