Classification of burn degrees in grinding by neural nets

Detalhes bibliográficos
Autor(a) principal: Spadotto, Marcelo M. [UNESP]
Data de Publicação: 2008
Outros Autores: Aguiar, Paulo R. [UNESP], Souza, Carlos C. P. [UNESP], Bianchi, Eduardo C. [UNESP], Souza, André N. De [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/225440
Resumo: One of the problems found in the implementation of intelligent grinding process is the automatic detection of surface burn of the parts. Several systems of monitoring have been assessed by researchers in order to control the grinding process and guarantee the quality of the ground parts. However, monitoring techniques still fails in certain situations where the phenomenon changes are not completely obtained by the employed signals. The aim of this work is to attain the classification of burn degrees of the parts ground with the utilization of neural networks. The acoustic emission and power signals as well as the statistics derived from the digital signal processing of these signals are utilized as inputs of the neural networks. A surface grinding machine with an aluminum oxide grinding wheel was used to grind parts of ANSI 1020 steels in the experimental tests. The results have shown the success of classification for most of the structures studied with the best result presented by the structure having the parameter referred to as DPO and depth of cut as inputs.
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spelling Classification of burn degrees in grinding by neural netsAcoustic emissionBurnGrindingMonitoringNeural networkOne of the problems found in the implementation of intelligent grinding process is the automatic detection of surface burn of the parts. Several systems of monitoring have been assessed by researchers in order to control the grinding process and guarantee the quality of the ground parts. However, monitoring techniques still fails in certain situations where the phenomenon changes are not completely obtained by the employed signals. The aim of this work is to attain the classification of burn degrees of the parts ground with the utilization of neural networks. The acoustic emission and power signals as well as the statistics derived from the digital signal processing of these signals are utilized as inputs of the neural networks. A surface grinding machine with an aluminum oxide grinding wheel was used to grind parts of ANSI 1020 steels in the experimental tests. The results have shown the success of classification for most of the structures studied with the best result presented by the structure having the parameter referred to as DPO and depth of cut as inputs.Sao Paulo State University - Unesp - Bauru Campus Electrical Engineering Department School of Engineering - FEB, Av. Luiz Edmundo Carrijo Coube, 14-01, Cep 17033-360, Bauru - SPSao Paulo State University - Unesp - Bauru Campus Mechanical Engineering Department School of Engineering - FEB, Av. Luiz Edmundo Carrijo Coube, 14-01, Cep 17033-360, Bauru - SPSao Paulo State University - Unesp - Bauru Campus Electrical Engineering Department School of Engineering - FEB, Av. Luiz Edmundo Carrijo Coube, 14-01, Cep 17033-360, Bauru - SPSao Paulo State University - Unesp - Bauru Campus Mechanical Engineering Department School of Engineering - FEB, Av. Luiz Edmundo Carrijo Coube, 14-01, Cep 17033-360, Bauru - SPUniversidade Estadual Paulista (UNESP)Spadotto, Marcelo M. [UNESP]Aguiar, Paulo R. [UNESP]Souza, Carlos C. P. [UNESP]Bianchi, Eduardo C. [UNESP]Souza, André N. De [UNESP]2022-04-28T20:50:24Z2022-04-28T20:50:24Z2008-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject175-180Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008, p. 175-180.http://hdl.handle.net/11449/2254402-s2.0-62849086147Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008info:eu-repo/semantics/openAccess2024-06-28T13:55:21Zoai:repositorio.unesp.br:11449/225440Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:59:59.854617Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Classification of burn degrees in grinding by neural nets
title Classification of burn degrees in grinding by neural nets
spellingShingle Classification of burn degrees in grinding by neural nets
Spadotto, Marcelo M. [UNESP]
Acoustic emission
Burn
Grinding
Monitoring
Neural network
title_short Classification of burn degrees in grinding by neural nets
title_full Classification of burn degrees in grinding by neural nets
title_fullStr Classification of burn degrees in grinding by neural nets
title_full_unstemmed Classification of burn degrees in grinding by neural nets
title_sort Classification of burn degrees in grinding by neural nets
author Spadotto, Marcelo M. [UNESP]
author_facet Spadotto, Marcelo M. [UNESP]
Aguiar, Paulo R. [UNESP]
Souza, Carlos C. P. [UNESP]
Bianchi, Eduardo C. [UNESP]
Souza, André N. De [UNESP]
author_role author
author2 Aguiar, Paulo R. [UNESP]
Souza, Carlos C. P. [UNESP]
Bianchi, Eduardo C. [UNESP]
Souza, André N. De [UNESP]
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Spadotto, Marcelo M. [UNESP]
Aguiar, Paulo R. [UNESP]
Souza, Carlos C. P. [UNESP]
Bianchi, Eduardo C. [UNESP]
Souza, André N. De [UNESP]
dc.subject.por.fl_str_mv Acoustic emission
Burn
Grinding
Monitoring
Neural network
topic Acoustic emission
Burn
Grinding
Monitoring
Neural network
description One of the problems found in the implementation of intelligent grinding process is the automatic detection of surface burn of the parts. Several systems of monitoring have been assessed by researchers in order to control the grinding process and guarantee the quality of the ground parts. However, monitoring techniques still fails in certain situations where the phenomenon changes are not completely obtained by the employed signals. The aim of this work is to attain the classification of burn degrees of the parts ground with the utilization of neural networks. The acoustic emission and power signals as well as the statistics derived from the digital signal processing of these signals are utilized as inputs of the neural networks. A surface grinding machine with an aluminum oxide grinding wheel was used to grind parts of ANSI 1020 steels in the experimental tests. The results have shown the success of classification for most of the structures studied with the best result presented by the structure having the parameter referred to as DPO and depth of cut as inputs.
publishDate 2008
dc.date.none.fl_str_mv 2008-12-01
2022-04-28T20:50:24Z
2022-04-28T20:50:24Z
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 Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008, p. 175-180.
http://hdl.handle.net/11449/225440
2-s2.0-62849086147
identifier_str_mv Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008, p. 175-180.
2-s2.0-62849086147
url http://hdl.handle.net/11449/225440
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2008
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 175-180
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
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