107-120Glass 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