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Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network

M. F. F., Ab Rashid and Mohd Rizal, Abdul Lani (2010) Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network. In: Proceedings of the World Congress on Engineering 2010, 30 June - 2 July 2010 , London, UK. pp. 2219-2224..

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Abstract

In CNC milling process, proper setting of cutting parameter is important to obtain better surface roughness. Unfortunately, conventional try and error method is time consuming as well as high cost. The purpose for this research is to develop mathematical model using multiple regression and artificial neural network model for artificial intelligent method. Spindle speed, feed rate, and depth of cut have been chosen as predictors in order to predict surface roughness. 27 samples were run by using FANUC CNC Milling α-T14E. The experiment is executed by using full-factorial design. Analysis of variances shows that the most significant parameter is feed rate followed by spindle speed and lastly depth of cut. After the predicted surface roughness has been obtained by using both methods, average percentage error is calculated. The mathematical model developed by using multiple regression method shows the accuracy of 86.7% which is reliable to be used in surface roughness prediction. On the other hand, artificial neural network technique shows the accuracy of 93.58% which is feasible and applicable in prediction of surface roughness. The result from this research is useful to be implemented in industry to reduce time and cost in surface roughness prediction.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: CNC milling; Surface roughness; Multiple regression; Artificial neural network
Subjects: T Technology > TS Manufactures
Faculty/Division: Faculty of Mechanical Engineering
Depositing User: Dr. Mohd Fadzil Faisae Ab. Rashid
Date Deposited: 03 Apr 2014 04:22
Last Modified: 03 Mar 2015 09:24
URI: http://umpir.ump.edu.my/id/eprint/5278
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