Optimization analysis of variable gradient structures with shape memory characteristics in zero poisson’s ratio metamaterials

Abstract

Mechanical metamaterials can achieve fantastic properties fabricated using artificial structural design. In this study, shape memory polymers (SMP) were combined to design variable gradient zero Poisson ratio mechanical metamaterials and 3D printing was used to fabricate complex structures. The shape memory performance of these structures was investigated by conducting simulation calculations to analyze the variations of zero Poisson’s ratio structures with different wall thicknesses, cell internal angles, and inclined wall length gradients. Through the analysis of structural dimension factors, it is concluded that the structures with smaller wall thickness and intracellular angle exhibit better shape memory performance. In order to further enhance the shape memory performance, several models with identical wall thickness and internal angles were designed to investigate the influence of inclined wall length gradients on shape memory characteristics, leading to the identification of optimal gradient structures. Finally, thermal cycling experiments were conducted on samples to validate the accuracy of the simulation results. The investigation of shape memory recovery characteristics in variable gradient zero Poisson’s ratio structures provides new insight and method for the optimization design and application of smart materials in mechanical metamaterial structures.