Multi-objective optimization of 3D Printing process using genetic algorithm for fabrication of copper reinforced ABS parts

Abstract

Fused deposition modeling (FDM) is now used in various industriesto create functional parts using polymers and metal reinforced polymer composites to attain desired properties. This study has been performed to acquire best FDM process parameters for maximization of tensile strength, compressive strength, and flexural strength. Copper particle reinforced ABS filament is used for fabrication of test samples to highlight the applications of 3d printing in composites. Multi-objective optimization has been performed using genetic algorithm to optimize three input parameters of FDM i.e. printing temperature, infill pattern, and layer height. The mechanical properties were calculated through Taguchi L9 design of experiments approach. It has been noticed that there was conflicting effect of each FDM parameter on strength. By using a genetic algorithm, the parameters are optimized and hence single combination was achieved i.e. 244.90 °C printing temperature, 0.20 mm layer height, and zig-zag pattern. The study would be helpful to fabricate copper reinforced ABS components for electronics, automobile and aerospace industry.