Algorithmically-consistent deep learning frameworks for structural topology optimization
Algorithmically-consistent deep learning frameworks for structural topology optimization
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DOI：
10.1016/j.engappai.2021.104483
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发表时间：
2021-10-09
影响因子：
8
通讯作者：
Krishnamurthy, Adarsh
中科院分区：
文献类型：
--
作者：
Rade, Jaydeep;Balu, Aditya;Krishnamurthy, Adarsh
拓扑优化已经成为改进组件设计和提高其性能的一种流行方法。然而，目前最先进的拓扑优化框架是计算密集型的，主要是由于在优化过程中需要多次有限元分析迭代来评估组件的性能。最近，研究人员探索了基于机器学习（ML）的拓扑优化方法来缓解这一问题。然而，以前的机器学习方法主要是在具有低分辨率几何的简单二维应用程序上进行演示的。此外，目前的方法是基于单个ML模型进行端到端预测，这需要大量的数据集进行训练。这些挑战使得将当前的方法扩展到更高的分辨率变得非常重要。在本文中，我们开发了与传统拓扑优化算法一致的基于深度学习的框架，用于具有合理精细（高）分辨率的3D拓扑优化。我们通过训练多个网络来实现这一点，每个网络学习整体拓扑优化方法的不同步骤，使框架与拓扑优化算法更加一致。我们演示了我们的框架在二维和三维几何上的应用。结果表明，与现有基于ml的拓扑优化方法相比，我们的方法可以更好地预测最终优化设计（二维总柔度误差降低5.76倍，三维总柔度误差降低2.03倍）。
Topology optimization has emerged as a popular approach to refine a component's design and increase its performance. However, current state-of-the-art topology optimization frameworks are compute-intensive, mainly due to multiple finite element analysis iterations required to evaluate the component's performance during the optimization process. Recently, machine learning (ML)-based topology optimization methods have been explored by researchers to alleviate this issue. However, previous ML approaches have mainly been demonstrated on simple two-dimensional applications with low-resolution geometry. Further, current methods are based on a single ML model for end-to-end prediction, which requires a large dataset for training. These challenges make it non-trivial to extend current approaches to higher resolutions. In this paper, we develop deep learning-based frameworks consistent with traditional topology optimization algorithms for 3D topology optimization with a reasonably fine (high) resolution. We achieve this by training multiple networks, each learning a different step of the overall topology optimization methodology, making the framework more consistent with the topology optimization algorithm. We demonstrate the application of our framework on both 2D and 3D geometries. The results show that our approach predicts the final optimized design better (5.76x reduction in total compliance MSE in 2D; 2.03x reduction in total compliance MSE in 3D) than current ML-based topology optimization methods.