Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization 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.authorAly, Mohamed F.
dc.contributor.authorAlharbi, Hamad F.
dc.date.accessioned2024-04-24T17:12:17Z
dc.date.available2024-04-24T17:12:17Z
dc.date.issued2018
dc.departmentDicle Üniversitesien_US
dc.description.abstractAll manufacturing engineers are faced with a lot of difficulties and high expenses associated with grinding processes of AZ61. For that reason, manufacturing engineers waste a lot of time and effort trying to reach the required surface roughness values according to the design drawing during the turning process. In this paper, an artificial neural network (ANN) modeling is used to estimate and optimize the surface roughness (R-a) value in cutting conditions of AZ61 magnesium alloy. A number of ANN models were developed and evaluated to obtain the most successful one. In addition to ANN models, traditional regression analysis was also used to build a mathematical model representing the equation required to obtain the surface roughness. Predictions from the model were examined against experimental data and then compared to the ANN model predictions using different performance criteria such as the mean absolute error, mean square error, and coeffcient of determination.en_US
dc.description.sponsorshipKing Saud University, Deanship of Scientific Research, 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.doi10.1155/2018/1825291
dc.identifier.issn1687-8434
dc.identifier.issn1687-8442
dc.identifier.scopus2-s2.0-85056241624
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1155/2018/1825291
dc.identifier.urihttps://hdl.handle.net/11468/17918
dc.identifier.volume2018en_US
dc.identifier.wosWOS:000426191600001
dc.identifier.wosqualityQ4
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.titlePrediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Networken_US
dc.titlePrediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network
dc.typeArticleen_US

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