How to realize the switching between various topology optimization approaches such as SIMP and moving morphable component (MMC) method, is a crucial challenge in the field of structural design. In this article, a robust conversion framework is proposed to convert a pixel-type topology optimization result to MMC representation. Based on the sparse optimization approach, the framework enables the determination of the minimum number of components with a specified shape error. This method provides an efficient bridge for these two types of geometric descriptions, and promotes the free switching between the topology optimization frameworks with pixel-based and MMC-based design domains. The proposed procedure contains a pre-processing of resolution improvement, symmetry axis extraction with sparse optimization, and variational shape approximation. Two practical applications are demonstrated using the proposed framework. First, it can be applied to the intermediate results of SIMP, to achieve faster optimization convergence. Furthermore, a stress-based shape optimization approach can be applied to the obtained MMCs, and novel progressive continuity constraints are also introduced to maintain boundary continuity. Several examples demonstrate advantages of the proposed framework.

中文翻译：

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基于稀疏优化的像素型拓扑优化结果到MMC表示及其应用

如何实现SIMP和移动变形组件（MMC）等多种拓扑优化方法之间的切换，是结构设计领域的一个关键挑战。在本文中，提出了一种鲁棒的转换框架，将像素型拓扑优化结果转换为 MMC 表示。基于稀疏优化方法，该框架能够确定具有指定形状误差的组件的最小数量。该方法为这两类几何描述提供了一个有效的桥梁，并促进了基于像素和基于MMC设计域的拓扑优化框架之间的自由切换。所提出的过程包含分辨率改进的预处理、稀疏优化的对称轴提取以及变分形状近似。使用所提出的框架演示了两个实际应用。首先，它可以应用于SIMP的中间结果，以实现更快的优化收敛。此外，基于应力的形状优化方法可以应用于所获得的MMC，并且还引入新颖的渐进连续性约束来保持边界连续性。几个例子展示了所提出的框架的优点。