微观结构
材料科学
微晶
航空航天
机械工程
方向(向量空间)
工作(物理)
计算科学
计算机科学
复合材料
几何学
冶金
数学
航空航天工程
工程类
作者
Yuwei Mao,Masud Hasan,Arindam Paul,Vishu Gupta,Kamal Choudhary,Francesca Tavazza,Wei-keng Liao,Alok Choudhary,Pınar Acar,Ankit Agrawal
标识
DOI:10.1038/s41524-023-01067-8
摘要
Abstract Materials design aims to identify the material features that provide optimal properties for various engineering applications, such as aerospace, automotive, and naval. One of the important but challenging problems for materials design is to discover multiple polycrystalline microstructures with optimal properties. This paper proposes an end-to-end artificial intelligence (AI)-driven microstructure optimization framework for elastic properties of materials. In this work, the microstructure is represented by the Orientation Distribution Function (ODF) that determines the volume densities of crystallographic orientations. The framework was evaluated on two crystal systems, cubic and hexagonal, for Titanium (Ti) in Joint Automated Repository for Various Integrated Simulations (JARVIS) database and is expected to be widely applicable for materials with multiple crystal systems. The proposed framework can discover multiple polycrystalline microstructures without compromising the optimal property values and saving significant computational time.
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