Hybrid optimization and prediction of mechanical properties of copper-enhanced ABS using Taguchi, ANN, and CSA–GWO algorithm

Purpose This study aims to enhance the mechanical properties of 3D-printed acrylonitrile butadiene styrene (ABS) by reinforcing it with copper and optimizing key process parameters using Taguchi, artificial neural network (ANN) and metaheuristic optimization techniques. Design/methodology/approach Copper-reinforced ABS filaments were fabricated using a twin-screw extruder and printed via fused deposition modelling. A Taguchi L25 design was used to study the effects of printing temperature and layer height on tensile, compressive and flexural strengths. An ANN was trained on experimental data to model these properties, and a hybrid crow search algorithm–grey wolf optimizer (CSA–GWO) was used for multi-objective parameter optimization. Findings The Taguchi method identified printing temperature as the most influential factor. The ANN model demonstrated high predictive accuracy, achieving R² values exceeding 0.99 and maintaining prediction errors below 2%. The hybrid CSA–GWO algorithm effectively identified optimal parameters for maximizing each mechanical property, with a balanced setting of 245.68°C and 0.100 mm providing strong overall performance (947.97 N tensile, 4,173.61 N compressive and 176.06 N flexural). Originality/value The use of a hybrid CSA–GWO algorithm presents a novel approach within the additive manufacturing domain, offering enhanced exploration and convergence capabilities for optimizing mechanical properties of copper-reinforced ABS composites.