INVESTIGATION OF DRILLING PROCESS USING REGRESSION ANALYSIS AND NEURAL NETWORK (CROSBI ID 553334)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Bajić, Dražen ; Jozić, Sonja ; Celent, Luka
engleski
INVESTIGATION OF DRILLING PROCESS USING REGRESSION ANALYSIS AND NEURAL NETWORK
The focus of this paper is to develop a reliable model to predict the depth of drilling, by standard drilling, where the conditions of machinability still stay satisfactory. In this sense, this paper is concerned with the influence of spindle speed and feedrate on increasing thrust force and torque in drilling process. Two modeling methodologies, namely regression analysis and artificial neural network, have been applied to experimentally determined data by means of the design of experiments. The results obtained by the models have been compared. The best prediction of depth of drilling shows neural network model with average relative prediction error of 6, 16%.
drilling; thrust force and torque; regression analysis; neural network.
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Podaci o prilogu
7-12.
2009.
objavljeno
Podaci o matičnoj publikaciji
CIM 2009 COMPUTER INTEGRATED MANUFACTURING AND HIGH SPEED MACHINING
Abele, Eberhard ; Udiljak, Toma ; Ciglar, Damir
Zagreb: Hrvatska udruga proizvodnog strojarstva (HUPS)
978-953-7689-00-1
Podaci o skupu
12th INTERNATIONAL SCIENTIFIC CONFERENCE ON PRODUCTION ENGINEERING
predavanje
17.06.2009-20.06.2009
Biograd na Moru, Hrvatska