Digital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single-point dressing operation

Detalhes bibliográficos
Autor(a) principal: Lopes, Wenderson Nascimento [UNESP]
Data de Publicação: 2017
Outros Autores: 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]
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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spelling 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
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