Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network

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dc.contributor.authorAlharthi, Nabeel H.
dc.contributor.authorBingol, Sedat
dc.contributor.authorAbbas, Adel T.
dc.contributor.authorRagab, Adham E.
dc.contributor.authorEl-Danaf, Ehab A.
dc.contributor.authorAlharbi, Hamad F.
dc.date.accessioned2024-04-24T17:12:17Z
dc.date.available2024-04-24T17:12:17Z
dc.date.issued2017
dc.departmentDicle Üniversitesien_US
dc.description.abstractIn this paper artificial neural network (ANN) and regression analysis were used for the prediction of surface roughness. Five models of neural network were developed and the model that showed best fit with experimental results was with 6 neurons in the hidden layer. Regression analysis was also used to build a mathematical model representing the surface roughness as a function of the process parameters. The coefficient of determination was found to be 94.93% and 93.63%, for the best neural network model and regression analysis, respectively, from the comparison of the models with thirteen validation experimental tests. Optical microscopy was conducted on two machined surfaces with two different values of feed rates while maintaining the spindle speed and depth of cut at the same values. Examining the surface topology and surface roughness profile for the two surfaces revealed that higher feed rate results in relatively thick roughness markings that are distantly spaced, whereas low values of feed rate result in thin surface roughness markings that are closely spaced giving better surface finish.en_US
dc.description.sponsorshipKing Saud University, College of Engineering Research Centeren_US
dc.description.sponsorshipThis project was supported by King Saud University, Deanship of Scientific Research, College of Engineering Research Center.en_US
dc.identifier.doi10.1155/2017/7560468
dc.identifier.issn1687-8434
dc.identifier.issn1687-8442
dc.identifier.scopus2-s2.0-85021641484
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1155/2017/7560468
dc.identifier.urihttps://hdl.handle.net/11468/17917
dc.identifier.volume2017en_US
dc.identifier.wosWOS:000403343500001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofAdvances in Materials Science and Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject[No Keyword]en_US
dc.titleOptimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Networken_US
dc.titleOptimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network
dc.typeArticleen_US

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