Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regression

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dc.contributor.authorAsilturk, Ilhan
dc.date.accessioned2020-03-26T18:31:04Z
dc.date.available2020-03-26T18:31:04Z
dc.date.issued2012
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this study, models for predicting the surface roughness of AISI 1040 steel material using artificial neural networks (ANN) and multiple regression (MRM) are developed. The models are optimized using cutting parameters as input and corresponding surface roughness values as output. Cutting parameters considered in this study include cutting speed, feed rate, depth of cut, and nose radius. Surface roughness is characterized by the mean (R (a)) and total (R (t)) of the recorded roughness values at different locations on the surface. A total of 81 different experiments were performed, each with a different setting of the cutting parameters, and the corresponding R (a) and R (t) values for each case are measured. Input-output pairs obtained through these 81 experiments are used to train an ANN is achieved at the 200,00th epoch. Mean squared error of 0.002917120% achieved using the developed ANN outperforms error rates reported in earlier studies and can also be considered admissible for real-time deployment of the developed ANN algorithm for robust prediction of the surface roughness in industrial settings.en_US
dc.identifier.doi10.1007/s00170-012-3903-zen_US
dc.identifier.endpage257en_US
dc.identifier.issn0268-3768en_US
dc.identifier.issue01.04.2020en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage249en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00170-012-3903-z
dc.identifier.urihttps://hdl.handle.net/20.500.12395/28319
dc.identifier.volume63en_US
dc.identifier.wosWOS:000310167400025en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER LONDON LTDen_US
dc.relation.ispartofINTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGYen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectIntelligent controlen_US
dc.subjectNeural networken_US
dc.subjectCNC turningen_US
dc.subjectSurface roughnessen_US
dc.subjectRegression modelen_US
dc.titlePredicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regressionen_US
dc.typeArticleen_US

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