Naslov
Regression analysis, support vector machines and Bayesian neural network approaches to modeling surface roughness in face milling

Autori
Lela, Branimir ; Bajić, Dražen ; Jozić, Sonja

Izvornik
International Journal of Advanced Manufacturing Technology (0268-3768) 42 (2009), 11-12; 1082-1088

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Face milling; Surface roughness; Regression; Support Vector Machines; Bayesian neural network

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo

POVEZANOST RADA