Influence of Support Vector Regression (SVR) on Cryogenic Face Milling

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dc.authoridKARA, Fuat/0000-0002-3811-3081
dc.contributor.authorKarthik, Rao M. C.
dc.contributor.authorMalghan, Rashmi L.
dc.contributor.authorKara, Fuat
dc.contributor.authorShettigar, Arunkumar
dc.contributor.authorRao, Shrikantha S.
dc.contributor.authorHerbert, Mervin A.
dc.date.accessioned2021-12-01T18:50:00Z
dc.date.available2021-12-01T18:50:00Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractThe paper aims to investigate the processing execution of SS316 in manageable machining cooling ways such as dry, wet, and cryogenic (LN2-liquid nitrogen). Furthermore, one parametric approach was utilized to study the influence and carry out the comparative analysis of LN(2)over dry and LN(2)over wet machining conditions. Response surface methodology (RSM) is incorporated to build a relationship model among the considered independent variables (spindle speed: (S, rpm), feed rate (F, mm/min), and depth of cut (doc) (D, mm)) and the dependent variable (surface roughness (Ra)). Since there is the involvement of more than one independent variable, the generation of regression equation is multiple linear regression. Based on the attained coefficient value of the independent variable, the respective impact on surface roughness is identified. The results of comparative analysis of LN(2)over dry and LN(2)over wet machining states revealed that LN2 machining yielded better surface finish with up to 64.9%, 54.9% over dry and wet machining, respectively, indicating the benefits of LN2 for achieving better Ra. The benchmark function of the proposed mode hybrid-bias (BNN-SVR) algorithm showcases the propensity to emerge out of the local minimum and coincide with the optimal target value. The performance of the (BNN-SVR) is a prevalent new ability to fetch the partially trained weights from the BNN model into the SVR model, thus leading to the conversion of static learning capability to dynamic capability. The performances of the adopted prediction approaches are compared through a range of attained error deviation, i.e., (RA: 3.95%-8.43%), (BNN: 2.36%-5.88%), (SVR: 1.04%-3.61%), respectively. Hybrid-bias (BNN-SVR) is the best suitable prediction model as it provides significant evidence by attaining less error in predicting Ra. However, SVR surpasses BNN and RSM approaches because of the convergence factor and narrow margin error.en_US
dc.description.sponsorshipNational Institute of Technology (NITK), Department of Mechanical Engineeringen_US
dc.description.sponsorshipThe authors would like to thank the National Institute of Technology (NITK), Department of Mechanical Engineering, for providing support and facilities.en_US
dc.identifier.doi10.1155/2021/9984369
dc.identifier.issn1687-8434
dc.identifier.issn1687-8442
dc.identifier.scopus2-s2.0-85113279528en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1155/2021/9984369
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10812
dc.identifier.volume2021en_US
dc.identifier.wosWOS:000686730700002en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofAdvances In Materials Science And Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectResponse-Surface Methodologyen_US
dc.subjectMachining Parameters Optimizationen_US
dc.subjectArtificial Neural-Networken_US
dc.subjectCutting Forcesen_US
dc.subjectSustainability Assessmenten_US
dc.subjectTool Wearen_US
dc.subjectRoughnessen_US
dc.subjectPredictionen_US
dc.subjectCompositesen_US
dc.subjectTi-6Al-4Ven_US
dc.titleInfluence of Support Vector Regression (SVR) on Cryogenic Face Millingen_US
dc.typeArticleen_US

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