Optimization of Surface Roughness and Delamination Damage of GFRP Composite Material in End Milling Using Taguchi Design Method and Artificial Neural Network

The present work is focused on the influence of cutting speed, feed rate and depth of cut on the delamination damage and surf ace roughness on Glass Fiber Reinforced Polymeric composite material (GFRP) during end milling. Taguchi design method is employed to investigate the machining characteristics of GFRP. From the results of ANOVA, it is concluded that cutting speed and depth of cut are the most significant factors affecting the responses, their contribution in an order of 26.84% and 40.44% respectively. Confirmatory experiments show that 5.052 μm for surface roughness and 1.682 delamination damage to validate the used approach after conducting with optimal setting of process parameters. Finally, artificial neural network has been applied to compare the predicted values with the experimental values, the deviations are found in the range of 3.7%, it shows good agreement between the predictive model results and the experimental measurements.