基于引入ResUNet生成对抗式网络以柔度最小为目标的高效多材料拓扑优化设计

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中文摘要：拓扑优化由于其严格的数学理论，成为连续结构设计中决定材料分布的重要方法。然而，随着材料类型在设计域内的增加，传统拓扑优化方法的计算效率大大降低。本文提出了一种基于深度学习的拓扑优化方法，用于提高多材料结构拓扑优化设计的计算效率。首先，利用固体各向同性材料惩罚(SIMP)方法对大量多材料结构进行优化设计，构建了多材料拓扑优化数据集。其次，ResUNet被引入生成对抗式网络形成ResUNet-GAN，构建了设计参数和优化构型之间的高维映射。最后，利用预训练的ResUNet-GAN实现多材料拓扑优化设计。数值模拟验证了ResUNet-GAN成功地应用于双材料的悬臂梁、三材料悬臂梁、三材料简支梁结构的拓扑优化设计。研究表明，基于深度学习拓扑优化方法的计算效率优于传统方法，为高效拓扑优化设计的发展奠定了理论基础。

外文标题：Efficient multi-material topology optimization design with mini-mum compliance based on ResUNet involved generative adver-sarial network

外文摘要：Topology optimization is a common approach for material distribution in continuous structure due to its rigorous mathematical theory.However,with the increase of material types in design domain,the computational efficiency of traditional topology optimization for multiple materials problem is greatly decreased.In this paper,a novel deep learning-based topology opti-mization method is proposed to achieve multi-material structural design for improving computational efficiency.A large number of multi-material topological configurations are simulated by solid isotropic material with penalization(SIMP),to construct multi-material topology optimization dataset.Subsequently,ResUNet involved generative adversarial network(ResUNet-GAN)is developed for high-dimensional mapping from design parameters to the corresponding multi-material topological configuration.Finally,the ResUNet-GAN,trained by the multi-material dataset,is utilized to design multi-material topological configuration.Numerical simulations verify that the well-trained ResUNet-GAN is successfully applied to three types of cases:the cantilever beam with double materials,the cantilever beam with triple materials,and the half-MBB with triple materials.The deep learning-based topology optimization approach is superior to the conventional methods in terms of higher computational efficiency,performing the potential of such a data-driven method to accelerate the calculation of structural optimization design.

外文关键词：

Multiple materialsTopology optimizationGenerative adversarial networkDeep learning

作者：

李继承、叶红玲、魏南、董永佳

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作者单位：

Faculty of Materials and Manufacturing,Beijing University of Technology,Beijing 100124,China

关键词：

Multiple materials Topology optimization Generative adversarial network Deep learning

基金：

National Natural Science Foundation of ChinaNatural Science Foundation of Beijing,China

项目编号：

118720803192005

出版年：

2024

DOI：

10.1007/s10409-023-23185-x

力学学报(英文版)

CSTPCD

影响因子：0.363

ISSN：0567-7718

年,卷(期)：2024.40(3)