Topology optimization of 3D-printed mycelium hydrogels

Abstract

As we move towards more sustainable and resilient materials, new opportunities for harnessing the next generation of biological materials will arise. Materials composed of living organisms have great potential in fulfilling this role as a self-healing, lightweight and sustainable structural material. Recent advances in 3D-printing using fungi-inoculated hydrogels opens the potential of additive manufacturing with fungi into optimized shapes. However, while this technique of 3D-printing fungi has great potential in a wide range of engineering applications, computational models do not yet exist to precisely engineer the strength of structures made from this material. Here we create a computational modeling scheme for 3D-printed mycelium structures, linking the growth of fungi to stiffness. We first model the growth of fungi through a diffusion model. We then convert the resultant density values into local stiffness, creating a computational representation of the varying elemental stiffness as a function of local mycelial density. We implement two Bayesian optimization-based topology optimization schemes to maximize the strength of cuboid 3D-printed structures while minimizing the input material cost. One maximizes the material specific stiffness while the other applies a constrained scheme for identifying a minimized mass for a target design stiffness. Both show a distinct tradeoff in print mass to stiffness, with results validated experimentally. These new insights provide important next steps in the effective harnessing of this class of emergent material, as well as its larger adoption for engineering applications.