Establishing the interfacial microstructure-behavior relations in composites via stochastic morphology reconstruction and deep learning

Xu, Nuo; Chen, Shaohua

doi:10.1016/j.actamat.2022.118582

Cited by 6 publications

(1 citation statement)

References 52 publications

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“…Due to the complexity of its hierarchical structure, the successful design of SiC f /SiC component requires a clear understanding of the relation between mechanical behavior and microstructures. It has been recognized that the mechanical behavior of CMC is highly dependent on the fiber/matrix interface, which is responsible for load transfer, fiber protection, and crack energy absorption, to name just a few [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Shear behavior of SiC_f/SiC interface under the thermo-chemo-mechanical influence and machine-learning-based interfacial microstructure design

Chen

2023

Modelling Simul. Mater. Sci. Eng.

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The mechanical behavior of composite interface can be influenced by multiple factors, including the morphological roughness, the structure of coating interphase, and the temperature. Here, high-throughput molecular dynamics (MD) simulations are carried out to investigate the entangled effects of these factors on the shear stiffness G, the friction coefficient μ, the debonding strain ϵ_d and stress τ_d, of SiCf/SiC interface. We find that G is maximized by small roughness and high temperature for the optimal chemical bonding effect; μ and ϵ_d are maximized by large roughness and low temperature, taking advantage of the mechanical interlocking effect while avoiding cusp softening; τ_d demonstrates two local maxima which result from the competition between chemical bonding and mechanical interlocking. Provided the MD simulation results, a variational autoencoder (VAE) model is proposed to design the microstructure of SiCf/SiC interface for desired shear properties. According to the validations, the VAE-predicted interfacial configuration demonstrates highly similar shear properties to the reference one, justifying its potential for the microstructure design of composite interface. The results of this work can be employed to facilitate the development of SiCf/SiC composite by taking advantage of the synergistic effects of multiple designable factors.

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Section: Introductionmentioning

confidence: 99%

Shear behavior of SiC_f/SiC interface under the thermo-chemo-mechanical influence and machine-learning-based interfacial microstructure design

Chen

2023

Modelling Simul. Mater. Sci. Eng.

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Recent Research on the Optimization of Interfacial Structure and Interfacial Interaction Mechanisms of Metal Matrix Composites: A Review

Zhong,

Jiang,

Sun

et al. 2024

Adv Eng Mater

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Interfaces in metal matrix composites (MMCs) are crucial in determining the mechanical properties and functionality of MMCs. This article reviews recent scientific advances and issues related to MMC interfaces from four different perspectives: interfacial structural design, interfacial defect control, thermodynamics and kinetics of interfacial reactions, and interfacial microstructure properties‐based numerical simulation. The influence of interfacial microstructural features on interfacial strength–toughness trade‐offs and their functionality is given special attention. The interfacial problem is still one of the main challenges to be solved in the composite system, which is mainly reflected in the following aspects: interfacial microstructure–deformation mechanisms in complex systems, precise control of interfacial defects and interfacial reaction products, and the efficient and cost‐effective application of numerical simulation techniques. The integrated computation and intelligent design (artificial intelligence) of composites through multiscale computational simulation with the guidance of “interface design‐property prediction” will be the key area of MMC interface research in the future.

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A machine learning method of accelerating multiscale analysis for spatially varying microstructures

Li,

Hou

2024

International Journal of Mechanical Sciences

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Establishing the interfacial microstructure-behavior relations in composites via stochastic morphology reconstruction and deep learning

Cited by 6 publications

References 52 publications

Shear behavior of SiC_f/SiC interface under the thermo-chemo-mechanical influence and machine-learning-based interfacial microstructure design

Shear behavior of SiC_f/SiC interface under the thermo-chemo-mechanical influence and machine-learning-based interfacial microstructure design

Recent Research on the Optimization of Interfacial Structure and Interfacial Interaction Mechanisms of Metal Matrix Composites: A Review

A machine learning method of accelerating multiscale analysis for spatially varying microstructures

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Establishing the interfacial microstructure-behavior relations in composites via stochastic morphology reconstruction and deep learning

Cited by 6 publications

References 52 publications

Shear behavior of SiCf/SiC interface under the thermo-chemo-mechanical influence and machine-learning-based interfacial microstructure design

Shear behavior of SiCf/SiC interface under the thermo-chemo-mechanical influence and machine-learning-based interfacial microstructure design

Recent Research on the Optimization of Interfacial Structure and Interfacial Interaction Mechanisms of Metal Matrix Composites: A Review

A machine learning method of accelerating multiscale analysis for spatially varying microstructures

Contact Info

Product

Resources

About

Shear behavior of SiC_f/SiC interface under the thermo-chemo-mechanical influence and machine-learning-based interfacial microstructure design

Shear behavior of SiC_f/SiC interface under the thermo-chemo-mechanical influence and machine-learning-based interfacial microstructure design