Predicting surface roughness of AISI 4140 steel in hard turning process through artificial neural network, fuzzy logic and regression models

dc.contributor.authorAkkuş, Harun
dc.contributor.authorAsiltürk, İlhan
dc.date.accessioned2020-03-26T18:22:18Z
dc.date.available2020-03-26T18:22:18Z
dc.date.issued2011
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this study, the average surface roughness values obtained when turning AISI 4140 grade tempered steel with a hardness of 51 HRC, were modeled using fuzzy logic, artificial neural networks (ANN) and multi-regression equations. Input variables consisted of cutting speed (V), feed rate (f) and depth of cut (a) while output variable was surface roughness (Ra). Fuzzy logic and ANN models were developed using Matlab Toolbox. Variance analysis was conducted using MINITAB. The predicted values of mean squared errors (MSE) were employed to compare the three models. Results showed that the optimum predictive model is the fuzzy logic model. With small errors (e.g MSE = 0.0173166), the model was considered sufficiently accurate. © 2011 Academic Journals.en_US
dc.identifier.endpage2736en_US
dc.identifier.issn1992-2248en_US
dc.identifier.issue13en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage2729en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/27272
dc.identifier.volume6en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofScientific Research and Essaysen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial neural networken_US
dc.subjectFuzzy logic modelen_US
dc.subjectHard turningen_US
dc.subjectMulti regression modelen_US
dc.subjectSurface roughnessen_US
dc.titlePredicting surface roughness of AISI 4140 steel in hard turning process through artificial neural network, fuzzy logic and regression modelsen_US
dc.typeArticleen_US

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