Classification of burn degrees in grinding by neural nets
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , , , |
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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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 |
|
_version_ |
1808129570849685504 |