Representation Of Microstructure Research Articles

Thermal Conductivity Analysis of Polymer‐Derived Nanocomposite via Image‐Based Structure Reconstruction, Computational Homogenization, and Machine Learning

Macroscopic thermal properties of engineered or inherent composites depend substantially on the composite structure and the interface characteristics. While it is acknowledged that unveiling such dependency relation is essential for materials design, the complexity involved in, e.g., microstructure representation and limited data impedes the research progress. Herein, this issue is tackled by machine learning techniques on image‐based microstructure and property data predicted from physics simulations, along with experimental validation. The methodology is demonstrated for the model system ultrahigh‐temperature ceramic nanocomposite. The structure is reconstructed from scanning electron microscope images, and is resolved by a diffuse‐interface representation, which is advantageous in handling complicated structure and interface properties. Subsequently, hierarchical finite element homogenization is carried out to evaluate the effective thermal conductivity. A thorough comparison between the computed results and experimentally measured data, conducted across diverse temperatures and varying interface thermal resistances, reveals a high level of agreement. The observed agreement allows for the inverse estimation of the interface thermal resistance, a parameter typically challenging to ascertain directly through experimental means. Utilizing comprehensive data, a machine learning surrogate model has been meticulously trained to accurately predict the effective thermal conductivity of composite structures with exceptional performance.

Periodic Composite Function-Based Approach for Designing Architected Materials With Programable Poisson’s Ratios

Abstract Advances in additive manufacturing enable fabrication of architected materials composed of microstructures with extreme mechanical properties. In the design of such architected materials, the parameterization of microstructures determines not just the computational cost but also connectivity between adjacent microstructures. In this paper, we propose a periodic composite function (PCF)-based approach for designing microstructures. The shape of the microstructures is characterized by the value of the periodic composite functions. The proposed method can program microstructures with both positive and negative Poisson’s ratios by a small number of parameters. Furthermore, due to its implicit representation, the proposed method allows for continuously tiling of microstructures with different mechanical properties. Explicit geometric features of the PCF-based microstructures are extracted, and the condition to maintain connectivity between adjacent microstructures is derived. Based on the proposed approach, multiple groups of 2D and 3D microstructures with Poisson’s ratios ranging from negative to positive are presented. Combining with a deep neural network (DNN)-based surrogate model to predict macroscopic material properties of the microstructures, the proposed method is applied to the design of architected materials for elastic deformation control. Numerical examples on both microstructure representation and architected materials design are presented to demonstrate the efficacy of the proposed approach.

Crystal plasticity modeling of deformation behavior of Al–Al2Cu eutectics based on high-fidelity representative microstructures

A comprehensive understanding of material deformation behavior holds importance in enhancing the properties of materials, which can be achieved through modeling the relationship between microstructure and deformation behavior by an appropriate crystal plasticity (CP) model. However, before simulations, obtaining a high-fidelity representative microstructure (RM) to simultaneously meet the efficiency and accuracy requirements of the CP model is still a challenge. Thus, taking the Al–Al2Cu eutectics as an example, we successfully obtained a high-fidelity RM with two-point statistics and some fashionable image processing techniques. Then a high-resolution RMCP model was developed and the deformation behavior of Al–Al2Cu eutectics at room temperatures was uncovered. Due to multiple activated slip systems on {111}<110> inducing obvious cross-slip behavior, severe and heterogenous plastic deformation occurs in α-Al. And dislocations originate from the microstructure defects. In θ-Al2Cu, all possible slip systems are not activated, resulting in no plastic deformation. The predictions of the RMCP model are verified to be reliable by comparing with the results of in-situ tensile tests. Our proposed RMCP method is not only applicable to the two-phase eutectic systems, but also suitable for various multiphase and polycrystalline systems.

