Poisson’s Ratio Control in Auxetic Metamaterials Under Large Tensile Strains

Auxetic materials, known for their negative Poisson's ratios, offer enhanced mechanical properties like indentation resistance and energy absorption. However, maintaining a consistent Poisson's ratio under large strains is challenging due to geometric nonlinearities. This study introduces a data-driven framework for designing auxetic structures with programmable Poisson’s ratio behavior over large deformations. A parametric finite element (FE) model is developed using Abaqus, and a machine learning surrogate model is trained to predict Poisson's ratio based on geometric parameters within a 25% tensile strain range. An inverse design framework is established to optimize building block geometry for a target Poisson's ratio. Optimized 3D-printed specimens are tested, showing excellent agreement with predictions, validating the framework. This approach provides an efficient tool for designing auxetic materials with controlled Poisson's ratio behavior, enabling applications in soft robotics and morphing structures.