论文标题
机械压缩的构建材料：通过模拟，深度学习和实验设计
Architected Materials for Mechanical Compression: Design via Simulation, Deep Learning, and Experimentation
论文作者
论文摘要
与普通的材料相比，构建的材料可以实现增强的性能。特定的架构是一种强大的设计杆，可以实现目标行为而无需更改基本材料。因此，从航空航天到民事应用到汽车应用，架构结构与所得属性之间的联系仍然是许多领域的开放场。在这里，我们专注于与机械压缩有关的属性，并设计层次蜂窝结构，以满足刚度和压缩应力的特定值。为此，我们在奇异的工作流程中采用了多种技术的组合，从分子动力学模拟开始对正面设计问题开始，并通过数据驱动的人工智能模型增强，以解决反相反的设计问题，并通过实验添加了添加性制造的样品，从而验证了从头结构的行为。因此，我们展示了一种用于构建设计的方法，该方法可推广到多种材料特性，而不可知论是基础材料的身份。
Architected materials can achieve enhanced properties compared to their plain counterparts. Specific architecting serves as a powerful design lever to achieve targeted behavior without changing the base material. Thus, the connection between architected structure and resultant properties remains an open field of great interest to many fields, from aerospace to civil to automotive applications. Here, we focus on properties related to mechanical compression, and design hierarchical honeycomb structures to meet specific values of stiffness and compressive stress. To do so, we employ a combination of techniques in a singular workflow, starting with molecular dynamics simulation of the forward design problem, augmenting with data-driven artificial intelligence models to address the inverse design problem, and verifying the behavior of de novo structures with experimentation of additively manufactured samples. We thereby demonstrate an approach for architected design that is generalizable to multiple material properties and agnostic to the identity of the base material.
