Inverse design of a petal-shaped honeycomb with zero Poisson’s ratio and bi-directional tunable mechanical properties

Abstract

Zero Poisson’s ratio (ZPR) honeycombs are widely used in aerospace applications due to their high load carrying capacity, tunable performance and lightweight. However, its structural design is difficult and often requires designers to have extensive experience. With the gradual development of artificial intelligence, it becomes possible to obtain structural configurations using meta-models and desired mechanical properties. In this paper, a petal-shaped honeycomb (PSH) with bi-directional tunable mechanical properties possessing ZPR effect is designed. Parametric modelling and Latin hypercube sampling (LHS) are applied to reveal the effect of structural parameters on the bi-directional mechanical properties. Combined with Python scripts to automate the running of finite element analyses and complete the collection of results. An artificial neural network (ANN) is improved to achieve the performance prediction of the PSH with a minimum error of only 0.032%. The inverse design of the PSH is completed based on the mechanical properties required for the conceptual application with a minimum error of 2.375%. An automatic design system for PSH is proposed by integrating parametric models, Python scripts and modified ANN. The overall process reduces human control time through the automation of scripts, improves the honeycomb design efficiency, and provides a new systematic approach for the design of ZPR honeycombs.