Data-Efficient Discovery of Hyperelastic TPMS Metamaterials with Extreme Energy Dissipation

TL;DR

This paper introduces a data-efficient method for discovering novel hyperelastic TPMS metamaterials with exceptional energy dissipation, doubling the absorption capacity of existing structures through iterative optimization and machine learning.

Contribution

A new parametric design approach combined with a predictive uncertainty-aware model enables rapid discovery of high-performance hyperelastic TPMS structures with limited empirical data.

Findings

Achieved double the energy absorption compared to existing TPMS structures.

Developed a predictive model that guides efficient structure testing and fabrication.

Created the first open-source dataset of hyperelastic TPMS structures.

Abstract

Triply periodic minimal surfaces (TPMS) are a class of metamaterials with a variety of applications and well-known primitive morphologies. We present a new method for discovering novel microscale TPMS structures with exceptional energy-dissipation capabilities, achieving double the energy absorption of the best existing TPMS primitive structure. Our approach employs a parametric representation, allowing seamless interpolation between structures and representing a rich TPMS design space. As simulations are intractable for efficiently optimizing microscale hyperelastic structures, we propose a sample-efficient computational strategy for rapid discovery with limited empirical data from 3D-printed and tested samples that ensures high-fidelity results. We achieve this by leveraging a predictive uncertainty-aware Deep Ensembles model to identify which structures to fabricate and test next. We iteratively refine our model through batch Bayesian optimization, selecting structures for fabrication that maximize exploration of the performance space and exploitation of our energy-dissipation objective. Using our method, we produce the first open-source dataset of hyperelastic microscale TPMS structures, including a set of novel structures that demonstrate extreme energy dissipation capabilities, and show several potential applications of these structures.