Buckling prediction and structural optimization of sandwich plates with negative Poisson’s ratio core

Abstract

Negative Poisson’s ratio (NPR) materials are attractive for their unique mechanical properties. Especially lightweight structures made of NPR materials have potential application in the aviation industry. The purpose of this study is to propose a lightweight structure with NPR materials and optimize it with its performance and mass as the objectives. In this study, buckling prediction and structural optimization of a sandwich plate with an NPR core are investigated by using artificial neural networks (ANN) and genetic algorithms (GA). A three-dimensional NPR structure for the core of the sandwich plate is presented, and the equivalent strain energy method is used to obtain the effective material properties of the NPR core. The governing equation and the corresponding analytical solution for the buckling of the sandwich plate are derived. In the numerical examples, the effect of the design parameters in sandwich plates with an NPR core on the critical buckling load is analyzed using the ANN. The ANN and the GA are also employed to predict the optimized maximum critical buckling load and minimum cell mass of the NPR sandwich plate. The Pareto-frontier curves for the multi-objective optimization under different core-to-thickness ratios are further obtained, with optimal solutions under different design conditions.