Remixing functionally graded structures: data-driven topology optimization with multiclass shape blending
Remixing functionally graded structures: data-driven topology optimization with multiclass shape blending
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DOI：
10.1007/s00158-022-03224-x
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发表时间：
2021-12
影响因子：
3.9
通讯作者：
Yu-Chin Chan;D. Da;Liwei Wang;Wei Chen
中科院分区：
文献类型：
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作者：
Yu-Chin Chan;D. Da;Liwei Wang;Wei Chen
为了创建具有前所未有的功能的异构多尺度结构，最近的拓扑优化方法设计完全非周期系统或功能梯度结构，在设计自由度和效率方面进行竞争。我们建议通过一个数据驱动的框架来继承两者的优点，该框架用于混合了几个家族的多类功能梯度结构，即，类，微结构拓扑结构，以创造空间变化的设计与保证可行性。关键是一个新的多类形状混合方案，生成平滑渐变的微观结构，而不需要兼容的类或连接性和可行性约束。此外，它转换成一个有效的，低维的微尺度问题，而不限制设计预定义的形状。合规性和形状匹配的例子，使用常见的桁架几何形状和多样性为基础的自由形式的拓扑结构证明了我们的框架的多功能性，而类的数量和多样性的影响的研究说明了有效性。所提出的方法的一般性支持未来的扩展超出线性应用程序。
To create heterogeneous, multiscale structures with unprecedented functionalities, recent topology optimization approaches design either fully aperiodic systems or functionally graded structures, which compete in terms of design freedom and efficiency. We propose to inherit the advantages of both through a data-driven framework for multiclass functionally graded structures that mixes several families, i.e., classes, of microstructure topologies to create spatially-varying designs with guaranteed feasibility. The key is a new multiclass shape blending scheme that generates smoothly graded microstructures without requiring compatible classes or connectivity and feasibility constraints. Moreover, it transforms the microscale problem into an efficient, low-dimensional one without confining the design to predefined shapes. Compliance and shape matching examples using common truss geometries and diversity-based freeform topologies demonstrate the versatility of our framework, while studies on the effect of the number and diversity of classes illustrate the effectiveness. The generality of the proposed methods supports future extensions beyond the linear applications presented.