An efficient multi-scale model for needle-punched Cf/SiCm composite materials with experimental validation

Lim, Hyoung Jun; Choi, Hoil; Lee, Min Jung; Yun, Gun Jin

doi:10.1016/j.compositesb.2021.108890

Cited by 22 publications

(5 citation statements)

References 32 publications

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“…Nondestructive testing technology, particularly industrial computed tomography (CT), holds significance in detecting internal defects and precisely measuring internal structures [6][7][8][9][10][11][12]. Schock et al [13] suggested a combination of high-resolution dual-energy X-ray micro-CT with subsequent advanced image processing steps to derive characteristic parameters for describing real porous systems. Liu et al [14] proposed the reconstruction of a microscopic heat transfer model through CT images to investigate the interaction between coal temperature rise fracturing and coal heat transfer performance, introducing a novel approach for efficient heat injection mining of coal seams.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation of fluid flow behavior in steel cord with lattice Boltzmann methodology: The impacts of microstructure and loading force

Zhao,

Jin,

Fan

et al. 2024

PLoS ONE

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Steel cord materials were found to have internal porous microstructures and complex fluid flow properties. However, current studies have rarely reported the transport behavior of steel cord materials from a microscopic viewpoint. The computed tomography (CT) scanning technology and lattice Boltzmann method (LBM) were used in this study to reconstruct and compare the real three-dimensional (3D) pore structures and fluid flow in the original and tensile (by loading 800 N force) steel cord samples. The pore-scale LBM results showed that fluid velocities increased as displacement differential pressure increased in both the original and tensile steel cord samples, but with two different critical values of 3.3273 Pa and 2.6122 Pa, respectively. The original steel cord sample had higher maximal and average seepage velocities at the 1/2 sections of 3D construction images than the tensile steel cord sample. These phenomena should be attributed to the fact that when the original steel cord sample was stretched, its porosity decreased, pore radius increased, flow channel connectivity improved, and thus flow velocity increased. Moreover, when the internal porosity of tensile steel cord sample was increased by 1 time, lead the maximum velocity to increase by 1.52 times, and the average velocity was increased by 1.66 times. Furthermore, when the density range was determined to be 0–38, the pore phase showed the best consistency with the segmentation area. Depending on the Zou-He Boundary and Regularized Boundary, the relative error of simulated average velocities was only 0.2602 percent.

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Section: Introductionmentioning

confidence: 99%

Numerical investigation of fluid flow behavior in steel cord with lattice Boltzmann methodology: The impacts of microstructure and loading force

Zhao,

Jin,

Fan

et al. 2024

PLoS ONE

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“…Among various experimental inspection technologies for microstructure characterization for RVE definition (such as optical microscopy, scanning electron microscopy (SEM) [23], transmission electron microscopy (TEM), and X-ray diffraction (XRD) analysis [24]), micro-computed tomography (micro-CT) is the most powerful tool that allows for visualizing the internal structure of a material in a 3D domain. It has been used to identify the constituents in the microstructure, including the shape, size, and distribution of the matrix and reinforcement and any defects or discontinuities present in the material [25]. In addition, it is beneficial for analyzing the behavior of composite structures with 3D FEM.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Framework for Designing Microstructure of Multifunctional Composites with Deep-Learned Diffusion-Based Generative Models

Lee

Lim

Yun

2023

Preprint

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This paper puts forward a novel integrated microstructure design methodology that replaces the common existing design approaches for multifunctional composites: 1) reconstruction of microstructures, 2) analyzing and quantifying material properties, and 3) inverse design of materials using the diffusion-based generative model (DGM). The problem of microstructure reconstruction is addressed using DGM, which is a new state-of-the-art generative model formulated with a forward Markovian diffusion process and the reverse process. Then, the conditional formulation of DGM is introduced for guidance to the embedded desired material properties with a transformer-based attention mechanism, which enables the inverse design of multifunctional composites. A convolutional neural network (CNN)-based surrogate model is utilized to facilitate the prediction of nonlinear material properties for building microstructure-property linkages. Combined, the proposed artificial intelligence-based design framework enables large data processing and database construction that is often not affordable with resource-intensive finite element method (FEM)-based direct numerical simulation (DNS) and iterative reconstruction methods. What is important is that the proposed DGM-based methodology is not susceptible to unstable training or mode collapse, which are common issues in neural network models that are often difficult to address even with extensive hyperparameter tuning. An example case is presented to demonstrate the effectiveness of the proposed approach, which is designing mechanoluminescence (ML) particulate composites made of europium and dysprosium ions. The results show that the inversely-designed multiple ML microstructure candidates with the proposed generative and surrogate models meet the multiple design requirements (e.g., volume fraction, elastic constant, and light sensitivity). The evaluation of the generated samples' quality and the surrogate models' performance using appropriate metrics are also included. This assessment demonstrates that the proposed integrated methodology offers an end-to-end solution for practical material design applications.

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“…Moreover, the use of micro-CT-based characterization of microstructures has been regarded as a reliable approach to understand material behavior with computational analysis, such as finite element analysis (FEA), particularly at the representative volume element (RVE) scale 9,[12][13][14][15][16] .…”

Section: Introductionmentioning

confidence: 99%

“…In order to acquire precise 3D information about microstructures, micro-computed tomography (micro-CT) has demonstrated its effectiveness as a tool allowing for the visualization of a material's internal structure and potential defects in a 3D domain. [9][10][11][12] .…”

Section: Introductionmentioning

confidence: 99%

Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling

Yun,

Lee

2023

Preprint

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Acquiring reliable microstructure datasets is a pivotal step toward the systematic design of materials with the aid of integrated computational materials engineering (ICME) approaches. However, obtaining three-dimensional (3D) microstructure datasets is often challenging due to high experimental costs or technical limitations, while acquiring two-dimensional (2D) micrographs is comparatively easier. To deal with this issue, this study proposes a novel framework for 2D-to-3D reconstruction of microstructures called ‘Micro3Diff’ using diffusion-based generative models (DGMs). Specifically, this approach solely requires pre-trained DGMs for the generation of 2D samples, and dimensionality expansion (2D-to-3D) takes place only during the generation process (i.e., reverse diffusion process). The proposed framework incorporates a new concept referred to as ‘multi-plane denoising diffusion’, which transforms noisy samples (i.e., latent variables) from different planes into the data structure while maintaining spatial connectivity in 3D space. Furthermore, a harmonized sampling process is developed to address possible deviations from the reverse Markov chain of DGMs during the dimensionality expansion. Combined, we demonstrate the feasibility of Micro3Diff in reconstructing 3D samples with connected slices that maintain morphologically equivalence to the original 2D images. To validate the performance of Micro3Diff, various types of microstructures (synthetic and experimentally observed) are reconstructed, and the quality of the generated samples is assessed both qualitatively and quantitatively. The successful reconstruction outcomes inspire the potential utilization of Micro3Diff in upcoming ICME applications while achieving a breakthrough in comprehending and manipulating the latent space of DGMs

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An efficient multi-scale model for needle-punched Cf/SiCm composite materials with experimental validation

Cited by 22 publications

References 32 publications

Numerical investigation of fluid flow behavior in steel cord with lattice Boltzmann methodology: The impacts of microstructure and loading force

Numerical investigation of fluid flow behavior in steel cord with lattice Boltzmann methodology: The impacts of microstructure and loading force

A Data-Driven Framework for Designing Microstructure of Multifunctional Composites with Deep-Learned Diffusion-Based Generative Models

Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling

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