Data-Driven Structural Design Optimization for Petal-Shaped Auxetics Using Isogeometric Analysis  

作　　者：Yingjun Wang Zhongyuan Liao Shengyu Shi Zhenpei Wang Leong Hien Poh 

机构地区：[1]National Engineering Research Center of Novel Equipment for Polymer Processing,the Key Laboratory of Polymer Processing Engineering of the Ministry of Education(South China University of Technology),Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing,South China University of Technology,Guangzhou,510641,China. [2]Institute of High Performance Computing(IHPC),Agency for Science,Technology and Research(A*STAR),1 Fusionopolis Way,138632,Singapore. [3]Department of Civil and Environmental Engineering,National University of Singapore 1 Engineering Drive 2,E1A 07-03,117576,Singapore

出　　处：《Computer Modeling in Engineering & Sciences》2020年第2期433-458,共26页工程与科学中的计算机建模（英文）

基　　金：National Natural Science Foundation of China(Grant Nos.51705158 and 51805174);the Fundamental Research Funds for the Central Universities(Grant Nos.2018MS45 and 2019MS059)。

摘　　要：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 NURBSbased 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.

关 键 词：DATA-DRIVEN BP neural network petal-shaped auxetics negative Poisson’s ratio structural design isogeometric analysis. 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]