Wallpaper Group-Based Mechanical Metamaterials: Dataset Including Mechanical Responses
Fleur Hendriks, Vlado Menkovski, Martin Do\v{s}k\'a\v{r}, Marc G.D. Geers, Kevin Verbeek, Ond\v{r}ej Roko\v{s}

TL;DR
This paper introduces a comprehensive dataset of 2D wallpaper group-based mechanical metamaterials, linking microstructure symmetries to mechanical responses, to support machine learning and symmetry studies in metamaterials design.
Contribution
It provides the first extensive dataset covering all 17 wallpaper groups with detailed mechanical responses, generated via a novel microstructure creation method and finite element analysis.
Findings
Dataset includes 1,020 geometries with 12,240 loading trajectories.
Supports development of surrogate models and symmetry-property studies.
Enables analysis of symmetry-breaking and emergent behaviors.
Abstract
Mechanical metamaterials often exhibit pattern transformations through instabilities, enabling applications in, e.g., soft robotics, sound reduction, and biomedicine. These transformations and their resulting mechanical properties are closely tied to the symmetries in these metamaterials' microstructures, which remain under-explored. Designing such materials is challenging due to the unbounded design space, and while machine learning offers promising tools, they require extensive training data. Here, we present a large dataset of 2D microstructures and their macroscopic mechanical responses in the hyperelastic, finite-strain regime, including buckling. The microstructures are generated using a novel method, which covers all 17 wallpaper symmetry groups and employs B\'ezier curves for a rich parametric space. Mechanical responses are obtained through finite element-based computational…
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Taxonomy
TopicsCellular and Composite Structures · Modular Robots and Swarm Intelligence
