MULTIPLE REGRESSION AND ANN MODELS FOR SURFACE QUALIFICATION OF CRYOGENICALLY-TREATED AISI 52100 BEARING STEEL

Basit Öğe Kaydı
dc.authoridCicek, Adem/0000-0002-9510-3242
dc.authoridKARA, Fuat/0000-0002-3811-3081
dc.contributor.authorKara, F.
dc.contributor.authorCicek, A.
dc.contributor.authorDemir, H.
dc.date.accessioned2024-09-29T16:11:19Z
dc.date.available2024-09-29T16:11:19Z
dc.date.issued2013
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThis paper focuses on 2 different models, the multiple regression method and the artificial neural network (ANN), for predicting surface roughness (R-a). Experiments were conducted to measure surface roughness in the cylindrical grinding of AISI 52100 bearing steel which had been conventionally heat-treated and deep cryogenically treated (-145 degrees C). In order to compare the effects of holding time at the deep cryogenic temperatures, 5 different holding times (12, 24, 36, 48 and 60 h) were employed to obtain the optimum R-a. The cylindrical grinding test results showed that optimum R-a values were obtained on specimens cryogenically treated for 36 h. In addition, the prediction results showed that the ANN was superior to the multiple regression method in terms of prediction capability. Moreover, due to a higher determination coefficient (R-2) and lower root-mean-square error (RMSE) and mean error percentage (MEP), the ANN was notably successful in predicting the R-a.en_US
dc.description.sponsorshipKarabuk University Scientific Research Project Division [KBU-BAP-11/2-DR-003]en_US
dc.description.sponsorshipThe authors wish to place their sincere thanks to Karabuk University Scientific Research Project Division for financial support for the Project No KBU-BAP-11/2-DR-003.en_US
dc.identifier.endpage584en_US
dc.identifier.issn1310-4772
dc.identifier.issue4en_US
dc.identifier.startpage570en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14619/8323
dc.identifier.volume19en_US
dc.identifier.wosWOS:000330146300008en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherScibulcom Ltden_US
dc.relation.ispartofJournal of the Balkan Tribological Associationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectANNen_US
dc.subjectbearing steelen_US
dc.subjectDCTen_US
dc.subjectsurface qualityen_US
dc.titleMULTIPLE REGRESSION AND ANN MODELS FOR SURFACE QUALIFICATION OF CRYOGENICALLY-TREATED AISI 52100 BEARING STEELen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu