Prediction of surface roughness and delamination in end milling of GFRP using mathematical model and ANN

Abstract

Glass fiber reinforced plastics (GFRP) composite is considered to be an alternative to heavy exortic materials. Accordingly, the need for accurate machining of composites has increased enormously. During machining, the reduction of delamination and obtaining good surface roughness is an important aspect. The present investigation deals with the study and development of a surface roughness and delamination prediction model for the machining of GFRP plate using mathematical model and artificial neural network (ANN) multi objective technique. The mathematical model is developed using RSM in order to study main and interaction effects of machining parameters. The competence of the developed model is verified by using coefficient of determination and residual analysis. ANN models have been developed to predict the surface roughness and delamination on machining GFRP components within the range of variables studied. Predicted values of surface roughness and delamination by both models are compared with the experimental values. The results of the prediction models are quite close with experiment values. The influences of different parameters in machining GFRP composite have been analyzed.