Hybrid response surface methodology–particle swarm optimization framework for predictive modeling and tensile strength optimization of PLA bio-composites

Developing mechanically robust biodegradable composites is critical for next-generation orthopedic support devices. Although polylactic acid (PLA) is widely used in additive manufacturing, incorporating fillers can lead to reduced tensile performance when interfacial bonding with the matrix is inadequate. This study aims to enhance the mechanical performance of 3D-printed PLA reinforced with 2 wt% rice husk-derived silica (SiO 2 ) through optimized post-annealing. A hybrid statistical–computational framework combining Response Surface Methodology (RSM) and Particle Swarm Optimization (PSO) was implemented to identify optimal print speed, annealing temperature, and annealing time. PSO predicted the optimal conditions as 50 mm/s, 90.90°C, and 60 min, respectively, corresponding to a projected ultimate tensile strength (UTS) of 54.65 MPa. Confirmation experiments validated the prediction, yielding a mean UTS of 54.49 MPa with an error below 1%. Scanning electron microscopy revealed improved interlayer fusion and enhanced ductility in the optimized samples relative to unoptimized ones. Overall, the integration of RSM and PSO effectively refined post-annealing conditions without modifying material composition, demonstrating a viable strategy for strengthening PLA-based biocomposites. The proposed framework provides a practical route for tailoring mechanical properties in biomedical additive manufacturing, particularly for load-bearing orthopedic applications.