Predicting Strut Geometry of PCL and DMSO<sub>2</sub> Biocomposites from Nozzle to Deposition in Bio-Scaffold 3D Printing.

Abstract

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 (DMSO₂) 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 DMSO₂, 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.