Monitoring of grinding burn by AE and vibration signals

Detalhes bibliográficos
Autor(a) principal: Neto, Rodolpho F. Godoy [UNESP]
Data de Publicação: 2014
Outros Autores: Marchi, Marcelo [UNESP], Martins, Cesar [UNESP], Aguiar, Paulo R. [UNESP], Bianchi, Eduardo [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/171598
Resumo: The grinding process is widely used in surface finishing of steel parts and corresponds to one of the last steps in the manufacturing process. Thus, it's essential to have a reliable monitoring of this process. In grinding of metals, the phenomenon of burn is one of the worst faults to be avoided. Therefore, a monitoring system able to identify this phenomenon would be of great importance for the process. Thus, the aim of this work is the monitoring of burn during the grinding process through an intelligent system that uses acoustic emission (AE) and vibration signals as inputs. Tests were performed on a surface grinding machine, workpiece SAE 1020 and aluminum oxide grinding wheel were used. The acquisition of the vibration signals and AE was done by means of an oscilloscope with a sampling rate of 2MHz. By analyzing the frequency spectra of these signals it was possible to determine the frequency bands that best characterized the phenomenon of burn. These bands were used as inputs to an artificial neural networks capable of classifying the surface condition of the part. The results of this study allowed characterizing the surface of the work piece into three groups: No burn, burn and high surface roughness. The selected neural model has produced good results for classifying the three patterns studied.
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spelling Monitoring of grinding burn by AE and vibration signalsAcoustic emissionBurnGrinding processMonitoringNeural network applicationThe grinding process is widely used in surface finishing of steel parts and corresponds to one of the last steps in the manufacturing process. Thus, it's essential to have a reliable monitoring of this process. In grinding of metals, the phenomenon of burn is one of the worst faults to be avoided. Therefore, a monitoring system able to identify this phenomenon would be of great importance for the process. Thus, the aim of this work is the monitoring of burn during the grinding process through an intelligent system that uses acoustic emission (AE) and vibration signals as inputs. Tests were performed on a surface grinding machine, workpiece SAE 1020 and aluminum oxide grinding wheel were used. The acquisition of the vibration signals and AE was done by means of an oscilloscope with a sampling rate of 2MHz. By analyzing the frequency spectra of these signals it was possible to determine the frequency bands that best characterized the phenomenon of burn. These bands were used as inputs to an artificial neural networks capable of classifying the surface condition of the part. The results of this study allowed characterizing the surface of the work piece into three groups: No burn, burn and high surface roughness. The selected neural model has produced good results for classifying the three patterns studied.Mechanical Department, School of Engineering, Univ. Estadual Paulista - UNESP, Av. Luiz E.C. Coube, 14-01, 17033-0360, Bauru - SPElectrical Engineering Department, School of Engineering, Univ. Estadual Paulista - UNESP, Av. Luiz E.C. Coube, 14-01, 17033-0360, Bauru - SPMechanical Department, School of Engineering, Univ. Estadual Paulista - UNESP, Av. Luiz E.C. Coube, 14-01, 17033-0360, Bauru - SPElectrical Engineering Department, School of Engineering, Univ. Estadual Paulista - UNESP, Av. Luiz E.C. Coube, 14-01, 17033-0360, Bauru - SPUniversidade Estadual Paulista (Unesp)Neto, Rodolpho F. Godoy [UNESP]Marchi, Marcelo [UNESP]Martins, Cesar [UNESP]Aguiar, Paulo R. [UNESP]Bianchi, Eduardo [UNESP]2018-12-11T16:56:10Z2018-12-11T16:56:10Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject272-279ICAART 2014 - Proceedings of the 6th International Conference on Agents and Artificial Intelligence, v. 1, p. 272-279.http://hdl.handle.net/11449/1715982-s2.0-8490230844188588006994253520000-0003-3534-974XScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengICAART 2014 - Proceedings of the 6th International Conference on Agents and Artificial Intelligenceinfo:eu-repo/semantics/openAccess2024-06-28T13:34:36Zoai:repositorio.unesp.br:11449/171598Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:50:38.576283Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Monitoring of grinding burn by AE and vibration signals
title Monitoring of grinding burn by AE and vibration signals
spellingShingle Monitoring of grinding burn by AE and vibration signals
Neto, Rodolpho F. Godoy [UNESP]
Acoustic emission
Burn
Grinding process
Monitoring
Neural network application
title_short Monitoring of grinding burn by AE and vibration signals
title_full Monitoring of grinding burn by AE and vibration signals
title_fullStr Monitoring of grinding burn by AE and vibration signals
title_full_unstemmed Monitoring of grinding burn by AE and vibration signals
title_sort Monitoring of grinding burn by AE and vibration signals
author Neto, Rodolpho F. Godoy [UNESP]
author_facet Neto, Rodolpho F. Godoy [UNESP]
Marchi, Marcelo [UNESP]
Martins, Cesar [UNESP]
Aguiar, Paulo R. [UNESP]
Bianchi, Eduardo [UNESP]
author_role author
author2 Marchi, Marcelo [UNESP]
Martins, Cesar [UNESP]
Aguiar, Paulo R. [UNESP]
Bianchi, Eduardo [UNESP]
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Neto, Rodolpho F. Godoy [UNESP]
Marchi, Marcelo [UNESP]
Martins, Cesar [UNESP]
Aguiar, Paulo R. [UNESP]
Bianchi, Eduardo [UNESP]
dc.subject.por.fl_str_mv Acoustic emission
Burn
Grinding process
Monitoring
Neural network application
topic Acoustic emission
Burn
Grinding process
Monitoring
Neural network application
description The grinding process is widely used in surface finishing of steel parts and corresponds to one of the last steps in the manufacturing process. Thus, it's essential to have a reliable monitoring of this process. In grinding of metals, the phenomenon of burn is one of the worst faults to be avoided. Therefore, a monitoring system able to identify this phenomenon would be of great importance for the process. Thus, the aim of this work is the monitoring of burn during the grinding process through an intelligent system that uses acoustic emission (AE) and vibration signals as inputs. Tests were performed on a surface grinding machine, workpiece SAE 1020 and aluminum oxide grinding wheel were used. The acquisition of the vibration signals and AE was done by means of an oscilloscope with a sampling rate of 2MHz. By analyzing the frequency spectra of these signals it was possible to determine the frequency bands that best characterized the phenomenon of burn. These bands were used as inputs to an artificial neural networks capable of classifying the surface condition of the part. The results of this study allowed characterizing the surface of the work piece into three groups: No burn, burn and high surface roughness. The selected neural model has produced good results for classifying the three patterns studied.
publishDate 2014
dc.date.none.fl_str_mv 2014-01-01
2018-12-11T16:56:10Z
2018-12-11T16:56:10Z
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 ICAART 2014 - Proceedings of the 6th International Conference on Agents and Artificial Intelligence, v. 1, p. 272-279.
http://hdl.handle.net/11449/171598
2-s2.0-84902308441
8858800699425352
0000-0003-3534-974X
identifier_str_mv ICAART 2014 - Proceedings of the 6th International Conference on Agents and Artificial Intelligence, v. 1, p. 272-279.
2-s2.0-84902308441
8858800699425352
0000-0003-3534-974X
url http://hdl.handle.net/11449/171598
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv ICAART 2014 - Proceedings of the 6th International Conference on Agents and Artificial Intelligence
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 272-279
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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