An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems

Yuwei Mao, Mahmudul Hasan, Arindam Paul, Vishu Gupta, Kamal Choudhary, Francesca Tavazza, Wei keng Liao, Alok Choudhary, Pinar Acar, Ankit Agrawal*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish (US)
Article number111
Journalnpj Computational Materials
Volume9
Issue number1
DOIs
StatePublished - Dec 2023

ASJC Scopus subject areas

  • Modeling and Simulation
  • General Materials Science
  • Mechanics of Materials
  • Computer Science Applications

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