Inverse-designed spinodoid metamaterials

Abstract

After a decade of periodic truss-, plate-, and shell-based architectures having dominated the design of metamaterials, we introduce the non-periodic class of spinodoid topologies. Inspired by natural self-assembly processes, spinodoid metamaterials are a close approximation of microstructures observed during spinodal phase separation. Their theoretical parametrization is so intriguingly simple that one can bypass costly phase-field simulations and obtain a rich and seamlessly tunable property space. Counter-intuitively, breaking with the periodicity of classical metamaterials is the enabling factor to the large property space and the ability to introduce seamless functional grading. We introduce an efficient and robust machine learning technique for the inverse design of (meta-)materials which, when applied to spinodoid topologies, enables us to generate uniform and functionally graded cellular mechanical metamaterials with tailored direction-dependent (anisotropic) stiffness and density. We specifically present biomimetic artificial bone architectures that not only reproduce the properties of trabecular bone accurately but also even geometrically resemble natural bone.

Discretely assembled mechanical metamaterials

“…Heterogeneous metamaterials have been shown to offer exponential combinatorial possibilities (43), as well as the ability to realize any arbitrary elasticity tensor (44). Furthermore, the design of new part geometries with blends of behavior is a promising next step for use in assembling spatially graded heterogeneous structures, which is one of the main benefits sought through additive processes (45) to achieve functionality seen in natural systems (46). By offering a simple yet diverse set of parts unified with a consistent assembly method, this work represents a significant step in lowering the barrier for entry to realizing the promise of mechanical metamaterials, especially for macroscale applications.…”

Characterising soft matter using machine learning

“…Finally, Kumar and coworkers have taken on the challenge of complex composite design in three dimensions with the aim of creating the desired anisotropic elastic properties. 99 This team is keen to create metamaterials while avoiding creating stress concentrations due to the use of trusses and or plates. To do this they focus on materials that are derived from the spinodal domain pattern familiar from phase separation.…”

Freely orientable microstructures for designing deformable 3D prints

“…The study of parameterized stochastic microstructures [Pham et al 2019;Tarantino et al 2019] has been gaining research focus in the last few years due to certain advantages it offers compared to periodic microstructures. First, stochastic microstructures allow for very progressive gradation and increased design freedom, as no grid or subdivision rule is constraining the structures [Siddhant et al 2020]. Namely, the spatial gradation of a stochastic process is intrinsically encoded by its parameters.…”