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://hdl.handle.net/11449/226053 |
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. © 2010 by ABCM. |
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Repositório Institucional da UNESP |
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Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networksArtificial neural networksBurn detectionGrindingHardnessSurface roughnessIndustry 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. © 2010 by ABCM.Department of Electrical Engineering UNESP - Univ. Estadual Paulista, Bauru, SPGrad. Prog. in Materials Science and Tech. UNESP - Univ. Estadual Paulista, Bauru, SPDepartment of Electrical Engineering UNESP - Univ. Estadual Paulista, Bauru, SPGrad. Prog. in Materials Science and Tech. UNESP - Univ. Estadual Paulista, Bauru, SPUniversidade Estadual Paulista (UNESP)De Aguiar, Paulo Roberto [UNESP]De Paula, Wallace C.F. [UNESP]Bianchi, Eduardo Carlos [UNESP]Ulson, José Alfredo Covolan [UNESP]Cruz, Carlos E. Dorigatti [UNESP]2022-04-28T21:25:06Z2022-04-28T21:25:06Z2010-04-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article146-153Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 32, n. 2, p. 146-153, 2010.1678-58781806-3691http://hdl.handle.net/11449/2260532-s2.0-77957163503Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of the Brazilian Society of Mechanical Sciences and Engineeringinfo:eu-repo/semantics/openAccess2022-04-28T21:25:06Zoai:repositorio.unesp.br:11449/226053Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-28T21:25:06Repositório 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] Artificial neural networks Burn detection Grinding Hardness Surface roughness |
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] Ulson, José Alfredo Covolan [UNESP] Cruz, Carlos E. Dorigatti [UNESP] |
author_role |
author |
author2 |
De Paula, Wallace C.F. [UNESP] Bianchi, Eduardo Carlos [UNESP] Ulson, José Alfredo Covolan [UNESP] Cruz, Carlos E. Dorigatti [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] Ulson, José Alfredo Covolan [UNESP] Cruz, Carlos E. Dorigatti [UNESP] |
dc.subject.por.fl_str_mv |
Artificial neural networks Burn detection Grinding Hardness Surface roughness |
topic |
Artificial neural networks Burn detection Grinding Hardness Surface roughness |
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. © 2010 by ABCM. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-04-01 2022-04-28T21:25:06Z 2022-04-28T21:25:06Z |
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 |
Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 32, n. 2, p. 146-153, 2010. 1678-5878 1806-3691 http://hdl.handle.net/11449/226053 2-s2.0-77957163503 |
identifier_str_mv |
Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 32, n. 2, p. 146-153, 2010. 1678-5878 1806-3691 2-s2.0-77957163503 |
url |
http://hdl.handle.net/11449/226053 |
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 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
146-153 |
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_ |
1799964956143124480 |