Tool condition monitoring of single-point dressing operation by digital signal processing of AE and AI

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniv Naples Federico II-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorD'Addona, Doriana M.-
Autor(es): dc.creatorConte, Salvatore-
Autor(es): dc.creatorLopes, Wenderson Nascimento [UNESP]-
Autor(es): dc.creatorAguiar, Paulo R. de [UNESP]-
Autor(es): dc.creatorBianchi, Eduardo C. [UNESP]-
Autor(es): dc.creatorTeti, Roberto-
Autor(es): dc.creatorTeti, R.-
Autor(es): dc.creatorDAddona, D. M.-
Data de aceite: dc.date.accessioned2022-02-22T00:22:02Z-
Data de disponibilização: dc.date.available2022-02-22T00:22:02Z-
Data de envio: dc.date.issued2020-12-10-
Data de envio: dc.date.issued2020-12-10-
Data de envio: dc.date.issued2018-01-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.procir.2017.12.218-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/197859-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/197859-
Descrição: dc.descriptionThis work aims at determining the right moment to stop single-point dressing the grinding wheel in order to optimize the grinding process as a whole. Acoustic emission signals and signal processing tools are used as primary approach. An acoustic emission (AE) sensor was connected to a signal processing module. The AE sensor was attached to the dresser holder, which was specifically built to perform dressing tests. In this work there were three types of test where the edit parameters of each dressing test are: the passes number, the dressing speed, the width of action of the dresser, the dressing time and the sharpness. Artificial Neural Networks (ANNs) technique is employed to classify and predict the best moment for stopping the dressing operation. During the ANNs use, the results from Supervised Neural Networks and Unsupervised Neural Networks are compared. (C) 2017 The Authors. Published by Elsevier B.V.-
Descrição: dc.descriptionUniv Naples Federico II, Fraunhofer Joint Lab Excellence Adv Prod Technol, Dept Chem Mat & Ind Prod Engn, Piazzale Tecchio 80, I-80125 Naples, Italy-
Descrição: dc.descriptionUniv Estadual Paulista Unesp, Sch Engn, Ave Luiz Ed C Coube 14-01, BR-17033360 Bauru, SP, Brazil-
Descrição: dc.descriptionUniv Estadual Paulista Unesp, Sch Engn, Ave Luiz Ed C Coube 14-01, BR-17033360 Bauru, SP, Brazil-
Formato: dc.format307-312-
Idioma: dc.languageen-
Publicador: dc.publisherElsevier B.V.-
Relação: dc.relation11th Cirp Conference On Intelligent Computation In Manufacturing Engineering-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectDressing-
Palavras-chave: dc.subjectAcustic emission signal-
Palavras-chave: dc.subjectVibration signal-
Palavras-chave: dc.subjectTool wear-
Palavras-chave: dc.subjectArtificial neural networks-
Título: dc.titleTool condition monitoring of single-point dressing operation by digital signal processing of AE and AI-
Aparece nas coleções:Repositório Institucional - Unesp

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