Inferential measurement of the dresser width for the grinding process automation

dc.contributor.authorFerreira, Fabio Isaac [UNESP]
dc.contributor.authorde Aguiar, Paulo Roberto [UNESP]
dc.contributor.authorLopes, Wenderson Nascimento [UNESP]
dc.contributor.authorMartins, Cesar Henrique Rossinoli
dc.contributor.authorRuzzi, Rodrigo de Souza
dc.contributor.authorBianchi, Eduardo Carlos [UNESP]
dc.contributor.authorD’Addona, Doriana Marilena
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Federal de Uberlândia (UFU)
dc.contributor.institutionNapoli Federico II University (UNINA)
dc.date.accessioned2019-10-06T16:02:21Z
dc.date.available2019-10-06T16:02:21Z
dc.date.issued2019-02-25
dc.description.abstractDressing is an essential process for the machining industries. The grinding community keeps the slogan “grinding is dressing,” given the importance of this reconditioning process. This paper presents a methodology for forecasting the dresser width one step forward by using indirect monitoring. The dresser width is an important parameter to guarantee the quality of the dressing process and, in many cases, it is monitored directly by the operators. Acoustic emission signals were collected during the dressing process and an estimation neural network was used to correlate the dresser width with the processed signals to estimate the current value of the width. The output of the estimation network was input to a time-delay neural network to predict the next value of the dresser width. By utilizing this procedure, an automatic system would be able to readjust the dressing parameters while avoiding the stops, reducing costs, and maintaining repeatability during the process.en
dc.description.affiliationDepartment of Electrical Engineering Faculty of Engineering Bauru (FEB) Universidade Estadual Paulista (UNESP), Av. Eng. Luiz Edmundo C. Coube 14-01
dc.description.affiliationDepartment of Electrical and Computational Engineering São Paulo University (USP)
dc.description.affiliationSchool of Mechanical Engineering Federal University of Uberlândia (UFU)
dc.description.affiliationDepartment of Mechanical Engineering Faculty of Engineering Bauru (FEB) Universidade Estadual Paulista (UNESP)
dc.description.affiliationDepartment of Chemical Materials and Production Engineering Napoli Federico II University (UNINA)
dc.description.affiliationUnespDepartment of Electrical Engineering Faculty of Engineering Bauru (FEB) Universidade Estadual Paulista (UNESP), Av. Eng. Luiz Edmundo C. Coube 14-01
dc.description.affiliationUnespDepartment of Mechanical Engineering Faculty of Engineering Bauru (FEB) Universidade Estadual Paulista (UNESP)
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.format.extent3055-3066
dc.identifierhttp://dx.doi.org/10.1007/s00170-018-2869-x
dc.identifier.citationInternational Journal of Advanced Manufacturing Technology, v. 100, n. 9-12, p. 3055-3066, 2019.
dc.identifier.doi10.1007/s00170-018-2869-x
dc.identifier.issn1433-3015
dc.identifier.issn0268-3768
dc.identifier.lattes1455400309660081
dc.identifier.orcid0000-0002-9934-4465
dc.identifier.scopus2-s2.0-85055537403
dc.identifier.urihttp://hdl.handle.net/11449/188260
dc.language.isoeng
dc.relation.ispartofInternational Journal of Advanced Manufacturing Technology
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectAcoustic emission
dc.subjectArtificial neural networks
dc.subjectDressing operation
dc.subjectInferential measurement
dc.subjectTool wear condition
dc.titleInferential measurement of the dresser width for the grinding process automationen
dc.typeArtigo
unesp.author.lattes1455400309660081[2]
unesp.author.lattes1099152007574921[6]
unesp.author.orcid0000-0002-0130-0975[1]
unesp.author.orcid0000-0002-9934-4465[2]
unesp.author.orcid0000-0003-2675-4276[6]

Arquivos