Topology optimization of 3D-printed mycelium hydrogels

Area of Science:

• Biomaterials Engineering
• Computational Modeling
• Additive Manufacturing

Background:

• Biological materials offer sustainable, self-healing, and lightweight alternatives for structural applications.
• 3D-printing with fungi-inoculated hydrogels enables additive manufacturing of complex shapes.
• Lack of computational models hinders precise engineering of 3D-printed fungal structures.

Purpose of the Study:

• To develop a computational modeling scheme for 3D-printed mycelium structures.
• To link fungal growth dynamics to material stiffness for predictive engineering.
• To optimize the strength and mass of mycelium structures using topology optimization.

Main Methods:

• Modeled fungal growth using a diffusion model.
• Converted fungal density to local stiffness for computational representation.
• Implemented Bayesian optimization-based topology optimization schemes to maximize strength and minimize cost/mass.

Main Results:

• Developed a computational scheme linking fungal growth to mycelial density and local stiffness.
• Identified a distinct tradeoff between print mass and stiffness in optimized structures.
• Experimental validation confirmed the computational model's predictions.

Conclusions:

• The developed computational models enable precise engineering of 3D-printed mycelium structures.
• This work facilitates the adoption of fungal-based materials in engineering applications.
• Insights pave the way for harnessing emergent biological materials for sustainable engineering.