The Influence of Tempering Parameters on the Microstructure, Mechanical Property, and the Corresponding Strengthening Mechanism of Metastable 301 Austenitic Stainless Steel

Metastable austenitic stainless steels (MASS) are widely used in various industrial applications due to their exceptional mechanical properties. The microstructure of MASS can be regulated through severe plastic deformation, heat treatments, and surface treatments to achieve further improvement of mechanical properties. Herein, nanograin and quasiheterostructure are prepared by cold rolling and annealing process in Fe–17Cr–6Ni MASS. The mechanical response, deformation mechanism, and strengthening contribution of the two steels with representative microstructure are studied. The results indicate that nanograin austenitic steel (803 MPa, 22%) has superior yield strength and similar ductility compared with that of quasiheterostructure steel (600 MPa, 25%). In the nanograin steel, the deformation mechanism is mainly dislocation slip and deformation‐induced martensite transformation, while in quasiheterostructure steel, the deformation twinning is also included for the relative lower stacking fault energy in the coarse grain. The yield strength of nanograin structural steel is about 200 MPa higher than that of quasiheterostructure steel, which can be attributed to the differences in grain refinement strengthening, phase‐transformation strengthening, and precipitation strengthening of Cu particles. The findings presented in this study are informative for modulating MASS properties and ultimately improving the lifespan in a variety of industrial settings.

Mesoscopic Model of Extrusion during Solvent‐Free Lithium‐ion Battery Electrode Manufacturing**

AbstractSolvent‐free (SF) manufacturing of lithium‐ion battery (LIB) electrodes is safer and more environmentally friendly than the traditional slurry casting approach. However, as a young technique, SF manufacturing is under development of its pathways and operation conditions. In different SF processes reported in literature, extrusion is a common step. A detailed model of this process would be extremely computationally demanding. This work proposes a novel simplified discrete element model at the mesoscopic scale for the extrusion during SF manufacturing of LIB electrodes. In addition to active material particles, we consider fluid‐like solid particles to approximate the molten polymer and the carbon additive phases. The formulation and other process parameters are taken from our experimental facility that uses extrusion to fabricate filaments for 3D printing of LIB cells. The extrusion is carried out in a conical twin screw extruder. Our approach allows to obtain representative electrode microstructures after extrusion, where electrical conductivity, ionic effective diffusivity, tortuosity factor and porosity are calculated. The model is a proof of concept that is employed to investigate the influence of the extruder speed and the cohesion level on the resulting electrode properties.

Stochastic Reconstruction of 3D Heterogeneous Microstructure Using a Column-Oriented Multiple-Point Statistics Program

Abstract Three-dimensional (3D) microstructure reconstruction is a key approach to exploring the relationship between pore characteristics and physical properties. Viewing the training image as a prior model, multiple-point statistics (MPS) focus on reproducing spatial patterns in the simulation grid. However, it is challenging to efficiently generate 3D nonstationary models with varying microstructures. In this work, we propose column-oriented simulation (ColSIM) to achieve the stochastic reconstruction of 3D porous media. A heterogeneous system is understood as a spatially evolving process that consists of frequent transitions of small magnitude and abrupt changes of large magnitude. First, a training image selection step is suggested to find representative microstructures. Our program applies modified Hausdorff distance, t-distributed stochastic neighboring embedding, and spectral clustering to organize two-dimensional (2D) candidate images. The medoid of each group is applied to guide the following programs. Second, we introduce column-oriented searching into MPS. To save simulation time, a subset of conditioning points is checked to find desired instances. Our program suggests an early stopping strategy to address complex microstructures. Third, a contrastive loss term is designed to create 3D models from 2D slice. To automatically calibrate the volume fraction and simplify parameter specification, the computer consistently monitors the difference between the present model and the target. The performance of ColSIM is examined by 3D multiphase material modeling and 3D heterogeneous shale simulation. To achieve quantitative evaluation, we compute various statistical functions and physical descriptors on simulated realizations. The proposed ColSIM exhibits competitive performance in terms of calculation efficiency, microstructure reproduction, and spatial uncertainty.

