Optimizing mechanical properties of PETG fabricated parts via PSO-BFO hybrid algorithm in fused deposition modelling

Polyethylene Terephthalate Glycol (PETG) material is utilized across various domains including orthopaedics, vascular health, and surgery, where the requirements for reliability, strength, and mechanical properties are important. In order to meet individual's needs, customized solutions are often needed. While PETG products can be manufactured through methods like injection molding, blow molding and thermoforming, these processes are costly and less adaptable to customization. In contrast, 3D printing offers a more cost-effective and customized solutions. Yet, its anisotropic nature poses challenges, leading to lower mechanical properties. In order to improve mechanical properties, our research focuses on improving important process factors such as build orientation, speed rate, infill density, nozzle temperature, and layer thickness. RSM is used in the DOE for the PETG material. Further, linear multiple regression establishes the relationship between input and output parameters, with ANOVA verifying statistical significance. Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), and a hybrid method combining PSO's global search with BFO's local search are then used to optimize parameters. The hybrid method overcomes PSO's local optima trapping and BFO's slower convergence, resulting in better fitness values and faster computation times compared to individual PSO and BFO.