Regression analysis, support vector machines and Bayesian neural network approaches to modeling surface roughness in face milling (CROSBI ID 145814)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Lela, Branimir ; Bajić, Dražen ; Jozić, Sonja
engleski
Regression analysis, support vector machines and Bayesian neural network approaches to modeling surface roughness in face milling
This study examines the influence of cutting speed, feed and depth of cut on surface roughness in face milling process. Three different modeling methodologies, namely regression analysis (RA), support vector machines (SVM) and Bayesian neural network (BNN), have been applied to data experimentally determined by means of the design of experiment (DOE). The results obtained by the models have been compared. All three models have the relative prediction error below 8 %. The best prediction of surface roughness shows BNN model with the average relative prediction error of 6.1 %. The research has shown that, when the training dataset is small, both BNN and SVR modeling methodologies are comparable with RA methodology and, furthermore, they can even offer better results. Regarding the influence of the examined cutting parameters on the surface roughness, it has been shown that the feed has the largest affect on it and the depth of cut the least.
Face milling; Surface roughness; Regression; Support Vector Machines; Bayesian neural network
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Podaci o izdanju
42 (11-12)
2009.
1082-1088
objavljeno
0268-3768
10.1007/s00170-008-1678-z