A novel microstructure-informed machine learning framework for mechanical property evaluation of SiCf/Ti composites

Hao, Wenqi; Shi, Duoqi; Liu, Changqi; Fan, Yongsheng; Yang, Xiaoguang; Tan, Long; Zhang, Bo

doi:10.1016/j.jmrt.2023.11.289

Journal of Materials Research and Technology

2024

DOI: 10.1016/j.jmrt.2023.11.289

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A novel microstructure-informed machine learning framework for mechanical property evaluation of SiCf/Ti composites

Wenqi Hao,

Duoqi Shi,

Changqi Liu

et al.

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Cited by 4 publications

References 52 publications

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Thermal cycling performance of GYbZ/YSZ thermal barrier coatings with different microstructures based on finite element simulation

Jiang,

Hao,

Liu

et al. 2025

Journal of Alloys and Compounds

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Thermal cycling performance of GYbZ/YSZ thermal barrier coatings with different microstructures based on finite element simulation

Jiang,

Hao,

Liu

et al. 2025

Journal of Alloys and Compounds

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Predictive mechanical property and fracture behavior in high-carbon steel containing high-density carbides via artificial RVE modeling

Wang,

Qian,

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Materials & Design

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What can machine learning help with microstructure-informed materials modeling and design?

Peng,

Fathidoost,

Lin

et al. 2024

MRS Bulletin

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Machine learning (ML) techniques have been widely employed as effective tools in addressing various engineering challenges in recent years, particularly for the challenging task of microstructure-informed materials modeling. This work provides a comprehensive review of the current ML-assisted and data-driven advancements in this field, including microstructure characterization and reconstruction, multiscale simulation, correlations among process, microstructure, and properties, as well as microstructure optimization and inverse design. It outlines the achievements of existing research through best practices and suggests potential avenues for future investigations. Moreover, it prepares the readers with educative instructions of basic knowledge and an overview on ML, microstructure descriptors, and ML-assisted material modeling, lowering the interdisciplinary hurdles. It should help to stimulate and attract more research attention to the rapidly growing field of ML-based modeling and design of microstructured materials. Graphical abstract

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A novel microstructure-informed machine learning framework for mechanical property evaluation of SiCf/Ti composites

Cited by 4 publications

References 52 publications

Thermal cycling performance of GYbZ/YSZ thermal barrier coatings with different microstructures based on finite element simulation

Thermal cycling performance of GYbZ/YSZ thermal barrier coatings with different microstructures based on finite element simulation

Predictive mechanical property and fracture behavior in high-carbon steel containing high-density carbides via artificial RVE modeling

What can machine learning help with microstructure-informed materials modeling and design?

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