Optimum damage and surface roughness prediction in end milling glass fibre-reinforced plastics, using neural network and genetic algorithm

Abstract

This paper presents an approach for the determination of the optimal cutting parameters (spindle speed, feed rate, and depth of cut) and end mill flutes leading to minimum surface roughness and delamination factor in end milling of glass fibre reinforced plastics (GFRP) by coupling neural network (NN) and genetic algorithm (GA). In this regard, the advantages of statistical experimental design technique, experimental measurements, artificial neural network, and genetic optimization method are exploited in an integrated manner. The genetically optimized neural network system (GONNS) is proposed for the selection of the optimal cutting conditions from the experimental data when an analytical model is not available. GONNS uses back-propagation (BP) type NNs to represent the input and output relations of the considered system. The GA obtains the optimal operational conditions through using the NNs. From this, it can be clearly seen that a good agreement is observed between the predicted values and the experimental measurements.