Enhancing 3D Printing of Gelatin/Siloxane-Based Cellular Scaffolds Using a Computational Model.

Area of Science:

• Biomaterials Engineering
• Additive Manufacturing
• Biotechnology

Background:

• Extrusion-based 3D bioprinting is increasingly used for biomedical applications.
• Optimizing printing parameters is crucial for achieving high-fidelity constructs.
• Gelatin/siloxane hybrid materials offer potential for biofabrication.

Purpose of the Study:

• To develop and validate a novel methodology for optimizing extrusion-based 3D bioprinting of a gelatin/siloxane hybrid material.
• To enhance the fidelity of 3D bioprinted constructs for biomedical applications.
• To integrate rheological characterization, CFD simulations, and machine learning for parameter optimization.

Main Methods:

• Rheological characterization was performed to determine material properties.
• Computational fluid dynamics (CFD) simulations were used to model the printing process.
• A machine-learning-based image analysis (convolutional neural network) was developed for post-printing assessment.
• A systematic optimization strategy combining these methods was employed.

Main Results:

• Initial printing parameters yielded 54.5% CAD similarity.
• Optimized parameters, guided by CFD and machine learning, achieved 92.35% CAD similarity.
• The integrated approach ultimately enabled fabrication of complex constructs with 94.13% CAD similarity.

Conclusions:

• The synergistic combination of CFD simulation and machine learning is effective for optimizing 3D bioprinting parameters.
• This integrated methodology significantly enhances the fidelity of biofabricated constructs.
• The approach holds great potential for advancing complex 3D construct fabrication in biomedical applications.