Flank wear prediction in end milling using regression analysis and radial basis function neural networks (CROSBI ID 567235)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Bajić, Dražen ; Jozić, Sonja ; Celent Luka
engleski
Flank wear prediction in end milling using regression analysis and radial basis function neural networks
End milling is commonly used machining process for the manufacturing of dies and molds, as well as numerous very high precision machine components. Flank wear develops due to abrasion of the cutting tool edge against the machined workpiece surface and is measured by the average width of wear land on the primary clearance face. This study presents the prediction of flank wear in end milling process. Machining parameters (cutting speed, vc, feed per tooth, ft, radial depth of cut, ae) and machining time, t, have been used as input variables. Since the flank wear has an influence on surface quality, the surface roughness has also been observed in this study. Regression analysis and radial basis function neural networks have been applied to data experimentally determined by means of the design of experiment and the effective mathematical models have been developed. The results obtained by the models have been compared. Both models have the relative prediction error below 7.66 %. The best prediction of flank wear and surface roughness shows radial basis function neural network model with the average relative prediction error of 4.48 % and 4.47 %, respectively.
flank wear; end milling; regression analysis; radial basis function neural network
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Podaci o prilogu
250-254.
2010.
objavljeno
Podaci o matičnoj publikaciji
IN-TECH 2010
Jan Kudlaček, Branimir Barišić, Xavier Velay, Kazuhiro Ohkura
Prag: Tisk AS s.r.o., Jaromer
978-80-904502-2-6
Podaci o skupu
International Conference on Innovative Technologies
predavanje
14.09.2010-16.09.2010
Prag, Češka Republika