Prediction of Dressing in Grinding Operation via Neural Networks
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.procir.2017.03.043 http://hdl.handle.net/11449/178949 |
Resumo: | In 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. |
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Repositório Institucional da UNESP |
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Prediction of Dressing in Grinding Operation via Neural NetworksAcoustic emission signalArtificial neural networksDressinggrindingIn 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.Fraunhofer 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 80University Estadual Paulista UNESP Faculty of Engineering Department of Electrical EngineeringAr.Ter. SrL, Via Padula 56/58University Estadual Paulista UNESP Faculty of Engineering Department of Electrical EngineeringUniversity of Naples Federico IIUniversidade Estadual Paulista (Unesp)Ar.Ter. SrLD'Addona, Doriana M.Matarazzo, DavideTeti, RobertoDe Aguiar, Paulo R. [UNESP]Bianchi, Eduardo C. [UNESP]Fornaro, Arcangelo2018-12-11T17:32:51Z2018-12-11T17:32:51Z2017-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject305-310http://dx.doi.org/10.1016/j.procir.2017.03.043Procedia CIRP, v. 62, p. 305-310.2212-8271http://hdl.handle.net/11449/17894910.1016/j.procir.2017.03.0432-s2.0-85020699153Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProcedia CIRP0,668info:eu-repo/semantics/openAccess2024-06-28T13:55:20Zoai:repositorio.unesp.br:11449/178949Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:06:46.989143Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Prediction of Dressing in Grinding Operation via Neural Networks |
title |
Prediction of Dressing in Grinding Operation via Neural Networks |
spellingShingle |
Prediction of Dressing in Grinding Operation via Neural Networks D'Addona, Doriana M. Acoustic emission signal Artificial neural networks Dressing grinding |
title_short |
Prediction of Dressing in Grinding Operation via Neural Networks |
title_full |
Prediction of Dressing in Grinding Operation via Neural Networks |
title_fullStr |
Prediction of Dressing in Grinding Operation via Neural Networks |
title_full_unstemmed |
Prediction of Dressing in Grinding Operation via Neural Networks |
title_sort |
Prediction of Dressing in Grinding Operation via Neural Networks |
author |
D'Addona, Doriana M. |
author_facet |
D'Addona, Doriana M. Matarazzo, Davide Teti, Roberto De Aguiar, Paulo R. [UNESP] Bianchi, Eduardo C. [UNESP] Fornaro, Arcangelo |
author_role |
author |
author2 |
Matarazzo, Davide Teti, Roberto De Aguiar, Paulo R. [UNESP] Bianchi, Eduardo C. [UNESP] Fornaro, Arcangelo |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
University of Naples Federico II Universidade Estadual Paulista (Unesp) Ar.Ter. SrL |
dc.contributor.author.fl_str_mv |
D'Addona, Doriana M. Matarazzo, Davide Teti, Roberto De Aguiar, Paulo R. [UNESP] Bianchi, Eduardo C. [UNESP] Fornaro, Arcangelo |
dc.subject.por.fl_str_mv |
Acoustic emission signal Artificial neural networks Dressing grinding |
topic |
Acoustic emission signal Artificial neural networks Dressing grinding |
description |
In 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. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01 2018-12-11T17:32:51Z 2018-12-11T17:32:51Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.procir.2017.03.043 Procedia CIRP, v. 62, p. 305-310. 2212-8271 http://hdl.handle.net/11449/178949 10.1016/j.procir.2017.03.043 2-s2.0-85020699153 |
url |
http://dx.doi.org/10.1016/j.procir.2017.03.043 http://hdl.handle.net/11449/178949 |
identifier_str_mv |
Procedia CIRP, v. 62, p. 305-310. 2212-8271 10.1016/j.procir.2017.03.043 2-s2.0-85020699153 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Procedia CIRP 0,668 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
305-310 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129286071123968 |