Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networks
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1590/S1678-58782010000200007 http://hdl.handle.net/11449/8921 |
Resumo: | Industry worldwide has been marked by intense competition in recent years, placing companies under ever increasing pressure to improve the efficiency of their product processes. In addition to efficiency, precision is an extremely important factor, allowing companies to maintain standards and procedures aligned with international standards. One of the finishing processes most widely utilized for the manufacturing of mechanical precision components is grinding, and one of the principal criteria for evaluating the final quality of a product is its surface, which is influenced mainly by thermal and mechanical factors. Thus, the objective of this work was to investigate the intrinsic relationship between the surface quality of ground workpieces and the behavior of the corresponding acoustic emission and grinding power signals in the surface grinding processes, using artificial neural networks. The surface quality of workpieces was analyzed based on parameters of surface grinding burn, surface roughness and microhardness. The use of artifice-al neural networks in the characterization of the surface quality ground workpieces was found to yield good results, constituting an interesting proposal for the implementation of intelligent systems in industrial environments. |
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Repositório Institucional da UNESP |
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Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networksgrindingburn detectionsurface roughnesshardnessartificial neural networksIndustry worldwide has been marked by intense competition in recent years, placing companies under ever increasing pressure to improve the efficiency of their product processes. In addition to efficiency, precision is an extremely important factor, allowing companies to maintain standards and procedures aligned with international standards. One of the finishing processes most widely utilized for the manufacturing of mechanical precision components is grinding, and one of the principal criteria for evaluating the final quality of a product is its surface, which is influenced mainly by thermal and mechanical factors. Thus, the objective of this work was to investigate the intrinsic relationship between the surface quality of ground workpieces and the behavior of the corresponding acoustic emission and grinding power signals in the surface grinding processes, using artificial neural networks. The surface quality of workpieces was analyzed based on parameters of surface grinding burn, surface roughness and microhardness. The use of artifice-al neural networks in the characterization of the surface quality ground workpieces was found to yield good results, constituting an interesting proposal for the implementation of intelligent systems in industrial environments.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)IFM - The Institute Factory of MillenniumUniv Estadual Paulista, UNESP, Dept Elect Engn, Bauru, SP, BrazilUniv Estadual Paulista, UNESP, Grad Prog Mat Sci & Tech, Bauru, SP, BrazilUniv Estadual Paulista, UNESP, Dept Mech Engn, Bauru, SP, BrazilUniv Estadual Paulista, UNESP, Dept Elect Engn, Bauru, SP, BrazilUniv Estadual Paulista, UNESP, Grad Prog Mat Sci & Tech, Bauru, SP, BrazilUniv Estadual Paulista, UNESP, Dept Mech Engn, Bauru, SP, BrazilAbcm Brazilian Soc Mechanical Sciences & EngineeringUniversidade Estadual Paulista (Unesp)de Aguiar, Paulo Roberto [UNESP]de Paula, Wallace C. F. [UNESP]Bianchi, Eduardo Carlos [UNESP]Covolan Ulson, Jose Alfredo [UNESP]Dorigatti Cruz, Carlos E. [UNESP]2014-05-20T13:27:16Z2014-05-20T13:27:16Z2010-04-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article146-153application/pdfhttp://dx.doi.org/10.1590/S1678-58782010000200007Journal of The Brazilian Society of Mechanical Sciences and Engineering. Rio de Janeiro Rj: Abcm Brazilian Soc Mechanical Sciences & Engineering, v. 32, n. 2, p. 146-153, 2010.1678-5878http://hdl.handle.net/11449/8921S1678-58782010000200007WOS:000284077800006S1678-58782010000200007-en.pdf1455400309660081109915200757492145170571214622580000-0002-9934-4465Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of the Brazilian Society of Mechanical Sciences and Engineering1.6270,362info:eu-repo/semantics/openAccess2024-01-19T06:29:29Zoai:repositorio.unesp.br:11449/8921Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-01-19T06:29:29Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networks |