Multiscale finite element analysis model of needle-punched carbon/carbon composites

The mechanical properties of matrix, fiber bundle, and chopped fiber felt are evaluated by means of micromechanical analysis. An equivalent matrix model composed of matrix and chopped fiber felt material is established, which together with fiber bundle is used as the basic component of single cell. Based on the mesostructural characteristics of NP C/C composites, the local structure is divided into five typical unit cells to characterize the five representative microstructures of NP C/C composites, and the mechanical properties of the five typical unit cells are calculated, respectively. According to the true distribution of needle holes, the five typical unit cells are arranged to form a representative volume element model of NP C/C composites. The Cai–Wu failure criterion is used to determine the failure of the five typical unit cells model, and the periodic boundary condition is applied to the tension and compression simulation of the NP C/C composites, the progressive damage law of the model is analyzed. The results show that the prediction of mechanical properties of the composites under unidirectional tensile load and unidirectional compression load are in good agreement with the experimental data.

CRITICAL DISCOURSE ANALYSIS OF NORMAN FAIRCLOUGH ON ONLINE MEDIA REPORTING ABOUT THE ROHINGYA REFUGEE CRISIS

Critical Discourse Analysis (CDA) examines language as 'discourse,' where language is understood as one element within social processes that dialectically relate to each other. Critical discourse analysis precisely aims to explore these dialectical interconnections within the tradition of critical social science. In this research, the researcher will employ Norman Fairclough's model of critical discourse analysis, which examines discourse analysis through three theories: Text Analysis (Microstructural) related to cohesion and coherence, grammar, and diction; Production Practice Analysis (Mesostructural) dealing with the text production process, text dissemination, and text consumption, such as work patterns, workflow, and routines in news production; Social Cultural Practice Analysis (Macrostructural) related to situational, institutional, and social aspects. This study utilizes a qualitative descriptive method to deepen the critical analysis description of news discourse. The research data consists of words, phrases, or sentences in two online news discourses supporting the research problem. The data collection technique employed is documentary study. The news regarding the arrival of Rohingya refugees in Aceh published by VIVA.co.id becomes the focus of critical discourse analysis using Norman Fairclough's approach. This news reflects the text production process involving microstructural, mesostructural, and macrostructural aspects. In microstructural analysis, language use and representation are highlighted. Phrases like "coming to Indonesia using old boats" and quotes from netizens create negative stereotypes about Rohingya refugees. Social interactions are reflected in conflicts and societal concerns represented by these comments. In mesostructural analysis, the selection of topics, framing, and source determination becomes the focus. Choosing to report the Rohingya refugee issue with a predominantly negative framing reflects editorial production decisions influenced by preferences. Determining sources from netizens and UNHCR creates variations in perspectives. Macrostructural analysis highlights the social, political, and cultural values that shape the news. The Rohingya refugee crisis and global migration politics form the macro background influencing news production. Indonesian cultural values and ideology are reflected in the stigmatization of refugees. In conclusion, this critical discourse analysis reveals how text production practices, social interactions, and macrostructural contexts are interconnected. This news not only reflects internal language and media dynamics but is also tied to power, ideology, and cultural values within society. This analysis opens up space for a deeper understanding of narrative construction and its impact on public perceptions of Rohingya refugees in Indonesia. Keywords: Critical Discourse Analysis, Norman Fairclough, News.

Theoretical and Experimental Performance Characterization of Battery Anodes Comprised of Coal Derived Graphite

As demand for energy storage expands, securing reliable supply-chains for raw battery materials is critical in mitigating the threats of delayed manufacturing and inflated manufacturing costs. These issues have pushed researchers and industry to consider alternative sources for traditional lithium-ion battery materials, such as graphite. Commonly available sources of graphite for Li-ion anodes include natural graphite and petroleum coke derived synthetic graphite. While many carbon sources have been studied, most require complicated processing and result in sub-par graphite anode materials. This talk highlights raw coal and coal derived materials as promising graphite sources thanks to their abundance of sp2 hybridized carbon, resulting in simpler processing, and low material costs.We show that coal derived graphite can be implemented as a lithium-ion anode, however, many chemical, microstructural, and morphological considerations are necessary to compare with commercial graphite performance. We will experimentally compare the morphology at the microstructure for graphite from various carbon sources, and then apply the virtual microstructure analysis method as detailed by Lopata et al to quantify potential differences between the anode microstructures with respect to effective active material particle size (diffusion length calculation), the electrochemical surface area, and the tortuosity of the liquid phase domain. These calculations from representative 3D microstructures can then be extended to a porous electrode model as detailed in Garrick et al to probe the impact in ionic, kinetic, and ohmic losses in the system.References Joseph S. Lopata et al 2023 J. Electrochem. Soc. 170 020530Taylor R. Garrick et al 2021 J. Electrochem. Soc. 168 010530

