An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems
npj Computational Materials, ISSN: 2057-3960, Vol: 9, Issue: 1
2023
- 8Citations
- 13Captures
- 1Mentions
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Northwestern University Researchers Have Provided New Data on Materials Research (An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems)
2023 JUL 07 (NewsRx) -- By a News Reporter-Staff News Editor at Tech Daily News -- Data detailed on materials research have been presented. According
Article Description
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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