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

Content Provider	Springer Nature Link
Author	Lela, B. Bajić, D. Jozić, S.
Copyright Year	2008
Abstract	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. 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.
Starting Page	1082
Ending Page	1088
Page Count	7
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	42
Issue Number	11-12
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-08-12
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Face milling Surface roughness Regression Support Vector Machines Bayesian neural network Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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