Prediction of Dressing in Grinding Operation via Neural Networks

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorD'Addona, Doriana M.-
Autor(es): dc.creatorMatarazzo, Davide-
Autor(es): dc.creatorTeti, Roberto-
Autor(es): dc.creatorDe Aguiar, Paulo R.-
Autor(es): dc.creatorBianchi, Eduardo C.-
Autor(es): dc.creatorFornaro, Arcangelo-
Data de aceite: dc.date.accessioned2021-03-11T00:55:07Z-
Data de disponibilização: dc.date.available2021-03-11T00:55:07Z-
Data de envio: dc.date.issued2018-12-11-
Data de envio: dc.date.issued2018-12-11-
Data de envio: dc.date.issued2017-01-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.procir.2017.03.043-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/178949-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/178949-
Descrição: dc.descriptionIn 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.-
Formato: dc.format305-310-
Idioma: dc.languageen-
Relação: dc.relationProcedia CIRP-
Relação: dc.relation0,668-
Direitos: dc.rightsopenAccess-
Palavras-chave: dc.subjectAcoustic emission signal-
Palavras-chave: dc.subjectArtificial neural networks-
Palavras-chave: dc.subjectDressing-
Palavras-chave: dc.subjectgrinding-
Título: dc.titlePrediction of Dressing in Grinding Operation via Neural Networks-
Aparece nas coleções:Repositório Institucional - Unesp

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