Journal of Applied Sciences1812-56541812-5662Asian Network for Scientific Information10.3923/jas.2012.955.962p Metal Matrix Composites and Its Evaluation]]>DevarasiddappaD.ChandrasekaranM.MandalAmitava1020121210In the present study surface roughness prediction model for
end milling of Al-SiC_{p} metal matrix composites using artificial neural
network is developed. The SiC percentage in the metal matrix composites is considered
as one of the input parameters in addition to three prominent variables i.e.,
spindle speed, feed rate and depth of cut for predicting surface roughness being
the output variable. A multi layer perception network having 4-14-1 architecture
is found to be optimum network. The network is trained with 25 data sets and
the trained network predicts the surface roughness for the interpolated values
of input parameters. The result shows that the prediction performance of the
neural network model is highly encouraging with an average percentage error
being 0.31%. While response surface model predicts an average percentage error
of 0.53%. The surface roughness is mainly affected by feed rate followed by
other parameters spindle speed, SiC percentage and depth of cut.]]>Lin, J.T., D. Bhattacharyya and V. Kecman,2003Cramer, D.R., D.F. Taggart and Hypercar Inc.,2002Golzar, M. and M. Poorzeinolabedin,2010Ramanujam, R., R. Raju and N. Muthukrishan,2010Abburi, N.R. and U.S. Dixit,2006Chandrasekaran, M., M. Muralidhar, C.M. Krishna and U.S. Dixit,2010Risbood, K.A., U.S. Dixit and A.D. Sahasrabudhe,2003Sonar, D.K., U.S. Dixit and D.K. Ojha,2006Akkus, H. and I. Asilturk,2011Pradhan, M.K. and C.K. Biswas,2010Rajasekaran, T., K. Palanikumar and B.K. Vinayagam,2011Barman, T.K. and P. Sahoo,2009Basavarajappa, S., G. Chandramohan, M. Prabhu, K. Mukund and M. Ashwin,2007Arokiadass, R., K. Palanirajda and N. Alagumoorthi,2011p metal matrix composite.]]>Kohli, A. and U.S. Dixit,2005