Inferential measurement of the dresser width for the grinding process automation
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , |
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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Repositório Institucional da UNESP |
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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 |
|
_version_ |
1808128461447888896 |