Artificial Intelligence-Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy

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dc.authoridKARA, Fuat/0000-0002-3811-3081
dc.contributor.authorEser, Aykut
dc.contributor.authorAyyildiz, Elmas Askar
dc.contributor.authorAyyildiz, Mustafa
dc.contributor.authorKara, Fuat
dc.date.accessioned2021-12-01T18:47:04Z
dc.date.available2021-12-01T18:47:04Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractThis study introduces the improvement of mathematical and predictive models of surface roughness parameter (Ra) in milling AA6061 alloy using carbide cutting tools coated with CVD-TiCN in dry condition. An experimental model has been improved for estimating the surface roughness using artificial neural networks (ANN) and response surface methodology (RSM). For these models, cutting speed, depth of cut, and feed rate were evaluated as input parameters for experimental design. For the ANN modelling, the standard backpropagation algorithm was established to be the optimum selection for training the model. In the forming of the network construction, five different learning algorithms were used: the conjugate gradient backpropagation, Levenberg-Marquardt, scaled conjugate gradient, quasi-Newton backpropagation, and resilient backpropagation. The best consequent with single hidden layers for the surface roughness was obtained by 3-8-1 network structures. The statistical analysis was performed with RSM-based second-order mathematics model. The influences of the cutting parameters on surface roughness were defined by using analysis of variance (ANOVA). The ANOVA results show that the depth of cut is the most effective parameter on surface roughness. Prediction models developed using ANN and RSM were compared in terms of prediction accuracy R2, MEP, and RMSE. The data estimated from ANN and RSM were realized to be very close to the data acquired from experimental studies. The value R-2 of RSM model was higher than the values of the ANN model which demonstrated the stability and sturdiness of the RSM method.en_US
dc.identifier.doi10.1155/2021/5576600
dc.identifier.issn1687-8434
dc.identifier.issn1687-8442
dc.identifier.scopus2-s2.0-85101526619en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1155/2021/5576600
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10126
dc.identifier.volume2021en_US
dc.identifier.wosWOS:000621837500001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofAdvances In Materials Science And Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleArtificial Intelligence-Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloyen_US
dc.typeArticleen_US

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