Abstract
In recent years, there has been a surge in the extrusion-based 3D printing of materials for various biomedical applications. This work presents a novel methodology for optimizing extrusion-based 3D bioprinting of a gelatin/siloxane hybrid material for biomedical applications. A systematic approach integrating rheological characterization, computational fluid dynamics simulation (CFD), and machine-learning-based image analysis, was employed. Rheological tests revealed a shear stress of 50 Pa, a maximum viscosity of 3 × 10(5) Pa·s, a minimum viscosity of 0.089 Pa·s, and a shear rate of 15 rad/s (27G nozzle, 180 kPa pressure, 32 °C temperature, 30 mm/s velocity) for a BIO X bioprinter. While these parameters yielded constructs with 54.5% similarity to the CAD design, a multi-faceted optimization strategy was implemented to enhance fidelity, computational fluid dynamics simulations in SolidWorks, coupled with a custom-develop a binary classifier convolutional neuronal network for post-printing image analysis, facilitated targeted parameter refinement. Subsequent printing optimized parameters (25G nozzle, 170 kPa, 32 °C, 20 mm/s) achieved a significantly improved similarity of 92.35% CAD, demonstrating efficacy. The synergistic combination of simulation and machine learning ultimately enabled the fabrication of complex 3D constructs with a high fidelity of 94.13% CAD similarity, demonstrating the efficacy and potential of this integrated approach for advanced biofabrication.