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

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dc.contributor.authorLopes, Wenderson Nascimento [UNESP]
dc.contributor.authorFerreira, Fabio Isaac [UNESP]
dc.contributor.authorAlexandre, Felipe Aparecido [UNESP]
dc.contributor.authorSantos Ribeiro, Danilo Marcus [UNESP]
dc.contributor.authorConceicao Junior, Pedro de Oliveira [UNESP]
dc.contributor.authorAguiar, Paulo Roberto de [UNESP]
dc.contributor.authorBianchi, Eduardo Carlos [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-11-26T17:39:52Z
dc.date.available2018-11-26T17:39:52Z
dc.date.issued2017-08-01
dc.description.abstractDressing 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.en
dc.description.affiliationSao Paulo State Univ, Dept Elect Engn, Ave Engn Luiz Edmundo C Coube 14-01, Bauru, Brazil
dc.description.affiliationSao Paulo State Univ, Dept Mech Engn, Ave Engn Luiz Edmundo C Coube 14-01, Bauru, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Dept Elect Engn, Ave Engn Luiz Edmundo C Coube 14-01, Bauru, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Dept Mech Engn, Ave Engn Luiz Edmundo C Coube 14-01, Bauru, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 2016/02831-5
dc.description.sponsorshipIdCNPq: 306677/2013-0
dc.format.extent631-636
dc.identifierhttp://dx.doi.org/10.1049/iet-smt.2016.0317
dc.identifier.citationIet Science Measurement & Technology. Hertford: Inst Engineering Technology-iet, v. 11, n. 5, p. 631-636, 2017.
dc.identifier.doi10.1049/iet-smt.2016.0317
dc.identifier.fileWOS000406147400013.pdf
dc.identifier.issn1751-8822
dc.identifier.lattes1455400309660081
dc.identifier.orcid0000-0002-9934-4465
dc.identifier.urihttp://hdl.handle.net/11449/163043
dc.identifier.wosWOS:000406147400013
dc.language.isoeng
dc.publisherInst Engineering Technology-iet
dc.relation.ispartofIet Science Measurement & Technology
dc.relation.ispartofsjr0,352
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectgrinding
dc.subjectacoustic emission
dc.subjectsignal processing
dc.subjectproduction engineering computing
dc.subjectstatistical analysis
dc.subjectwheels
dc.subjectgrinding machines
dc.subjectcutting
dc.subjectwear
dc.subjectcorrelation methods
dc.subjectproduction testing
dc.subjectspectral analysis
dc.subjectfiltering theory
dc.subjectcutting tools
dc.subjectdigital signal processing
dc.subjectacoustic emission signal processing
dc.subjectpower spectral density
dc.subjectsingle-point dressing operation
dc.subjectgrinding process
dc.subjectwheel tool reconditioning
dc.subjectAE monitoring system
dc.subjectAE signal processing
dc.subjectautomatic control system
dc.subjectaluminium oxide grinding wheel
dc.subjectsingle-point dresser
dc.subjecttool cutting condition
dc.subjectprocess automation
dc.titleDigital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single-point dressing operationen
dc.typeArtigo
dcterms.rightsHolderInst Engineering Technology-iet
unesp.author.lattes1455400309660081[6]
unesp.author.lattes1099152007574921[7]
unesp.author.orcid0000-0002-8476-3333[5]
unesp.author.orcid0000-0002-9934-4465[6]
unesp.author.orcid0000-0003-2675-4276[7]

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