JFET_V5_N1_RP8 N. Muthukrishnan Journal on Future Engineering and Technology 2230 – 7184 5 1 67 72 Metal Matrix Composites, Machining, Surface Finish, Tool Wear, ANN The paper presents the results of an experimental investigation on the machinability of fabricated Aluminum metal matrix composite (A356/SiC/10p) during continuous turning of composite rods using medium grade Polycrystalline Diamond (PCD 1500) inserts. Experiments were conducted at LMW-CNC-LAL-2 production lathe at various cutting conditions and parameters such as surface roughness and tool wear were measured. The influences of cutting speed on the insert wear were studied. An Artificial Neural Network (ANN) model has been developed for prediction of machinability parameters of MMC using feed forward back propagation algorithm. The various stages in the development of ANN models VIZ. selection of network type, input and out put of the network, arriving at a suitable network configuration, training of the network, validation of the resulting network has been taken up. A 2-9-2 feed forward neural network has been successfully trained and validated to act as a model for predicting the machining parameters of Al-SiC (10p) MMC. The ANN models after successful training are able to predict the surface quality; tool wear for a given set of input values of cutting speed and machining time. August - October 2009 Copyright © 2009 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=1021