Data-driven Design of Isotropic and High-Stiffness TPMS-based Amorphousness-Induced Architected Material (TAAM)

TL;DR

This paper introduces a data-driven method to design TPMS-based architected materials with tunable amorphousness, achieving improved elastic isotropy and high stiffness for advanced engineering applications.

Contribution

It presents a novel framework combining geometric disorder control with multi-objective optimization to enhance isotropy and stiffness in TPMS-based materials.

Findings

TAAMs show significantly improved elastic isotropy.

Validated designs match numerical predictions.

High stiffness maintained across various densities.

Abstract

For their excellent stiffness-to-weight characteristics, triply periodic minimal surfaces (TPMS) are widely adopted in architected materials. However, their geometric regularity often leads to elastic anisotropy, limiting their effectiveness under complex loading. To address this, we propose TPMS-based amorphousness-induced architected materials (TAAMs), which incorporate controllable geometric disorder as a tunable design variable. This concept of designable amorphousness broadens the geometric design space, enabling the simultaneous optimization of stiffness and isotropy. A data-driven framework integrating computational homogenization with multi-objective Bayesian optimization is employed to discover high-performance TAAMs. Selected designs were fabricated using fused deposition modeling and validated through uniaxial compression tests, showing strong agreement with numerical predictions. Compared to conventional TPMS, TAAMs exhibit significantly improved elastic isotropy while maintaining high stiffness across various relative densities. This approach offers a robust and scalable pathway for developing architected materials tailored to applications requiring isotropic performance, such as biomedical implants, protective systems, and aerospace components.