An efficient and high-resolution topology optimization method based on convolutional neural networks

作     者：Xue, Liang Liu, Jie Wen, Guilin Wang, Hongxin 

作者机构：State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Center for Research on Leading Technology of Special Equipment School of Mechanical and Electric Engineering Guangzhou University Guanghzou510006 China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2019年

核心收录：

主　　题：Shape optimization 

摘      要：Topology optimization is a pioneering design method that can provide various candidates with high mechanical properties. However, the high-resolution for the optimum structures is highly desired, normally in turn leading to computationally intractable puzzle, especially for the famous Solid Isotropic Material with Penalization (SIMP) method. In this paper, we introduce the Super-Resolution Convolutional Neural Network (SRCNN) technique into topology optimization framework to improve the resolution of topology solutions with extremely high computational efficiency. Additionally, a pooling strategy is established to balance the number of finite element analysis (FEA) and the output mesh in optimization process. Considering the high training cost of 3D neural networks, several 2D neural networks are combined to deal with 3D topology optimization design problems. The combined treatment method used in 3D topology optimization design eliminates the expense of retraining 3D convolutional neural network and guarantees the quality of 3D design. Some typical examples justify that the high-resolution topology optimization method adopting SRCNN has excellent applicability and high *** Codes 65k10(Primary), 68T05(Secondary) Copyright © 2019, The Authors. All rights reserved.