Effect of aluminium filler concentration on delamination in GFRP composite with optimized machining conditions using ANN-genetic algorithm

Abstract

Abstract Drilling is the most common operation that is performed on the composite material to facilitate the fastening of a composite with other components in an assembly. The present investigation investigates the most significant parameter that affects the quality of the drilled hole in glass fiber-reinforced composites. Epoxy resin is used in the matrix phase. Aluminum powder is used as a filler material in the epoxy matrix to reduce delamination. Laminated composites were fabricated by the hand-layup process with 0%, 3%, and 6 vol% aluminum fillers. Holes were drilled in the composites with the help of a computer numerical controlled drilling machine equipped with a dynamometer. Spindle speeds of 1000, 3000, and 5000 rpm, and feed rates of 50, 100, and 150 mm min−1 were the variables involved in the study. The thrust force and torque experienced during the machining process were measured. An artificial neural network model was executed using python programing language. The data obtained from the model was optimized using genetic algorithm to arrive the optimized machining conditions that would result in least delamination due to drilling.