Comparative analysis of delamination factor prediction using RSM and ANN during endmilling of GFRP composites

Abstract

This research work is to study the comparison between a response surface methodology (RSM) and artificial neural network (ANN) in the modelling and prediction of delamination factor during endmilling of glass fibre reinforced polymer (GFRP) composites. Aiming to achieve this goal, several milling experiments were performed with polycrystalline diamond inserts at different machining parameters namely feed rate, cutting speed, depth of cut and fibre orientation angle. Mathematical model is created using central composite face centred second-order in RSM and the adequacy of the model was verified using analysis of variance. ANN model is created using back propagation algorithm. With regard to the machining test, it was observed that feed rate is the dominant parameter that affects the delamination factor followed by the fibre orientation. The comparison results show that models provide accurate prediction of delamination factor in which ANN perform better than RSM. The data predicted from ANN is very nearer to experimental results compared to RSM, therefore we can use this ANN model to determine the delamination factor for various FRP composites and also for various machining parameters.