期刊
APL MATERIALS
卷 10, 期 4, 页码 -出版社
AIP Publishing
DOI: 10.1063/5.0082338
关键词
-
资金
- MIT-IBM AI Lab
- ONR [N000141912375, N000142012189]
- AFOSR-MURI [FA9550-15-1-0514]
- ARO [W911NF1920098]
- MIT Quest
- U.S. Department of Defense (DOD) [N000142012189] Funding Source: U.S. Department of Defense (DOD)
This paper describes a method for generating 3D architected materials based on mathematically parameterized human readable word input. The researchers used a combination of generative adversarial networks and language-image pre-training neural networks to generate images, which were then translated into 3D architectures and 3D printed. This method has broad applications and can provide new avenues for the analysis and manufacturing of architected materials.
We describe a method to generate 3D architected materials based on mathematically parameterized human readable word input, offering a direct materialization of language. Our method uses a combination of a vector quantized generative adversarial network and contrastive language-image pre-training neural networks to generate images, which are translated into 3D architectures that are then 3D printed using fused deposition modeling into materials with varying rigidity. The novel materials are further analyzed in a metallic realization as an aluminum-based nano-architecture, using molecular dynamics modeling and thereby providing mechanistic insights into the physical behavior of the material under extreme compressive loading. This work offers a novel way to design, understand, and manufacture 3D architected materials designed from mathematically parameterized language input. Our work features, at its core, a generally applicable algorithm that transforms any 2D image data into hierarchical fully likable, periodic architected materials. This method can have broader applications beyond language-based materials design and can render other avenues for the analysis and manufacturing of architected materials, including microstructure gradients through parametric modeling. As an emerging field, language-based design approaches can have a profound impact on end-to-end design environments and drive a new understanding of physical phenomena that intersect directly with human language and creativity. It may also be used to exploit information mined from diverse and complex databases and data sources. (C) 2022 Author(s).
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据