Neural Networks Tool Condition Monitoring in Single-point Dressing Operations

Detalhes bibliográficos
Autor(a) principal: D'Addona, Doriana M.
Data de Publicação: 2016
Outros Autores: Matarazzo, Davide, De Aguiar, Paulo R. [UNESP], Bianchi, Eduardo C. [UNESP], Martins, Cesar H.R. [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.procir.2016.01.001
http://hdl.handle.net/11449/178025
Resumo: Cognitive modeling of tool wear progress is employed to obtain a dependable trend of tool wear curves for optimal utilization of tool life and productivity improvement, while preserving the surface integrity of the ground parts. This paper describes a method to characterize the dresser wear condition utilizing vibration signals by applying a cognitive paradigm, such as Artificial Neural Networks (ANNs). Dressing tests with a single-point dresser were performed in a surface grinding machine and tool wear measurements taken along the experiments. The results show that ANN processing offers an effective method for the monitoring of grinding wheel wear based on vibration signal analysis.
id UNSP_532ddea7fda21766ab9b6b11b51e28e8
oai_identifier_str oai:repositorio.unesp.br:11449/178025
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling Neural Networks Tool Condition Monitoring in Single-point Dressing OperationsArtificial neural networksDressingTool wearVibration signalCognitive modeling of tool wear progress is employed to obtain a dependable trend of tool wear curves for optimal utilization of tool life and productivity improvement, while preserving the surface integrity of the ground parts. This paper describes a method to characterize the dresser wear condition utilizing vibration signals by applying a cognitive paradigm, such as Artificial Neural Networks (ANNs). Dressing tests with a single-point dresser were performed in a surface grinding machine and tool wear measurements taken along the experiments. The results show that ANN processing offers an effective method for the monitoring of grinding wheel wear based on vibration signal analysis.Fraunhofer Joint Laboratory of Excellence on Advanced Production Technology (Fh-J-LEAPT Naples) Department of Chemical Material and Industrial Production Engineering University of Naples Federico II, Piazzale Tecchio 80University Estadual Paulista-UNESP Faculty of Engineering Department of Electrical EngineeringUniversity Estadual Paulista-UNESP Faculty of Engineering Department of Electrical EngineeringUniversity of Naples Federico IIUniversidade Estadual Paulista (Unesp)D'Addona, Doriana M.Matarazzo, DavideDe Aguiar, Paulo R. [UNESP]Bianchi, Eduardo C. [UNESP]Martins, Cesar H.R. [UNESP]2018-12-11T17:28:16Z2018-12-11T17:28:16Z2016-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject431-436http://dx.doi.org/10.1016/j.procir.2016.01.001Procedia CIRP, v. 41, p. 431-436.2212-8271http://hdl.handle.net/11449/17802510.1016/j.procir.2016.01.0012-s2.0-84968779473Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProcedia CIRP0,668info:eu-repo/semantics/openAccess2024-06-28T13:55:18Zoai:repositorio.unesp.br:11449/178025Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:41:28.185531Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Neural Networks Tool Condition Monitoring in Single-point Dressing Operations
title Neural Networks Tool Condition Monitoring in Single-point Dressing Operations
spellingShingle Neural Networks Tool Condition Monitoring in Single-point Dressing Operations
D'Addona, Doriana M.
Artificial neural networks
Dressing
Tool wear
Vibration signal
title_short Neural Networks Tool Condition Monitoring in Single-point Dressing Operations
title_full Neural Networks Tool Condition Monitoring in Single-point Dressing Operations
title_fullStr Neural Networks Tool Condition Monitoring in Single-point Dressing Operations
title_full_unstemmed Neural Networks Tool Condition Monitoring in Single-point Dressing Operations
title_sort Neural Networks Tool Condition Monitoring in Single-point Dressing Operations
author D'Addona, Doriana M.
author_facet D'Addona, Doriana M.
Matarazzo, Davide
De Aguiar, Paulo R. [UNESP]
Bianchi, Eduardo C. [UNESP]
Martins, Cesar H.R. [UNESP]
author_role author
author2 Matarazzo, Davide
De Aguiar, Paulo R. [UNESP]
Bianchi, Eduardo C. [UNESP]
Martins, Cesar H.R. [UNESP]
author2_role author
author
author
author
dc.contributor.none.fl_str_mv University of Naples Federico II
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv D'Addona, Doriana M.
Matarazzo, Davide
De Aguiar, Paulo R. [UNESP]
Bianchi, Eduardo C. [UNESP]
Martins, Cesar H.R. [UNESP]
dc.subject.por.fl_str_mv Artificial neural networks
Dressing
Tool wear
Vibration signal
topic Artificial neural networks
Dressing
Tool wear
Vibration signal
description Cognitive modeling of tool wear progress is employed to obtain a dependable trend of tool wear curves for optimal utilization of tool life and productivity improvement, while preserving the surface integrity of the ground parts. This paper describes a method to characterize the dresser wear condition utilizing vibration signals by applying a cognitive paradigm, such as Artificial Neural Networks (ANNs). Dressing tests with a single-point dresser were performed in a surface grinding machine and tool wear measurements taken along the experiments. The results show that ANN processing offers an effective method for the monitoring of grinding wheel wear based on vibration signal analysis.
publishDate 2016
dc.date.none.fl_str_mv 2016-01-01
2018-12-11T17:28:16Z
2018-12-11T17:28:16Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.procir.2016.01.001
Procedia CIRP, v. 41, p. 431-436.
2212-8271
http://hdl.handle.net/11449/178025
10.1016/j.procir.2016.01.001
2-s2.0-84968779473
url http://dx.doi.org/10.1016/j.procir.2016.01.001
http://hdl.handle.net/11449/178025
identifier_str_mv Procedia CIRP, v. 41, p. 431-436.
2212-8271
10.1016/j.procir.2016.01.001
2-s2.0-84968779473
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Procedia CIRP
0,668
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 431-436
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_ 1808128845656621056

Registros relacionados