title |
Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networks |
spellingShingle |
Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networks de Aguiar, Paulo Roberto [UNESP] grinding burn detection surface roughness hardness artificial neural networks |
title_short |
Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networks |
title_full |
Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networks |
title_fullStr |
Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networks |
title_full_unstemmed |
Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networks |
title_sort |
Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networks |
author |
de Aguiar, Paulo Roberto [UNESP] |
author_facet |
de Aguiar, Paulo Roberto [UNESP] de Paula, Wallace C. F. [UNESP] Bianchi, Eduardo Carlos [UNESP] Covolan Ulson, Jose Alfredo [UNESP] Dorigatti Cruz, Carlos E. [UNESP] |
author_role |
author |
author2 |
de Paula, Wallace C. F. [UNESP] Bianchi, Eduardo Carlos [UNESP] Covolan Ulson, Jose Alfredo [UNESP] Dorigatti Cruz, Carlos E. [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
de Aguiar, Paulo Roberto [UNESP] de Paula, Wallace C. F. [UNESP] Bianchi, Eduardo Carlos [UNESP] Covolan Ulson, Jose Alfredo [UNESP] Dorigatti Cruz, Carlos E. [UNESP] |
dc.subject.por.fl_str_mv |
grinding burn detection surface roughness hardness artificial neural networks |
topic |
grinding burn detection surface roughness hardness artificial neural networks |
description |
Industry worldwide has been marked by intense competition in recent years, placing companies under ever increasing pressure to improve the efficiency of their product processes. In addition to efficiency, precision is an extremely important factor, allowing companies to maintain standards and procedures aligned with international standards. One of the finishing processes most widely utilized for the manufacturing of mechanical precision components is grinding, and one of the principal criteria for evaluating the final quality of a product is its surface, which is influenced mainly by thermal and mechanical factors. Thus, the objective of this work was to investigate the intrinsic relationship between the surface quality of ground workpieces and the behavior of the corresponding acoustic emission and grinding power signals in the surface grinding processes, using artificial neural networks. The surface quality of workpieces was analyzed based on parameters of surface grinding burn, surface roughness and microhardness. The use of artifice-al neural networks in the characterization of the surface quality ground workpieces was found to yield good results, constituting an interesting proposal for the implementation of intelligent systems in industrial environments. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-04-01 2014-05-20T13:27:16Z 2014-05-20T13:27:16Z |
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/10.1590/S1678-58782010000200007 Journal of The Brazilian Society of Mechanical Sciences and Engineering. Rio de Janeiro Rj: Abcm Brazilian Soc Mechanical Sciences & Engineering, v. 32, n. 2, p. 146-153, 2010. 1678-5878 http://hdl.handle.net/11449/8921 S1678-58782010000200007 WOS:000284077800006 S1678-58782010000200007-en.pdf 1455400309660081 1099152007574921 4517057121462258 0000-0002-9934-4465 |
url |
http://dx.doi.org/10.1590/S1678-58782010000200007 http://hdl.handle.net/11449/8921 |
identifier_str_mv |
Journal of The Brazilian Society of Mechanical Sciences and Engineering. Rio de Janeiro Rj: Abcm Brazilian Soc Mechanical Sciences & Engineering, v. 32, n. 2, p. 146-153, 2010. 1678-5878 S1678-58782010000200007 WOS:000284077800006 S1678-58782010000200007-en.pdf 1455400309660081 1099152007574921 4517057121462258 0000-0002-9934-4465 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal of the Brazilian Society of Mechanical Sciences and Engineering 1.627 0,362 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
146-153 application/pdf |
dc.publisher.none.fl_str_mv |
Abcm Brazilian Soc Mechanical Sciences & Engineering |
publisher.none.fl_str_mv |
Abcm Brazilian Soc Mechanical Sciences & Engineering |
dc.source.none.fl_str_mv |
Web of Science 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_ |
1799965663708577792 |