Machine learning-based stiffness optimization of digital composite metamaterials with desired positive or negative Poisson's ratio

Abstract

Mechanical metamaterials such as auxetic materials have attracted great interest due to their unusual properties that are dictated by their architectures. However, these architected materials usually have low stiffness because of the bending or rotation deformation mechanisms in the microstructures. In this work, a convolutional neural network (CNN) based self-learning multi-objective optimization is performed to design digital composite materials. The CNN models have undergone rigorous training using randomly generated two-phase digital composite materials, along with their corresponding Poisson's ratios and stiffness values. Then the CNN models are used for designing composite material structures with the minimum Poisson's ratio at a given volume fraction constraint. Furthermore, we have designed composite materials with optimized stiffness while exhibiting a desired Poisson's ratio (negative, zero, or positive). The optimized designs have been successfully and efficiently obtained, and their validity has been confirmed through finite element analysis results. This self-learning multi-objective optimization model offers a promising approach for achieving comprehensive multi-objective optimization.