Digital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single-point dressing operation
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1049/iet-smt.2016.0317 http://hdl.handle.net/11449/163043 |
Resumo: | Dressing is an important operation for the grinding process. Its goal is to recondition the wheel tool to re-establish its cutting characteristics, owing to the wear produced after successive passes. Monitoring systems that use acoustic emission (AE) have been studied to correlate the signals with several tool conditions. This study brings a new approach of processing AE signals with the purpose of identifying the correct moment to stop the dressing, which is essential in an automatic control system. From the AE signals collected in dressing tests with aluminium oxide grinding wheel and single-point dresser, spectral analysis was made through power spectral density, selecting frequencies bands that best characterise the process. The statistical parameter counts' was applied to the raw signal unfiltered and filtered in the selected bands in order to identify the tool condition and, in turn, towards a monitoring system implementation. Results showed an expressive relation between tool cutting conditions and processed signals in the selected bands. There was a great disparity of the filtered signals in the selected bands and signals unfiltered, reflecting that the filtered ones were more efficient in terms of process automation. |
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Digital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single-point dressing operationgrindingacoustic emissionsignal processingproduction engineering computingstatistical analysiswheelsgrinding machinescuttingwearcorrelation methodsproduction testingspectral analysisfiltering theorycutting toolsdigital signal processingacoustic emission signal processingpower spectral densitysingle-point dressing operationgrinding processwheel tool reconditioningAE monitoring systemAE signal processingautomatic control systemaluminium oxide grinding wheelsingle-point dressertool cutting conditionprocess automationDressing is an important operation for the grinding process. Its goal is to recondition the wheel tool to re-establish its cutting characteristics, owing to the wear produced after successive passes. Monitoring systems that use acoustic emission (AE) have been studied to correlate the signals with several tool conditions. This study brings a new approach of processing AE signals with the purpose of identifying the correct moment to stop the dressing, which is essential in an automatic control system. From the AE signals collected in dressing tests with aluminium oxide grinding wheel and single-point dresser, spectral analysis was made through power spectral density, selecting frequencies bands that best characterise the process. The statistical parameter counts' was applied to the raw signal unfiltered and filtered in the selected bands in order to identify the tool condition and, in turn, towards a monitoring system implementation. Results showed an expressive relation between tool cutting conditions and processed signals in the selected bands. There was a great disparity of the filtered signals in the selected bands and signals unfiltered, reflecting that the filtered ones were more efficient in terms of process automation.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Sao Paulo State Univ, Dept Elect Engn, Ave Engn Luiz Edmundo C Coube 14-01, Bauru, BrazilSao Paulo State Univ, Dept Mech Engn, Ave Engn Luiz Edmundo C Coube 14-01, Bauru, BrazilSao Paulo State Univ, Dept Elect Engn, Ave Engn Luiz Edmundo C Coube 14-01, Bauru, BrazilSao Paulo State Univ, Dept Mech Engn, Ave Engn Luiz Edmundo C Coube 14-01, Bauru, BrazilFAPESP: 2016/02831-5CNPq: 306677/2013-0Inst Engineering Technology-ietUniversidade Estadual Paulista (Unesp)Lopes, Wenderson Nascimento [UNESP]Ferreira, Fabio Isaac [UNESP]Alexandre, Felipe Aparecido [UNESP]Santos Ribeiro, Danilo Marcus [UNESP]Conceicao Junior, Pedro de Oliveira [UNESP]Aguiar, Paulo Roberto de [UNESP]Bianchi, Eduardo Carlos [UNESP]2018-11-26T17:39:52Z2018-11-26T17:39:52Z2017-08-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article631-636application/pdfhttp://dx.doi.org/10.1049/iet-smt.2016.0317Iet Science Measurement & Technology. Hertford: Inst Engineering Technology-iet, v. 11, n. 5, p. 631-636, 2017.1751-8822http://hdl.handle.net/11449/16304310.1049/iet-smt.2016.0317WOS:000406147400013WOS000406147400013.pdf14554003096600810000-0002-9934-4465Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIet Science Measurement & Technology0,352info:eu-repo/semantics/openAccess2024-06-28T13:55:00Zoai:repositorio.unesp.br:11449/163043Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:23:58.139407Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Digital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single-point dressing operation |
title |
Digital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single-point dressing operation |
spellingShingle |
