Nonlinear performance analysis and rapid prediction of out-of-plane deformation in graded honeycombs

Abstract

Honeycomb structures, known for their excellent properties, are widely used in various advanced applications, including adaptive mirrors and soft wearable devices, due to their out-of-plane deformation capabilities. However, predicting the out-of-plane deformation of graded honeycombs remains challenging. A novel approach for rapidly predicting the out-of-plane deformation of graded honeycombs, considering their isotropic and nonlinear behavior, is presented in this study. Discrete material property spaces for seven honeycomb types were derived using a stiffness-updating nonlinear homogenization method and validated through digital image correlation (DIC) experiments. Prediction of nonlinear equivalent properties within two seconds was achieved by utilizing a hyperparameter optimization neural network (HONN). Graded honeycomb connection criteria (GHCC) were established to ensure performance stability. A rapid and accurate prediction method was enabled by the developed deformation-to-color mapping, which effectively bypasses costly numerical computations. Out-of-plane deformation is accurately forecasted by this approach, which also facilitates the transformation of flat surfaces into various shapes with distinct Gaussian curvatures, thereby opening new possibilities for large-scale deformable structures.