Triple junction disclinations in severely deformed Cu–0.4%Mg alloys

Stress fields arising from triple junction disclinations (TJDs) play a significant role in the microstructure evolution during the plastic deformation of metals. The calculation of TJD strengths from grain orientation data was developed by Bollmann more than 50 years ago, but so far applied only to collections of a few grains. Developed here is a new methodology for calculating TJD strengths and the associated stress fields in large polycrystalline assemblies using experimental electron back-scattered diffraction (EBSD) maps. The methodology combines Bollmann’s approach with a representation of materials as cell complexes. It is computationally efficient and allows for obtaining the spatial distribution of TJD strengths from EBSD images containing thousands of grains. Analysed are the fraction, distribution, and strengths of TJDs within statistically representative microstructures of Cu–0.4%Mg alloy subjected to severe plastic deformation (SPD) by equal channel angular pressing. It is shown that the formation of low-angle grain boundaries (dislocation walls) during SPD leads to an increasing number of TJDs, whose spatial distribution is progressively more uniform and whose strength distribution remains nearly constant. This result suggests that the SPD reduces the internal stresses associated with disclinations in large regions of the material, as closely situated disclinations screen each other’s fields. Regions with high local stresses can be expected between sparsely distributed TJDs with the highest strengths. The average distance between such TJDs could be considered as a natural length scale in a material.

Comparison of three algorithms generating virtual microstructures in terms of the degree of randomness

The degree of randomness of fibre arrangements within the resin area is of great importance to the composites’ micro-distribution of stress and strain. A new algorithm, hard-core model & random shaking model, is developed to generate the unidirectional continuous fibre-reinforced composites. Another two algorithms, the random sequential expansion model and the initially periodic shaking model are presented and improved to generate the representative volume element microstructures. Statistical analyses are performed to compare these representative volume element microstructures generated by different algorithms at different fibre volume fractions. A quantitative approach is first applied to provide the exact degree of randomness of both virtual and real microstructures. Results of qualitative and quantitative analyses show that the novel algorithm is capable of generating statistically equivalent fibre distributions to real continuous fibre-reinforced composites.

A novel lattice model to predict chloride diffusion coefficient of unsaturated cementitious materials based on multi-typed pore structure characteristics

This paper develops a novel lattice diffusive model to quantitatively study the chloride diffusion coefficient in unsaturated cementitious materials, in which the pore voxels are redistributed to make a better representation of a real microstructure of hardened cement paste. Considering the hierarchical microstructure and different drying-wetting cycles, water distributions in multiscale pore structures are modelled and the structure characteristics of water-filled pores, including water connectivity, water tortuosity and effective porosity, are computationally extracted based on that. A lattice diffusion network is established to predict relative chloride diffusion coefficient by combining the effect of both water saturation degree and pore structure characteristics. The predicted results are validated against experimental data, and a concise analytical equation is proposed to predict the relative chloride diffusion coefficient. The equation indicated that the relative chloride diffusion coefficient is proportional to water connectivity but inversely proportional to the square of water tortuosity. Besides, the lattice model's quantitative results reveal that the water connectivity and water tortuosity are highly related to pre-water loading processes, and influenced by the gel pore fraction, which in turn will affect the relative chloride diffusion coefficient. Compared with existing equations and non-redistributed models, the present model could improve the prediction accuracy significantly.

Digital polycrystalline microstructure generation using diffusion probabilistic models

Accurate micromechanical simulation of polycrystalline materials requires a realistic digital representation of the grain scale microstructure. This work demonstrates the use of a generative diffusion probabilistic model for synthesizing single phase polycrystalline realizations. The model performs well and is capable of producing realistic microstructures consisting of not just simple equiaxed structures but also structures exhibiting more complex spatial arrangements. Masked microstructure generation reveals that the model is context aware of morphological descriptors which may be encoded in the latent space. Training on more diverse data sets, with scaled up architectures, may enable development of future models capable of synthesizing even more complex microstructural features.

