Application of Artificial Intelligent to Predict Surface Roughness

Basit Öğe Kaydı
dc.contributor.authorAsilturk, I.
dc.date.accessioned2020-03-26T18:49:26Z
dc.date.available2020-03-26T18:49:26Z
dc.date.issued2014
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis article proposes for predicting the surface roughness of AISI 1040 steel material using the artificial intelligent. Cutting speed, feed rate, depth of cut, and nose radius have been taken into consideration as input factors and corresponding surface roughness values (R-a, R-t) as output. A series of experiments have been carried out in accordance with a full factorial design on the CNC lathe to obtain the data used for the training and testing of an artificial neural network (ANN). The developed MATLAB TM interface was used to predict surface roughness. Multilayer perceptron structure, which is a kind of feed forward ANNs, is applied to model and prediction of the surface roughness in turning operations. The number of iterations used was 20,000. MSE = 1E-4 and R-2 = 0.9991 were achieved using the developed ANN Model. The obtained results indicate that the ANN algorithm coupled with back propagation neural network is an efficient and accurate method in predicting of surface roughness in turning.en_US
dc.description.sponsorshipScientific Research Projects Coordinators (BAP) of Selcuk UniversitySelcuk University; ISOMER; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)en_US
dc.description.sponsorshipThis study is supported by Scientific Research Projects Coordinators (BAP) of Selcuk University, ISOMER and, TUBITAK.en_US
dc.identifier.doi10.1111/j.1747-1567.2012.00827.xen_US
dc.identifier.endpage60en_US
dc.identifier.issn0732-8818en_US
dc.identifier.issn1747-1567en_US
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage54en_US
dc.identifier.urihttps://dx.doi.org/10.1111/j.1747-1567.2012.00827.x
dc.identifier.urihttps://hdl.handle.net/20.500.12395/30608
dc.identifier.volume38en_US
dc.identifier.wosWOS:000339551200008en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWILEY-BLACKWELLen_US
dc.relation.ispartofEXPERIMENTAL TECHNIQUESen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectNeural Networken_US
dc.subjectCNC Turningen_US
dc.subjectSurface Roughnessen_US
dc.subjectPrediction Modelen_US
dc.titleApplication of Artificial Intelligent to Predict Surface Roughnessen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu