Among the various structural optimization tools, topology optimization is the widely used technique in obtaining the initial design of structural components. The resulting topologically optimal initial design will be the input for subsequent structural optimizations such as shape, size and layout optimizations. However, iterative solvers used in conventional topology optimization schemes are known to be computationally expensive, thus act as a bottleneck in the manufacturing process. In this paper, a novel deep learning-based accelerated topology optimization technique with the ability to predict ductile material failure is presented. A Conditional Generative Adversarial Network (cGAN) coupled with a Convolutional Neural Network (CNN) is used to predict the optimal topology of a given structure subject to a set of input variables. Subsequently, the same cGAN is trained to predict the Von-Mises stress contours on the optimal structure by means of color transformed image-to-image translations. The ductile failure criterion is evaluated by comparing the cGAN predicted maximum Von-Mises stress with the yield strength of the material. The proposed novel numerical method is proven to arrive at the topologically optimal design, accompanying the material failure decision within a negligible amount of time but also maintaining a higher prediction accuracy.

中文翻译：

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使用条件生成对抗网络进行拓扑优化设计和故障预测

在各种结构优化工具中，拓扑优化是获得结构部件初始设计的广泛使用的技术。由此产生的拓扑优化初始设计将成为后续结构优化（例如形状、尺寸和布局优化）的输入。然而，众所周知，传统拓扑优化方案中使用的迭代求解器计算量大，因此成为制造过程中的瓶颈。在本文中，提出了一种新型的基于深度学习的加速拓扑优化技术，具有预测延性材料失效的能力。条件生成对抗网络 (cGAN) 与卷积神经网络 (CNN) 相结合，用于预测受一组输入变量影响的给定结构的最佳拓扑。随后，训练相同的 cGAN 以通过颜色转换的图像到图像的转换来预测最佳结构上的 Von-Mises 应力轮廓。延性破坏准则是通过比较 cGAN 预测的最大 Von-Mises 应力与材料的屈服强度来评估的。所提出的新型数值方法被证明可以达到拓扑优化设计，在可忽略不计的时间内做出材料失效决策，同时保持更高的预测精度。