Prediction of Dressing in Grinding Operation via Neural Networks

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dc.contributor.authorD'Addona, Doriana M.
dc.contributor.authorMatarazzo, Davide
dc.contributor.authorTeti, Roberto
dc.contributor.authorDe Aguiar, Paulo R. [UNESP]
dc.contributor.authorBianchi, Eduardo C. [UNESP]
dc.contributor.authorFornaro, Arcangelo
dc.contributor.institutionUniversity of Naples Federico II
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionAr.Ter. SrL
dc.date.accessioned2018-12-11T17:32:51Z
dc.date.available2018-12-11T17:32:51Z
dc.date.issued2017-01-01
dc.description.abstractIn order to obtain a modelling and prediction of tool wear in grinding operations, a Cognitive System has been employed to observe the dressing need and its trend. This paper aims to find a methodology to characterize the condition of the wheel during grinding operations and, by the use of cognitive paradigms, to understand the need of dressing. The Acoustic Emission signal from the grinding operation has been employed to characterize the wheel condition and, by the feature extraction of such signal, a cognitive system, based on Artificial Neural Networks, has been implemented.en
dc.description.affiliationFraunhofer Joint Laboratory of Excellence on Advanced Production Technology (Fh-J-LEAPT Naples) Department of Chemical Materials and Industrial Production Engineering University of Naples Federico II, Piazzale Tecchio 80
dc.description.affiliationUniversity Estadual Paulista UNESP Faculty of Engineering Department of Electrical Engineering
dc.description.affiliationAr.Ter. SrL, Via Padula 56/58
dc.description.affiliationUnespUniversity Estadual Paulista UNESP Faculty of Engineering Department of Electrical Engineering
dc.format.extent305-310
dc.identifierhttp://dx.doi.org/10.1016/j.procir.2017.03.043
dc.identifier.citationProcedia CIRP, v. 62, p. 305-310.
dc.identifier.doi10.1016/j.procir.2017.03.043
dc.identifier.issn2212-8271
dc.identifier.scopus2-s2.0-85020699153
dc.identifier.urihttp://hdl.handle.net/11449/178949
dc.language.isoeng
dc.relation.ispartofProcedia CIRP
dc.relation.ispartofsjr0,668
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectAcoustic emission signal
dc.subjectArtificial neural networks
dc.subjectDressing
dc.subjectgrinding
dc.titlePrediction of Dressing in Grinding Operation via Neural Networksen
dc.typeTrabalho apresentado em evento
unesp.author.lattes1099152007574921[5]
unesp.author.orcid0000-0003-2675-4276[5]

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Artigos - Engenharia Mecânica - FEB