Abstract— In this paper optimization of End milling has been reported. In recent years GFRP have attracted increasing use for many purposes. The material has many excellent properties, such as high specific strength, high specific modulus of elasticity, light weight, good corrosion resistance, etc., the parameters are depth of cut, feed, speed and tool were varied. The experiments were designed based on statistical three level full factorial experimental design techniques. Back Propagation Feed Forward Artificial Neural Network (BPFF-ANN) has been used for prediction of surface roughness and Delamination. In the development of predictive models the cutting speed, feed, depth of cut and tool type were considered as the model variables. Twenty seven data were used for training the network. The required datas for predictive model are obtained by conducting a series of test and measuring surface roughness and delamination data. Good agreement is observed between the predictive model results and the experimental measurements.

Index Terms— GFRP, ANN, Back propagation, Delamination, Surface roughness

Cite: M Muthuvel and G Ranganath, " Optimization of Machining Parameters in Milling of Composite Materials," International Journal of Mechanical Engineering and Robotics Research, Vol. 1, No. 2, pp. 277-285, July 2012.