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Application of Regression and ANN Techniques for Modeling of the Surface Roughness in End Milling Machining Process

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3 Author(s)
Zain, A.M. ; Fac. of Comput. Sci. & Inf. Syst., Univ. Teknol. Malaysia, Skudai ; Haron, H. ; Sharif, S.

Development of mathematical models to predict the values of performance measure is important in order to have a better understanding of the machining process. Surface roughness is one of the most common performance measures in machining process and an effective parameter in representing the quality of machined surface. The minimization of the machining performance measures such as surface roughness must be formulated in the standard mathematical model. To predict the minimum values of surface roughness, the process of modeling is taken in this study. The developed model deals with real experimental data of the surface roughness performance measure in the end milling machining process. Two modeling approaches, regression and artificial neural network techniques are applied to predict the minimum value of surface roughness. The result of the modeling process indicated that artificial neural network technique gave a better prediction of surface roughness compared to the result of regression technique.

Published in:

Modelling & Simulation, 2009. AMS '09. Third Asia International Conference on

Date of Conference:

25-29 May 2009