Actual Microstructure Research Articles

Damage Quantification via Digital Volume Correlation with Heterogeneous Mechanical Regularization: Application to an In situ Meso-Flexural Test on Mortar

The mechanical response and damage mechanisms of heterogeneous cementitious materials result from the mechanical properties of their constituents at the mesoscale. In this study, a finite element based Digital Volume Correlation (FE-DVC) method with mechanical regularization tailored for heterogeneous materials was applied to study crack propagation in mortar. A realistic 3D mesh was built consistently with the actual microstructure based on high resolution X-ray tomography. A notched mesobeam was subjected to in situ three-point flexure. The introduction of mechanical contrast in DVC analyses allowed the strain heterogeneities related to the underlying microstructure to be described and inelastic strain localization due to cracks to be captured. Furthermore, the relaxation of mechanical regularization in damaged elements improved the trustworthiness of displacement measurements. Estimations of crack opening displacements and characterization of the crack morphology were carried out, providing insights into the damage processes at the mesoscale.

Properties of a powder metallurgy low-alloy steel with varying fractions of large iron particles

ABSTRACT Interiors of coarse iron particles in blends used in powder metallurgy may not be adequately alloyed during sintering. Relatively low concentrations of alloying elements reduce the hardenability of these regions, which may not transform to martensite during quenching. Since an actual microstructure (martensite, pores and pearlite) may differ from the desired microstructure (martensite and voids), it is reasonable to speculate that large and relatively soft pearlitic inclusions may worsen mechanical properties. This conjecture was tested, and it was experimentally found that unless the fraction of coarse particles exceeded a certain threshold, the properties of a medium-density powder metallurgy steel did not vitiate significantly. It also was observed that when the fraction of large particles dropped below a certain limit, apparent density, compressibility and flowability degraded to an extent rendering the corresponding mixtures unusable.A contradiction between a visual perception and a verdict of a statistical analysis is commented upon.

Understanding the Impact of Microstructure on Charge Transport in Polycrystalline Materials Through Impedance Modelling

Charge transport in polycrystalline electronic or ionic conductors is usually analyzed by serial macroscopic equivalent circuits, e.g., the brick layer model, which assume a homogeneous electric potential distribution across the sample. In such analyses, the microstructure is highly idealized and usually not representative of the actual microstructure. Here, we use a network model approach to investigate the impact of the sample’s microstructure on the impedance. We find that this influence can be severe and should not be ignored. The interplay between microscopic transport paths affects the impedance response, which is reflected in both the frequency and the time domain. Especially in the distribution of relaxation times additional signals are identified and studied systematically. These additional contributions cannot be assigned to a microscopic transport process as usually done in a conventional analysis based on an equivalent circuit model fitted to the impedance data. The neglect of the peculiarities of the real microstructure in impedance analyses based on the brick layer model may yield deviations in the order of 100 % in terms of the derived microscopic transport parameters. The microstructures used as input for the modelling are digitalized electron microscope images of polycrystalline samples.

Geometry-considered 3D pseudorandom grain-scale modelling for crystalline material miniature parts

Grain-scale modelling and simulation is of vital importance during the full lifecycle of crystalline material miniature parts, whose mechanical responses depend on the actual material microstructure, part geometry and their correlation. Unfortunately, current studies fail to comprehensively consider indispensable microstructure information, and lack the consideration regarding part geometry. Therefore, this paper proposes and implements a geometry-considered 3D pseudorandom grain-scale modelling method, which takes into account the necessary actual microstructure information, part geometry and their mutual relations to generate accurate and efficient microstructure-dependent crystal plasticity finite element (M−CPFE) models. Firstly, the 3D actual grain-scale microstructure was acquired efficiently by combining 2D actual microstructures. Then the geometry-considered grain-scale microstructure was created pseudorandomly based on the actual 3D grain size, orientation and misorientation distributions. On this basis, the M−CPFE model was constructed accurately. The proposed method has been proved to have higher accuracy and robustness than the Voronoi random method on macroscopic response prediction, microstructure generation and microstructure evolution prediction. Furthermore, the applicability in micro-manufacturing and service has been demonstrated by micro sheet bending and micro impeller rotation simulations. There is a great potential for the full lifecycle tracking of crystalline material miniature parts with the help of the proposed method.

