BPNN-ACO application on minimization of hole delamination during GFRP drilling process

Abstract

Delamination defects during the drilling process on the glass fiber reinforced polymer (GFRP) have a great contribution to the component failure. Hence, it is necessary to properly choose the combination of machining variables to minimize hole entry delamination (EnDel) and hole exit delamination (ExDel) during drilling process simultaneously. This study underlines the modelling and minimizing the EnDel and hole ExDel during GFRP drilling process by combining a backpropagation neural network (BPNN) method and ant colony optimization (ACO). The varied drilling parameters were type of drill point angle, feeding speed, and cutting speed. The optimum BPNN architecture could be obtained by using 3-4-2-5-8-2 network architecture with tansig activation function. The optimum GFRP drilling parameters that can minimize EnDel and ExDel simultaneously were 116° of drill point angle, 51.3 mm min−1 of feeding speed and 4975 rpm of spindle speed.