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Application of regression and ANN techniques for modeLling of the surface roughness in end milling machining process

Mohd. Zain, Azlan and Haron, Habibollah and Sharif, Safian (2009) Application of regression and ANN techniques for modeLling of the surface roughness in end milling machining process. In: Proceedings - 2009 3rd Asia International Conference on Modelling and Simulation, AMS 2009. Article number 5071981 . Institute of Electrical and Electronics Engineers, New York, pp. 188-193. ISBN 8-076953648-4

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Official URL: http://dx.doi.org/10.1109/AMS.2009.76

Abstract

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.

Item Type:Book Section
Additional Information:2009 3rd Asia International Conference on Modelling and Simulation, AMS 2009; Bandung, Bali; 25 May 2009 through 26 May 2009
Uncontrolled Keywords:ANN, machining, modeling, regression, surface roughness
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TJ Mechanical engineering and machinery
Divisions:Computer Science and Information System (Formerly known)
ID Code:13170
Deposited By: Ms Zalinda Shuratman
Deposited On:20 Jul 2011 09:06
Last Modified:14 Sep 2011 01:46

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