Inferential measurement of the dresser width for the grinding process automation

Detalhes bibliográficos
Autor(a) principal: Ferreira, Fabio Isaac [UNESP]
Data de Publicação: 2019
Outros Autores: de Aguiar, Paulo Roberto [UNESP], Lopes, Wenderson Nascimento [UNESP], Martins, Cesar Henrique Rossinoli, Ruzzi, Rodrigo de Souza, Bianchi, Eduardo Carlos [UNESP], D’Addona, Doriana Marilena
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/s00170-018-2869-x
http://hdl.handle.net/11449/188260
Resumo: Dressing 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.
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spelling Inferential measurement of the dresser width for the grinding process automationAcoustic emissionArtificial neural networksDressing operationInferential measurementTool wear conditionDressing 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.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Department of Electrical Engineering Faculty of Engineering Bauru (FEB) Universidade Estadual Paulista (UNESP), Av. Eng. Luiz Edmundo C. Coube 14-01Department of Electrical and Computational Engineering São Paulo University (USP)School of Mechanical Engineering Federal University of Uberlândia (UFU)Department of Mechanical Engineering Faculty of Engineering Bauru (FEB) Universidade Estadual Paulista (UNESP)Department of Chemical Materials and Production Engineering Napoli Federico II University (UNINA)Department of Electrical Engineering Faculty of Engineering Bauru (FEB) Universidade Estadual Paulista (UNESP), Av. Eng. Luiz Edmundo C. Coube 14-01Department of Mechanical Engineering Faculty of Engineering Bauru (FEB) Universidade Estadual Paulista (UNESP)Universidade Estadual Paulista (Unesp)Universidade de São Paulo (USP)Universidade Federal de Uberlândia (UFU)Napoli Federico II University (UNINA)Ferreira, Fabio Isaac [UNESP]de Aguiar, Paulo Roberto [UNESP]Lopes, Wenderson Nascimento [UNESP]Martins, Cesar Henrique RossinoliRuzzi, Rodrigo de SouzaBianchi, Eduardo Carlos [UNESP]D’Addona, Doriana Marilena2019-10-06T16:02:21Z2019-10-06T16:02:21Z2019-02-25info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article3055-3066http://dx.doi.org/10.1007/s00170-018-2869-xInternational Journal of Advanced Manufacturing Technology, v. 100, n. 9-12, p. 3055-3066, 2019.1433-30150268-3768http://hdl.handle.net/11449/18826010.1007/s00170-018-2869-x2-s2.0-8505553740314554003096600810000-0002-9934-4465Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Journal of Advanced Manufacturing Technologyinfo:eu-repo/semantics/openAccess2024-06-28T13:54:35Zoai:repositorio.unesp.br:11449/188260Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:06:01.854087Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Inferential measurement of the dresser width for the grinding process automation
title Inferential measurement of the dresser width for the grinding process automation
spellingShingle Inferential measurement of the dresser width for the grinding process automation
Ferreira, Fabio Isaac [UNESP]
Acoustic emission
Artificial neural networks
Dressing operation
Inferential measurement
Tool wear condition
title_short Inferential measurement of the dresser width for the grinding process automation
title_full Inferential measurement of the dresser width for the grinding process automation
title_fullStr Inferential measurement of the dresser width for the grinding process automation
title_full_unstemmed Inferential measurement of the dresser width for the grinding process automation
title_sort Inferential measurement of the dresser width for the grinding process automation
author Ferreira, Fabio Isaac [UNESP]
author_facet Ferreira, Fabio Isaac [UNESP]
de Aguiar, Paulo Roberto [UNESP]
Lopes, Wenderson Nascimento [UNESP]
Martins, Cesar Henrique Rossinoli
Ruzzi, Rodrigo de Souza
Bianchi, Eduardo Carlos [UNESP]
D’Addona, Doriana Marilena
author_role author
author2 de Aguiar, Paulo Roberto [UNESP]
Lopes, Wenderson Nascimento [UNESP]
Martins, Cesar Henrique Rossinoli
Ruzzi, Rodrigo de Souza
Bianchi, Eduardo Carlos [UNESP]
D’Addona, Doriana Marilena
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade de São Paulo (USP)
Universidade Federal de Uberlândia (UFU)
Napoli Federico II University (UNINA)
dc.contributor.author.fl_str_mv Ferreira, Fabio Isaac [UNESP]
de Aguiar, Paulo Roberto [UNESP]
Lopes, Wenderson Nascimento [UNESP]
Martins, Cesar Henrique Rossinoli
Ruzzi, Rodrigo de Souza
Bianchi, Eduardo Carlos [UNESP]
D’Addona, Doriana Marilena
dc.subject.por.fl_str_mv Acoustic emission
Artificial neural networks
Dressing operation
Inferential measurement
Tool wear condition
topic Acoustic emission
Artificial neural networks
Dressing operation
Inferential measurement
Tool wear condition
description Dressing 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.
publishDate 2019
dc.date.none.fl_str_mv 2019-10-06T16:02:21Z
2019-10-06T16:02:21Z
2019-02-25
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/10.1007/s00170-018-2869-x
International Journal of Advanced Manufacturing Technology, v. 100, n. 9-12, p. 3055-3066, 2019.
1433-3015
0268-3768
http://hdl.handle.net/11449/188260
10.1007/s00170-018-2869-x
2-s2.0-85055537403
1455400309660081
0000-0002-9934-4465
url http://dx.doi.org/10.1007/s00170-018-2869-x
http://hdl.handle.net/11449/188260
identifier_str_mv International Journal of Advanced Manufacturing Technology, v. 100, n. 9-12, p. 3055-3066, 2019.
1433-3015
0268-3768
10.1007/s00170-018-2869-x
2-s2.0-85055537403
1455400309660081
0000-0002-9934-4465
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv International Journal of Advanced Manufacturing Technology
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 3055-3066
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
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