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

In this work, a novel microstructure-informed machine learning (MIML) framework is proposed for the longitudinal ultimate tensile strength (UTS) evaluation and the construction of microstructure-property linkages of titanium matrix composites (TMCs). The main components of the MIML framework include (i) high-fidelity simulation and dataset generation using finite element method (FEM), (ii) the quantitative characterization of microstructures obtained by 2-point cross-correlation analysis and principal component analysis, (iii) the extraction of structure-property linkages based on support vector regression model and (iv) the validation of MIML by experimental data. The fiber breakage observed by advanced microstructure characterization is considered as the dominant failure mode during mechanical tests of TMCs, which is embedded in the FEM simulations. The MIML approach reaches capable accuracy in UTS prediction of TMCs using FEM-generated datasets and trained MIML is validated by experimental data. In addition, the permutation importance-based method is utilized to accurately identify the key feature parameters related to UTS. The first two principal components of microstructural reduced-order representation are recognized as highly relevant features for UTS. The well-trained MIML model is a promising tool in the evaluation of the mechanical property, and further provides a technical chain for the establishment of microstructure-property linkages of multi-phase composites.

Trade-offs in the latent representation of microstructure evolution

Characterizing and quantifying microstructure evolution is critical to forming quantitative relationships between material processing conditions, resulting microstructure, and observed properties. Machine-learning methods are increasingly accelerating the development of these relationships by treating microstructure evolution as a pattern recognition problem, discovering relationships explicitly or implicitly. These methods often rely on identifying low-dimensional microstructural fingerprints as latent variables. However, using inappropriate latent variables can lead to challenges in learning meaningful relationships. In this work, we survey and discuss the ability of various linear and nonlinear dimensionality reduction methods including principal component analysis, autoencoders, and diffusion maps to quantify and characterize the learned latent space microstructural representations and their time evolution. We characterize latent spaces by their ability to represent high-dimensional microstructural data in terms of compression achieved as a function of the number of latent dimensions required to represent the data accurately, their accuracy based on their reconstruction performance, and the smoothness of the microstructural trajectories in latent dimension. We quantify these metrics for common microstructure evolution problems in material science including spinodal decomposition of a binary metallic alloy, thin film deposition of a binary metallic alloy, dendritic growth, and grain growth in a polycrystal. This study provides considerations and guidelines for choosing dimensionality reduction methods when considering materials problems that involve high dimensional data and a variety of features over a range of lengths and time scales.

Probing the varying ranges of damping capacity of magnesium alloys containing long-period stacking ordered phases

Traditional high damping Mg alloys, based on the concept of purified matrix, cannot withstand large vibrational strain, inhibiting their practical use. The current study employed Mg alloys containing long-period stacking ordered (LPSO) phase and designed representative microstructures, to probe the varying ranges of damping capacity. It is found that intragranular LPSO lamellae effectively purify the matrix without hindering dislocation vibrations, hence contribute to improving damping capacity at small strain. They also provide additional boost to damping capacity at intermediate strain. Precipitates, as matrix purifiers, are detrimental to damping capacity. Quantitative analyses of damping curves and the correlation with stress-strain data reveal that the transition to the rapidly rising part on damping curves physically corresponds to plastic yielding. This further inspired two perspectives of viewing damping capacity at the characteristic strain values of different materials, representing a paradigm shift of material selection and providing a rational criterion for future alloy development.

Peridynamic modeling of the micromechanical response of snow under high strain rates

Modeling the mechanical response of snow is a challenge due to the wide range of properties and conditions that impact the mechanics, such as temperature, loading conditions, and the geometry of the microstructure. Current modeling approaches including the finite element and discrete element methods have made great strides in modeling the micromechanical behavior of snow, but they often make simplifications to the microstructure geometry and failure process. However, it has been shown that the geometry of individual snow grains and the sintered bonds that form between them, as well as the failure of these bonds, are important factors in the micromechanical behavior of snow. We investigate a modeling framework called peridynamics that has not been applied to snow problems previously, but has been successfully applied to material fracture problems. This approach can use segmented voxels from μCT scans of snow as the model input geometry, which allows for highly accurate representations of realistic snow microstructures within the model. We provide the first analyses to determine appropriate simulation domain sizes and the domain resolutions required to capture realistic snow behavior with peridynamics. We show that the approach is able to simulate realistic brittle behavior for snow at very high strain rates (≥0.1s−1). The model naturally produces highly damaged regions within the sintered bond regions of the microstructure without any preconditioning. We then discuss next steps for further validating peridynamics for snow applications.

