Speaker
Description
Mechanical metamaterials are architected materials whose mechanical properties are determined by the combination of a bulk material and a designed microstructure. Therefore, these materials are ideally suited for inverse design tasks, i.e., for finding a specific metamaterial for a given property. Within this contribution, we present a neural network (NN)-based inverse design approach and apply it to two well-known classes of metamaterials, triply periodic minimal surfaces (TPMS) [1] and spinodoid metamaterials [2]. TPMS metamaterials are mathematically described through combinations of trigonometric functions and are characterized by having a zero mean surface curvature and periodicity in 3D space. Within the scope of inverse design, it becomes necessary to be able to describe a structure with a set of design parameters, also referred to as descriptors, enabling the seamless tuning of the geometry to meet target mechanical properties. Even though there is a wide variety of TPMS types exhibiting different topologies, they are lacking such a common descriptor space, complicating their application in inverse design tasks. In recent years, spinodoid metamaterials inspired by naturally occurring spinodal topologies gained a lot of attention due to some favourable properties like their tunable anisotropy and non-periodicity, and thus robustness against symmetry-breaking defects [2]. These structures offer a vast design space defined by only a small set of design parameters, i.e., descriptors. The relatively simple parametric representation of those complex spinodoid structures poses a unique opportunity to create architected materials with precisely adjustable mechanical properties, making them favourable for the application in inverse design [3]. After introducing the design possibilities of both architecture types, they are compared with regard to achievable elastic properties based on the formulation of an inverse design task. Specifically, we solve two different design tasks: maximizing stiffness in one direction for a given density and mimicking a fully specified stiffness.
References
[1] Fisher, J.W. et al. Catalog of triply periodic minimal surfaces, equation-based lattice structures, and their homogenized property data. Data in Brief 49, (2023).
[2] Kumar, S. et al. Inverse-designed spinodoid metamaterials. npj Comput. Mater. 6, 73 (2020).
[3] Raßloff, A. et al. Inverse design of spinodoid structures using Bayesian optimization, (2024).
