Prediction of Dressing in Grinding Operation via Neural Networks

Detalhes bibliográficos
Autor(a) principal: D'Addona, Doriana M.
Data de Publicação: 2017
Outros Autores: Matarazzo, Davide, Teti, Roberto, De Aguiar, Paulo R. [UNESP], Bianchi, Eduardo C. [UNESP], Fornaro, Arcangelo
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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spelling 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/openAccess2021-10-23T21:44:26Zoai:repositorio.unesp.br:11449/178949Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:44:26Repositório 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_ 1799965382974373888

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