Optimising the Impact Strength of 3D Printed PLA Components Using Metaheuristic Algorithms

This study investigates the correlation among the impact strength of Polylactic acid (PLA) material as well as many 3D printing parameters, including layer height, infill density, extrusion temperature, and print speed, using Fused Deposition Modelling (FDM) in Additive Manufacturing (AM). By using well-planned trials, the ASTM D256 standard assessed the impact strength of samples. Impact strength was optimized using six distinct techniques: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Teaching Learning Based Optimization (TLBO), and Cohort Intelligence (CI). These approaches are reliable since they consistently delivered similar impact strength values after several iterations. The best algorithms, according to the study, were TLBO and JAYA, which produced a maximum impact strength of 4.08 kJ/m². The algorithms’ effectiveness was validated by validation studies, which showed little error and near matches between the expected and actual impact strength values. The advantages of employing these methods to increase the impact strength of PLA material for 3D printing are illustrated in the present research, which provides helpful insights on how to improve FDM procedures.