Inverse Design of a NURBS-Based Chiral Metamaterial Via Machine Learning for Programmable Mechanical Deformation

Abstract

Chiral metamaterials are manmade structures with extraordinary mechanical properties derived from their special geometric design instead of chemical composition. To make the mechanical deformation programmable, the non-uniform rational B-spline (NURBS) curves are taken to replace the traditional ligament boundaries of the chiral structure. The Neural networks are innovatively inserted into the calculation of mechanical properties of the chiral structure instead of finite element methods to improve computational efficiency. For the problem of finding structure configuration with specified mechanical properties, such as Young’s modulus, Poisson’s ratio or deformation, an inverse design method using the Neural network-based proxy model is proposed to build the relationship between mechanical properties and geometric configuration. To satisfy some more complex deformation requirements, a non-homogeneous inverse design method is proposed and verified through simulation and experiments. Numerical and test results reveal the high computational efficiency and accuracy of the proposed method in the design of chiral metamaterials.