Process Parameter Optimization for Enhanced Compressive Strength of FDM-Printed ABS and Glass-Fibre-Reinforced ABS Using RSM and Metaheuristic Algorithms

This study systematically investigates the effect of three Fused Deposition Modelling (FDM) printing parameters, i.e., wall thickness, layer height, and infill density, on the compressive behaviour of FDM-printed acrylonitrile butadiene styrene (ABS) and glass-fibre-reinforced ABS (ABS-GF). A Response Surface Methodology (RSM)-based Central Composite Design (CCD) comprising twenty experimental runs was employed to execute the experiments and develop regression models. The statistical significance of the process variables was determined through Analysis of Variance (ANOVA). Additionally, three nature-inspired optimization techniques, namely, the Firefly Algorithm (FA), Big Bang–Big Crunch (BB-BC), and Grey Wolf Optimization (GWO), were applied to identify the optimal printing conditions for achieving maximum compression strength. Experimental results demonstrated that glass-fibre-reinforced ABS exhibited superior compressive strength, greater load-carrying capacity, and more favourable deformation characteristics compared to neat ABS. Among the investigated parameters, infill density was found to be highly influential, as it controlled the internal porosity and structural continuity of the printed parts. The quadratic regression models developed exhibited excellent prediction accuracy, with coefficients of determination exceeding 95% for ABS and 97% for ABS-GF. Optimization results showed that the GWO algorithm achieved the highest fitness value of 97.15% and 98.48% for ABS and ABS-GF, respectively. The proposed optimization approach provides an effective framework for optimizing the compressive behaviour of FDM-printed polymer composites, thereby offering valuable guidance for the design and manufacture of high-performance engineering components through additive manufacturing techniques.