同济大学学报（自然科学版）2024，Vol.52Issue(z1)：20-28,9.DOI:10.11908/j.issn.0253-374x.24775
基于深度学习的多参数结构拓扑优化方法
Multi-Parameter Structural Topology Optimization Method Based On Deep Learning
摘要
Abstract
The traditional topology optimization method based on finite element method requires multiple finite element calculation and iterations,which consumes a lot of computational resources and time.In order to improve the efficiency of topology optimization,the paper takes topology optimization of cantilever beam as an example and proposes a generative convolutional neural network(CNN)model based on residual connections,which considers four optimization parameters:filter radius,volume fraction,loading point and loading direction.And the influence of different loss functions and number of samples on the accuracy of generative CNN model is discussed at length.The results show that the proposed model has high accuracy and generalization ability,and the mean structural similarity index between the model prediction and finite element method can reach 0.9720,the mean absolute error is 0.0143.And the prediction time of the model is only 0.0041 of finite element method,which significantly improves the efficiency of topology optimization.关键词
拓扑优化/卷积神经网络/固体各向同性材料惩罚模型/结构相似度Key words
topology optimization/convolutional neural networks/solid isotropic material with penalization/structural similarity index分类
交通工程引用本文复制引用
楚遵康,余海燕,高泽,饶卫雄..基于深度学习的多参数结构拓扑优化方法[J].同济大学学报（自然科学版）,2024,52(z1):20-28,9.基金项目
国家重点研发计划(2022YFE0208000) （2022YFE0208000）
