Data-Driven Structural Design Optimization for Petal-Shaped Auxetics Using Isogeometric Analysis
Yingjun Wang, Zhongyuan Liao, Shengyu Shi, Zhen-Pei Wang, Leong Hien Poh
Abstract
Focusing on the structural optimization of auxetic materials using data-driven methods, a back-propagation neural network (BPNN) based design framework is developed for petal-shaped auxetics using isogeometric analysis. Adopting a NURBS-based parametric modelling scheme with a small number of design variables, the highly nonlinear relation between the input geometry variables and the effective material properties is obtained using BPNN-based fitting method, and demonstrated in this work to give high accuracy and efficiency. Such BPNN-based fitting functions also enable an easy analytical sensitivity analysis, in contrast to the generally complex procedures of typical shape and size sensitivity approaches.