Extended Micromorphic Computational Homogenization for Mechanical Metamaterials Exhibiting Multiple Geometric Pattern Transformations

Abstract

Honeycomb-like microstructures have been shown to exhibit local elastic buckling under compression, with three possible geometric buckling modes, or pattern transformations. The individual pattern transformations, and consequently also spatially distributed patterns, can be induced by controlling the applied compression along two orthogonal directions. Exploitation of this property holds great potential in, e.g., soft robotics applications. For fast and optimal design, efficient numerical tools are required, capable of bridging the gap between the microstructural and engineering scale, while capturing all relevant pattern transformations. A micromorphic homogenization framework for materials exhibiting multiple pattern transformations is therefore presented in this paper, which extends the micromorphic scheme of Roko\v{s} et al., J. Mech. Phys. Solids 123, 119-137 (2019), for elastomeric metamaterials exhibiting only a single pattern transformation. The methodology is based on a suitable kinematic ansatz consisting of a smooth part, a set of spatially correlated fluctuating fields, and a remaining, spatially uncorrelated microfluctuation field. Whereas the latter field is neglected or condensed out at the level of each macroscopic material point, the magnitudes of the spatially correlated fluctuating fields emerge at the macroscale as micromorphic fields. We develop the balance equations which these micromorphic fields must satisfy as well as a computational homogenization approach to compute the generalized stresses featuring in these equations. To demonstrate the potential of the methodology, loading cases resulting in mixed modes in both space and time are studied and compared against full-scale simulations. It is shown that the proposed framework is capable of capturing the relevant phenomena, although the inherent multiplicity of solutions entails sensitivity to the initial guess.