Data-driven inverse design of composite triangular lattice structures

In this work, we introduce a class of novel bi-material composite triangular lattice structures. The inverse design of these structures is achieved by using a data-driven method. They exhibit a broad range of tunable effective elastic properties, i.e., the effective Young's and shear moduli span a few orders of magnitude, and the effective Poisson's ratio can be both negative and positive. We exploit the computational homogenization method to calculate the effective elastic constants of these structures with varying structural features to generate a representative dataset. Subsequently, we harness the dataset to train artificial neural network models for both forward prediction and inverse design. The forward model predicts the effective properties of a given structure, while the inverse model generates a structure design for the specified target properties. We validate the performance of these models by several examples such as optimizing the isotropic auxetic properties. The data-driven surrogate models greatly facilitate the practical application of these novel lattice structures for various structural and/or functional purposes.