论文标题
通过估算材料分布的合规性敏感性，基于卷积神经网络的拓扑优化（CNN-TO）
Convolutional Neural Network-based Topology Optimization (CNN-TO) By Estimating Sensitivity of Compliance from Material Distribution
论文作者
论文摘要
本文提出了一种应用卷积神经网络（CNN）的新拓扑优化方法，该方法是一种用于拓扑优化问题的深度学习技术。使用此方法，我们获得了一个具有更高性能的结构，而先前的拓扑优化方法无法获得。特别是在本文中，我们解决了一个旨在使用质量约束最大化刚度的拓扑优化问题，这是拓扑优化的一种常见类型。在本文中，我们首先通过固体各向同性材料制定了常规的拓扑优化，并使用惩罚方法制定。接下来，我们使用CNN制定拓扑优化。最后，我们通过求解验证示例，即旨在最大化刚度的拓扑优化问题，展示了提出的拓扑优化方法的有效性。在这项研究中，由于解决了16x32元素的小型设计区域的验证示例，我们获得了与先前拓扑优化方法不同的解决方案。该结果表明，可以通过使用CNN（例如图像）分析密度分布来提取结构的刚度信息，以进行结构设计。这表明CNN技术可以用于结构设计和拓扑优化。
This paper proposes a new topology optimization method that applies a convolutional neural network (CNN), which is one deep learning technique for topology optimization problems. Using this method, we acquire a structure with a little higher performance that could not be obtained by the previous topology optimization method. In particular, in this paper, we solve a topology optimization problem aimed at maximizing stiffness with a mass constraint, which is a common type of topology optimization. In this paper, we first formulate the conventional topology optimization by the solid isotropic material with penalization method. Next, we formulate the topology optimization using CNN. Finally, we show the effectiveness of the proposed topology optimization method by solving a verification example, namely a topology optimization problem aimed at maximizing stiffness. In this research, as a result of solving the verification example for a small design area of 16x32 element, we obtain the solution different from the previous topology optimization method. This result suggests that stiffness information of structure can be extracted and analyzed for structural design by analyzing the density distribution using CNN like an image. This suggests that CNN technology can be utilized in the structural design and topology optimization.