Comparison of Grain-Growth Mean-Field Models Regarding Predicted Grain Size Distributions.

Mean-field models have the ability to predict the evolution of grain size distribution that occurs through thermomechanical solicitations. This article focuses on a comparison of mean-field models under grain-growth conditions. Different microstructure representations are considered and discussed, especially regarding the consideration of topology in the neighborhood construction. Experimental data obtained with a heat treatment campaign on 316L austenitic stainless steel are used for the identification of material parameters and as a reference for model comparisons. Mean-field models are also applied to both mono- and bimodal initial grain size distributions to investigate the potential benefits of introducing neighborhood topology in microstructure prediction models. This article demonstrates that improvements in the predictions can be obtained in monomodal cases for topological models. In the bimodal test, no comparison with experimental data was performed as no data were available. But relative comparisons between models indicated few differences in the predictions. Although of interest, the consideration of neighborhood topology in grain-growth mean-field models generally results in only small improvements compared to classical mean-field models, especially in terms of implementation complexity.

A deep adversarial approach for the generation of synthetic titanium alloy microstructures with limited training data

Microstructures play a central role in determining the mechanical and functional properties of a material. An important aspect in computational materials science is to reliably predict the key microstructural features, and to utilise them in bridging the processing and properties of new materials. The existing microstructure characterization and reconstruction (MCR) techniques have inherent limitations in terms of the lack of design variables and information loss due to various assumptions. In previous studies, Generative Adversarial Network (GAN) models were trained using more than 10,000 image datasets, however without performance analysis using quantitative morphometric and statistical measures. In this perspective, the present work demonstrates the capability of GAN architectures to learn the mapping between random latent vectors and synthetic microstructural images, even in a limited data regime (1225 images). Three different architectures Deep Convolutional GAN (DCGAN) ,Wasserstein GAN- Gradient Penalty (WGAN-GP), StyleGAN2- Adaptive Discriminator Augmentation (ADA) were explored, together with comprehensive statistical and morphological analysis, while training on a publicly accessible Ti-6Al-4V (Ti64) alloy microstructure dataset. The StyleGAN2-ADA outperforms the other two GAN models to generate realistic synthetic microstructures of higher resolution, with good qualitative similarity to original images. The analysis of performance metrics, like Frechet Inception Distance (FID), Kernel Inception Distance (KID), and Inception Score (IS) scores, reveal that the generated image distribution is statistically close to the original distribution of microstructural features. The morphometric parameters, including α/β phase fractions, local α/β boundary thickness, and orientation of lamellar morphology, were also used to compare original and synthetic images quantitatively. More importantly, two-point correlation and t-stochastic neighbour embedding (t-SNE) illustrate the statistical similarity between the original and synthetic microstructures. Taken together, the present work establishes the capability of generative models like GAN in generating representative microstructures of Titanium alloy in a statistically reliable manner. Such an approach, when adopted, will accelerate the field of microstructure fingerprinting.

Evaluation of neural network attribution methods for microstructural representation of aero-engine material

Efficiency and accuracy of evaluation for the key to microstructure parameters in aero-engine materials is crucial for understanding the properties and performance. However, it is still a tough research topic to identify the important microstructural variables using conventional methods. In the present work, attribution (variable importance evaluation) methods based on neural networks have been systematically sorted out with a comprehensive understanding of the strengths and limitations. Microstructures of nickel base single-crystal alloys is taken as an example for discuss the variable importance evaluation methods, namely forward stepwise, backward stepwise, and partial derivative. Suggestions are provided for future application in material science and solid mechanics.