Comparison of ANN and RSM modeling approaches for WEDM process optimization

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dc.authorscopusid26656037500
dc.authorscopusid25924937500
dc.authorscopusid57190344499
dc.contributor.authorSağbaş, Aysun
dc.contributor.authorGürtuna, Filiz
dc.contributor.authorPolat, Ulviye
dc.date.accessioned2022-05-11T14:26:33Z
dc.date.available2022-05-11T14:26:33Z
dc.date.issued2021
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü
dc.description.abstractIn this paper, an effective process optimization approach based on artificial neural networks with a back propagation algorithm and response surface methodology including central composite design is presented for the modeling and prediction of surface roughness in the wire electrical discharge machining process. In the development of predictive models, cutting parameters of pulse duration, open circuit voltage, wire speed and dielectric flushing are considered as model variables. After experiments are carried out, the analysis of variance is implemented to identify the contribution of uncontrollable process parameters effecting surface roughness. Then, a comparative analysis of the proposed approaches is carried out to determine the most efficient one. The performance of the developed artificial neural networks and response surface methodology predictive models is tested for prediction accuracy in terms of the coefficient of determination and root mean square error metrics. The results indicate that an artificial neural networks model provides more accurate prediction than the response surface methodology model.
dc.identifier.doi10.1515/mt-2020-0057
dc.identifier.endpage392
dc.identifier.issn0025-5300
dc.identifier.issn2195-8572
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85117253625
dc.identifier.scopusqualityQ2
dc.identifier.startpage386
dc.identifier.urihttps://doi.org/10.1515/mt-2020-0057
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6478
dc.identifier.volume63
dc.identifier.wosWOS:000645172500013
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorSağbaş, Aysun
dc.institutionauthorGürtuna, Filiz
dc.institutionauthorPolat, Ulviye
dc.language.isoen
dc.publisherWalter De Gruyter Gmbh
dc.relation.ispartofMaterials Testing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectWEDM
dc.subjectANN
dc.subjectRSM
dc.subjectSurface roughness
dc.subjectModeling
dc.subjectArtificial Neural-Networks
dc.subjectSurface-Roughness
dc.subjectCutting Parameters
dc.subjectBox-Behnken
dc.subjectWire
dc.subjectPrediction
dc.subjectPerformance
dc.subjectDesign
dc.subjectAlloy
dc.subjectMmc
dc.titleComparison of ANN and RSM modeling approaches for WEDM process optimization
dc.typeArticle

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