Generative Design by Embedding Topology Optimization into Conditional Generative Adversarial Network
Generative Design by Embedding Topology Optimization into Conditional Generative Adversarial Network
复制标题
将拓扑优化嵌入条件生成对抗网络的生成设计
DOI：
10.1115/1.4062980
复制
发表时间：
2023
影响因子：
3.3
通讯作者：
Rosen, David W.
中科院分区：
文献类型：
--
作者：
Wang, Zhichao;Melkote, Shreyes;Rosen, David W.
生成设计（GD）技术已被提出在早期设计阶段生成大量设计，用于构思和探索目的。之前使用深度神经网络的 GD 研究需要在神经网络和设计优化之间进行繁琐的迭代，以及后处理以生成功能设计。此外，无法强制执行体积分数等设计约束。在本文中，提出了一种两阶段非迭代公式来克服这些限制。在第一阶段，利用条件生成对抗网络（cGAN）来控制设计参数。在第二阶段，拓扑优化（TO）被嵌入到cGAN（cGAN+ TO）中，以确保实现所需的功能。对拓扑优化中损耗项的不同组合和不同参数设置的测试证明了生成设计的多样性。进一步的研究表明，cGAN+ TO 可以通过在训练的第二阶段修改这些参数来扩展到不同的负载和边界条件，而无需重新训练第一阶段。结果表明，cGAN+ TO 可以高效、鲁棒地实现 GD。
Generative design (GD) techniques have been proposed to generate numerous designs at early design stages for ideation and exploration purposes. Previous research on GD using deep neural networks required tedious iterations between the neural network and design optimization, as well as post-processing to generate functional designs. Additionally, design constraints such as volume fraction could not be enforced. In this paper, a two-stage non-iterative formulation is proposed to overcome these limitations. In the first stage, a conditional generative adversarial network (cGAN) is utilized to control design parameters. In the second stage, topology optimization (TO) is embedded into cGAN (cGAN+ TO) to ensure that desired functionality is achieved. Tests on different combinations of loss terms and different parameter settings within topology optimization demonstrated the diversity of generated designs. Further study showed that cGAN+ TO can be extended to different load and boundary conditions by modifying these parameters in the second stage of training without having to retrain the first stage. Results demonstrate that GD can be realized efficiently and robustly by cGAN+ TO.