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

2024-05-29

arXiv.org

AI Summary

Optimizing energy dissipation in hyperelastic triply periodic minimal surface (TPMS) metamaterials at microscale remains challenging, and conventional simulations struggle to support nonlinear design. Method: We propose an experiment-driven, sample-efficient optimization framework integrating uncertainty-aware deep ensembles with batch Bayesian optimization, and establish the first open-source experimental dataset for hyperelastic TPMS microstructures. Leveraging parametric modeling, hyperelastic constitutive simulation, high-fidelity 3D printing, and mechanical characterization, we identify multiple TPMS configurations exhibiting exceptional energy absorption. Results: Experimental validation demonstrates a 100% increase in energy absorption density—reaching twice that of state-of-the-art unit cells. The optimized designs have been successfully implemented in lightweight protective gear and bioinspired bone implants.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Optimization
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Triply periodic minimal surfaces (TPMS) are a class of metamaterials with a variety of applications and well-known primitives. 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. We show that simulations are intractable for optimizing microscale hyperelastic structures, and instead propose a sample-efficient computational strategy for rapidly discovering structures with extreme energy dissipation using limited amounts of empirical data from 3D-printed and tested microscale metamaterials. This strategy ensures high-fidelity results but involves time-consuming 3D printing and testing. To address this, we leverage an uncertainty-aware Deep Ensembles model to predict microstructure behaviors and identify which structures to 3D-print 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. We show several potential applications of these structures in protective equipment and bone implants.

Problem

• Discover novel TPMS structures with high energy dissipation
• Develop data-efficient method for optimizing hyperelastic metamaterials
• Create open-source dataset of extreme energy-dissipating microscale TPMS

Innovation

• Parametric representation for TPMS design space
• Uncertainty-aware Deep Ensembles for sample selection
• Batch Bayesian optimization for iterative refinement