Abstract
Extrusion-based 3D bioprinting is prevalent in tissue engineering, but enhancing precision is critical as demands for functionality and accuracy escalate. Process parameters (nozzle diameter d, layer height h, printing speed v(1), extrusion speed v(2)) significantly influence hydrogel deposition and structure formation. This study optimizes these parameters using an orthogonal experimental design and grey relational analysis. Hydrogel filament formability and the die swell ratio served as optimization objectives. A response mathematical model linking parameters to grey relational grade was established via support vector regression (SVR). Particle Swarm Optimization (PSO) then determined the optimal parameter combination: d = 0.6 mm, h = 0.3 mm, v(1) = 8 mm/s, and v(2) = 8 mm/s. Comparative experiments showed the optimized parameters predicted by the model with a mean error of 5.15% for printing precision, which outperformed random sets. This data-driven approach reduces uncertainties inherent in conventional simulation methods, enhancing predictive accuracy. The methodology establishes a novel framework for optimizing precision in extrusion-based 3D bioprinting.