Optimally‐Tailored Spinodal Architected Materials for Multiscale Design and Manufacturing

Abstract

Spinodal architected materials with tunable anisotropy unify optimal design and manufacturing of multiscale structures. By locally varying the spinodal class, orientation, and porosity during topology optimization, a large portion of the anisotropic material space is exploited such that material is efficiently placed along principal stress trajectories at the microscale. Additionally, the bicontinuous, nonperiodic, unstructured, and stochastic nature of spinodal architected materials promotes mechanical and biological functions not explicitly considered during optimization (e.g., insensitivity to imperfections, fluid transport conduits). Furthermore, in contrast to laminated composites or periodic, structured architected materials (e.g., lattices), the functional representation of spinodal architected materials leads to multiscale, optimized designs with clear physical interpretation that can be manufactured directly, without special treatment at spinodal transitions. Physical models of the optimized, spinodal‐embedded parts are manufactured using a scalable, voxel‐based strategy to communicate with a masked stereolithography (m‐SLA) 3D printer.

Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized Voronoi lattices

“…We expect the scope of this study to be extended to the inverse design of architected cellular materials with other target properties by replacing the labels – for example, diffusivity, permeability, and conductivity – for the sake of energy storage and conservation [ 33 , 72 , 73 ]. Finally, although we only focused on a typical geometry (Voronoi lattices) in this study, the proposed approach has the potential to combine other geometries created using other methods, such as triply periodic minimal surfaces, spinodal architectures, and foams [ 30 , 31 , 64 ], to enable the inverse design of architected cellular materials inside and outside the material property space [ 57 ].…”

“…Notably, most of these studies have adopted the forward design, that is, a structure is designed based on computational modeling methods, and its effective properties are explored using time-consuming simulations and/or experiments. Using such forward design methods, models can be generated via mathematical modeling [ 10 , 31 , 32 ], Boolean and lofting operations [ 7–9 , 12 , 13 ], and topology optimization [ 38 , 39 ]. However, this requires experienced designers and extensive trial-and-error efforts to achieve the desired properties.…”

Generating 3D architectured nature-inspired materials and granular media using diffusion models based on language cues

“…In this article, we used a pre-trained Stable Diffusion model and consider it as an experimental system—reflecting a broad corpus of human knowledge—to examine its capacity to generate novel material designs specifically in the context of 3D material architectures. Such materials design may find many applications ranging from optical to mechanical [ 60 , 61 ] or multifunctional and integrated responsive material systems [ 62 , 63 ].…”

Inverse Design of Energy‐Absorbing Metamaterials by Topology Optimization

“…First, the topology optimization of energy‐absorbing structures needs to consider the large geometric deformation of the structure and the nonlinear material constitutive relationship. The existing metamaterial topology optimization studies, aiming at minimum compliance, [ 15 ] negative Poisson's ratio, [ 16 ] and multiscale optimization, [ 17 ] are generally carried out under the assumption of small deformation and linear elasticity, rendering significant deviations under large deformations. Sigmund's group considered the finite deformation gradient, [ 18 ] and realized the optimization of nonlinear negative Poisson's ratio structure up to 30% strain [ 19 ] and nonlinear multimaterial structures.…”