Predicting Strut Geometry of PCL and DMSO Biocomposites from Nozzle to Deposition in Bio-Scaffold 3D Printing.

The field of tissue engineering increasingly demands accurate predictive models to optimize the 3D printing process of bio-scaffolds. This study presents a unified numerical model that predicts extrusion velocity and strut diameter based on printing conditions and the material properties of polycaprolactone (PCL) and dimethyl sulfone (DMSO) composites. The extrusion velocity was simulated using Navier-Stokes equations, while the strut diameter was calculated via a surface energy model. For PCL, the extrusion velocity showed a temperature coefficient of 23.3%/°C and a pressure coefficient of 19.1% per 100 kPa; the strut diameter exhibited a temperature coefficient of 21.6%/°C and a pressure coefficient of 16.6% per 100 kPa. When blended with DMSO, the lower viscosity and higher surface energy resulted in increased extrusion velocity and strut diameter. The proposed model achieved a high predictive accuracy, with determination coefficient (R²) values exceeding 0.95. These results demonstrate the model's potential to optimize 3D printing parameters, guide biomaterial selection, and predict pore characteristics, ultimately supporting the rational design of tissue engineering scaffolds.