Joint Material and Shape Optimization of a 3D Printing Nozzle by CFD and Response Surface Methodology

Abstract Three-dimensional printing (3D printing) has emerged as a transformative technology in medicine, enabling the fabrication of complex structures with high precision. In bioprinting, optimization of nozzle design is essential to balance print resolution, mechanical strength, and biocompatibility. This study presents a computational framework for optimizing bioprinting performance by simultaneously accounting for nozzle geometry and biomaterial rheological properties. Using Computational Fluid Dynamics (CFD) simulations in combination with Response Surface Methodology (RSM), the framework identifies optimal configurations that enhance both extrudability and cell viability. The approach includes single- and multi-objective optimization strategies, supported by 2D-axisymmetric simulations. Results demonstrated a reduction in maximum wall shear stress (WSSmax) from 4157.75 Pa to 3059.69 Pa, a decrease in average wall shear stress (WSSavg) from 584.89 Pa to 225.19 Pa, and an increase in volumetric flow rate from 17.38 mm3 s−1 to 207.54 mm3 s−1. Multi-objective analysis revealed trade-offs between stress minimization and flow enhancement, underscoring the benefit of integrated shape—material optimization. A supervised Support Vector Machine (SVM) classifier was employed to evaluate model performance and achieved high predictive accuracy across the design categories. This simulation-based framework provides an adaptable, data-driven alternative to experimental approaches, offering a systematic tool for improving bioprinting efficiency and biomaterial design.