Controlling auxeticity in curved-beam metamaterials via a deep generative model

Abstract

Lattice-based mechanical metamaterials are known to exhibit quite a unique mechanical behavior owing to their rational internal architecture. This includes unusual properties such as a negative Poisson’s ratio, which can be easily tuned in reentrant-hexagonal metamaterials by adjusting the angles between beams. However, changing the angles also affects the geometrical dimensions of the unit cell. We show that by replacing traditional straight beams with curved ones, it is possible to control Poisson’s ratio of reentrant-hexagonal metamaterials keeping their overall dimensions constant. While the mechanical properties of these structures can be predicted through finite element simulations or, in some cases, analytically, many applications require the identification of architectures with specific target properties. To solve this inverse problem, we introduce a deep learning framework for generating metamaterials with desired properties. By supplying the generative model with a guide structure in addition to the target properties, we are able to generate a large number of alternative architectures with the same properties and express a preference for a specific shape. Deep learning predictions, together with experimental measurements, prove that this approach allows us to accurately generate unit cells fitting specific properties for curved-beam metamaterials.