Digital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single-point dressing operation Lopes, Wenderson Nascimento [UNESP] grinding acoustic emission signal processing production engineering computing statistical analysis wheels grinding machines cutting wear correlation methods production testing spectral analysis filtering theory cutting tools digital signal processing acoustic emission signal processing power spectral density single-point dressing operation grinding process wheel tool reconditioning AE monitoring system AE signal processing automatic control system aluminium oxide grinding wheel single-point dresser tool cutting condition process automation |
title_short |
Digital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single-point dressing operation |
title_full |
Digital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single-point dressing operation |
title_fullStr |
Digital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single-point dressing operation |
title_full_unstemmed |
Digital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single-point dressing operation |
title_sort |
Digital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single-point dressing operation |
author |
Lopes, Wenderson Nascimento [UNESP] |
author_facet |
Lopes, Wenderson Nascimento [UNESP] Ferreira, Fabio Isaac [UNESP] Alexandre, Felipe Aparecido [UNESP] Santos Ribeiro, Danilo Marcus [UNESP] Conceicao Junior, Pedro de Oliveira [UNESP] Aguiar, Paulo Roberto de [UNESP] Bianchi, Eduardo Carlos [UNESP] |
author_role |
author |
author2 |
Ferreira, Fabio Isaac [UNESP] Alexandre, Felipe Aparecido [UNESP] Santos Ribeiro, Danilo Marcus [UNESP] Conceicao Junior, Pedro de Oliveira [UNESP] Aguiar, Paulo Roberto de [UNESP] Bianchi, Eduardo Carlos [UNESP] |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Lopes, Wenderson Nascimento [UNESP] Ferreira, Fabio Isaac [UNESP] Alexandre, Felipe Aparecido [UNESP] Santos Ribeiro, Danilo Marcus [UNESP] Conceicao Junior, Pedro de Oliveira [UNESP] Aguiar, Paulo Roberto de [UNESP] Bianchi, Eduardo Carlos [UNESP] |
dc.subject.por.fl_str_mv |
grinding acoustic emission signal processing production engineering computing statistical analysis wheels grinding machines cutting wear correlation methods production testing spectral analysis filtering theory cutting tools digital signal processing acoustic emission signal processing power spectral density single-point dressing operation grinding process wheel tool reconditioning AE monitoring system AE signal processing automatic control system aluminium oxide grinding wheel single-point dresser tool cutting condition process automation |
topic |
grinding acoustic emission signal processing production engineering computing statistical analysis wheels grinding machines cutting wear correlation methods production testing spectral analysis filtering theory cutting tools digital signal processing acoustic emission signal processing power spectral density single-point dressing operation grinding process wheel tool reconditioning AE monitoring system AE signal processing automatic control system aluminium oxide grinding wheel single-point dresser tool cutting condition process automation |
description |
Dressing is an important operation for the grinding process. Its goal is to recondition the wheel tool to re-establish its cutting characteristics, owing to the wear produced after successive passes. Monitoring systems that use acoustic emission (AE) have been studied to correlate the signals with several tool conditions. This study brings a new approach of processing AE signals with the purpose of identifying the correct moment to stop the dressing, which is essential in an automatic control system. From the AE signals collected in dressing tests with aluminium oxide grinding wheel and single-point dresser, spectral analysis was made through power spectral density, selecting frequencies bands that best characterise the process. The statistical parameter counts' was applied to the raw signal unfiltered and filtered in the selected bands in order to identify the tool condition and, in turn, towards a monitoring system implementation. Results showed an expressive relation between tool cutting conditions and processed signals in the selected bands. There was a great disparity of the filtered signals in the selected bands and signals unfiltered, reflecting that the filtered ones were more efficient in terms of process automation. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-08-01 2018-11-26T17:39:52Z 2018-11-26T17:39:52Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1049/iet-smt.2016.0317 Iet Science Measurement & Technology. Hertford: Inst Engineering Technology-iet, v. 11, n. 5, p. 631-636, 2017. 1751-8822 http://hdl.handle.net/11449/163043 10.1049/iet-smt.2016.0317 WOS:000406147400013 WOS000406147400013.pdf 1455400309660081 0000-0002-9934-4465 |
url |
http://dx.doi.org/10.1049/iet-smt.2016.0317 http://hdl.handle.net/11449/163043 |
identifier_str_mv |
Iet Science Measurement & Technology. Hertford: Inst Engineering Technology-iet, v. 11, n. 5, p. 631-636, 2017. 1751-8822 10.1049/iet-smt.2016.0317 WOS:000406147400013 WOS000406147400013.pdf 1455400309660081 0000-0002-9934-4465 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Iet Science Measurement & Technology 0,352 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
631-636 application/pdf |
dc.publisher.none.fl_str_mv |
Inst Engineering Technology-iet |
publisher.none.fl_str_mv |
Inst Engineering Technology-iet |
dc.source.none.fl_str_mv |
Web of Science 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_ |
1808129316670668800 |