A DOE based approach for the design of RBF artificial neural networks applied to prediction of surface roughness in AISI 52100 hardened steel turning

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUNIFEI - Universidade Federal de Itajubá-
Autor(es): dc.creatorPontes, Fabrício José-
Autor(es): dc.creatorSilva, Messias Borges-
Autor(es): dc.creatorFerreira, João Roberto-
Autor(es): dc.creatorDe Paiva, Anderson Paulo-
Autor(es): dc.creatorBalestrassi, Pedro Paulo-
Autor(es): dc.creatorSchönhorst, Gustavo Bonnard-
Data de aceite: dc.date.accessioned2025-08-21T22:17:44Z-
Data de disponibilização: dc.date.available2025-08-21T22:17:44Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2010-01-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1590/s1678-58782010000500010-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/226282-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/226282-
Descrição: dc.descriptionThe use of artificial neural networks for prediction in hard turning has received considerable attention in literature. An often quoted drawback of ANNs is the lack of a systematic way for the design of high performance networks. This study presents a DOE based approach for the design of ANNs of Radial Basis Function (RBF) architecture applied to surface roughness prediction in turning of AISI 52100 hardened steel. Experimental factors are the number of radial units on the hidden layer, the algorithm employed to calculate the spread factor of radial units and the algorithm employed to calculate radial function centers. DOE is employed to select levels of factors that benefit network prediction skills. Experiments with data sets of distinct sizes were conducted and network configurations leading to high performance were identified. ANN models obtained proved capable to predict roughness in accurate, precise and affordable way. Results pointed significant factors for network design and revealed that interaction effects between design parameters have significant influence on network performance for the task proposed. The work concludes that the DOE methodology constitutes a better approach to the design of RBF networks for roughness prediction than the most common trial and error approach. Copyright © 2010 by ABCM.-
Descrição: dc.descriptionDepartamento de Mecânica Faculdade de Engenharia de Guaratinguetá UNESP - Universidade Estadual Paulista, 12516-410 Guaratinguetá, São Paulo-
Descrição: dc.descriptionInstituto de Eng. Produção e Gestão UNIFEI - Universidade Federal de Itajubá, Caixa Postal 50, 37500-903 Itajubá, Minas Gerais-
Descrição: dc.descriptionDepartamento de Mecânica Faculdade de Engenharia de Guaratinguetá UNESP - Universidade Estadual Paulista, 12516-410 Guaratinguetá, São Paulo-
Formato: dc.format503-510-
Idioma: dc.languageen-
Relação: dc.relationJournal of the Brazilian Society of Mechanical Sciences and Engineering-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAISI 52100 hardened steel-
Palavras-chave: dc.subjectDesign of experiments-
Palavras-chave: dc.subjectHard turning-
Palavras-chave: dc.subjectRadial basis function neural networks-
Palavras-chave: dc.subjectSurface roughness-
Título: dc.titleA DOE based approach for the design of RBF artificial neural networks applied to prediction of surface roughness in AISI 52100 hardened steel turning-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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