Optimization and prediction of surface roughness in the milling process of aluminum alloy using Taguchi method and artificial neural network

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dc.contributor.authorAyyildiz, Mustafa
dc.date.accessioned2025-10-11T20:48:16Z
dc.date.available2025-10-11T20:48:16Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractPurpose - This paper explores the machining of aluminum alloy, focusing on optimizing and predicting surface roughness through advanced methods. The study investigates the optimization and prediction of surface roughness in milling aluminum alloy using cryogenically treated and non-cryogenically treated cutting tools. Design/methodology/approach - A Taguchi L18 orthogonal array was implemented to determine the effects of cutting tool type, cutting speed, feed rate and depth of cut on surface roughness. Additionally, an artificial neural network model was developed to predict surface roughness. The back-propagation algorithm was used for training, and various architectures and learning algorithms, including Levenberg-Marquardt (LM), scaled conjugate gradient (SCG), quasi-Newton back propagation (BFGS), resilient back propagation (RP) and conjugate gradient back propagation (CGP), were evaluated. Findings - Optimization results indicated that feed rate was the most significant factor affecting surface quality, contributing 36.41% according to analysis of variance. In the ANN the best predictive performance was achieved using the BFGS algorithm with a 4-13-1 network structure, yielding correlation coefficients (R2) values above 0.97 and low root mean square error (RMSE) and mean error percentage (MEP) values for both training and testing datasets. Originality/value - The study concludes that the Taguchi method effectively optimized machining parameters, and the artificial neural network model demonstrated strong predictive accuracy, confirming its suitability for estimating surface roughness in milling processes.en_US
dc.identifier.doi10.1108/MMMS-02-2025-0059
dc.identifier.issn1573-6105
dc.identifier.issn1573-6113
dc.identifier.scopus2-s2.0-105004349271en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1108/MMMS-02-2025-0059
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21839
dc.identifier.wosWOS:001483669800001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAyyildiz, Mustafa
dc.language.isoenen_US
dc.publisherEmerald Group Publishing Ltden_US
dc.relation.ispartofMultidiscipline Modeling in Materialsand Structuresen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectOptimizationen_US
dc.subjectPredictionen_US
dc.subjectMachinabilityen_US
dc.subjectSurface roughnessen_US
dc.titleOptimization and prediction of surface roughness in the milling process of aluminum alloy using Taguchi method and artificial neural networken_US
dc.typeArticleen_US

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