Monitoring of grinding burn by AE and vibration signals
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , |
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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Repositório Institucional da UNESP |
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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 |
|
_version_ |
1808128989318873088 |