Characterisation of damage evolution in plain weave SiC/SiC composites using in situ X-ray micro-computed tomography

The paper presents the investigation of microstructures and tensile damage evolution of plain weave SiC fibre reinforced SiC matrix (SiC/SiC) composites using X-ray computed tomography (CT). A miniature high-precision in situ X-ray CT test fixture has been developed to conduct micro X-ray CT in-situ tensile tests. Through the image reconstruction of the CT data, the actual microstructures and the damage evolution of the material under six successive loading levels have been revealed precisely. Deep-learning-based image segmentation has been employed to recognise material damages, including transverse matrix cracking, longitudinal matrix cracking, and fibre pull-outs. Three-dimensional rendering models of these damages have been created to quantify the damage initiation and evolution. Both transverse and longitudinal matrix cracking initiates and propagates gradually at the intermediate loading level, but rapidly at the high loading level. The volume fraction of longitudinal matrix cracks is much larger than that of its transverse counterpart. It indicates that the longitudinal matrix cracking is much severer damage than transverse tracking. The lengths of fibre pull-outs at the fracture surface of fibre tows are in a broad range and on average they are in the order of a hundred microns.

Experimental Characterization of Short Fiber-Reinforced Composites on the Mesoscale by Indentation Tests

Indentation tests are widely used to characterize the material properties of heterogeneous materials. So far there is no explicit analysis of the spatially distributed material properties for short fiber-reinforced composites on the mesoscale as well as a determination of the effective cross-section that is characterized by the obtained measurement results. Hence, the primary objective of this study is the characterization of short fiber-reinforced composites on the mesoscale. Furthermore, it is of interest to determine the corresponding area for which the obtained material parameters are valid. For the experimental investigation of local material properties of short fiber-reinforced composites, the Young’s modulus is obtained by indentation tests. The measured values of the Young’s modulus are compared to results gained by numerical simulation. The numerical model represents an actual microstructure derived from a micrograph of the used material. The analysis of the short fiber-reinforced material by indentation tests reveals the layered structure of the specimen induced by the injection molding process and the oriented material properties of the reinforced material are observed. In addition, the experimentally obtained values for Young’s modulus meet the results of a corresponding numerical analysis. Finally, it is shown, that the area characterized by the indentation test is 25 times larger than the actual projected area of the indentation tip. This leads to the conclusion that indentation tests are an appropriate tool to characterize short fiber-reinforced material on the mesoscale.

Multiscale numerical investigation on failure behaviour of three-dimensional orthogonal woven carbon/carbon composites subjected to pin-loading

On the basis of the experimental results in previous work, a multiscale numerical modeling strategy on the failure behaviour of three-dimensional orthogonal woven carbon/carbon composites under pin-loading was presented. In consideration of the complexity of internal woven architecture, the finite element analysis at micro-/meso-/macro-scale levels was performed to exhibit the effective material properties and mechanical behaviour. The geometric models were reconstructed via X-ray tomography technology to obtain feasible configurations based on actual microstructure, meanwhile the models considered the fibers random arrangement in the yarn and voids stochastic distribution in the matrix. The anisotropic composites damage evolution was characterized by Murakami-Ohno damage theory. Additionally, for a further exploration on the practical bearing failure mode, the macro-scale open-hole plate model was established using mesh superposition method to expose the damage mechanism of each component in composites at hole edge, and the numerical predictions agreed reasonably well with the experimental results.

Analysis of variant-pairing tendencies in lenticular martensite microstructures based on rank-1 connection

Herein, variant-pairing tendencies of lenticular martensite in an Fe–30Ni–0.3C (wt%) alloy are analyzed based on rank-1 connection at martensite/martensite junction planes (JPs) to facilitate the understanding of martensite microstructure. The degree of incompatibility (θ) at the JPs successfully explained their observed frequency; in the actual microstructure, variant pairs with a small θ form preferentially. The experimentally obtained JPs were consistent with theoretical ones. To the best of our knowledge, this is the first study to confirm the validity of variant-pair crystallography in steel based on rank-1 connection, both theoretically and experimentally. Diamond, composite-spear, and composite-kink clusters are considered. The cumulative θ at the JPs can suppress diamond cluster formation because it exceeds the θ of a single variant pair, and the diamond cluster is not observed experimentally. However, θ at the JPs cancel out in composite-spear (CS) and composite-kink (CK) clusters, but CK clusters are rarely observed experimentally, while a few CS clusters are observed. This demonstrates the analytical limitations of 2D approaches used to evaluate the frequency of variant pairs and clusters. These two variant clusters have a narrow window of 2D observation because the orientation relationships between JPs and intersection lines between two habit planes affect the areas of JPs.

Machine Learning Based Methods for Obtaining Correlations between Microstructures and Thermal Stresses

The microstructure–property relationship is critical for parts made using the emerging additive manufacturing process where highly localized cooling rates bestow spatially varying microstructures in the material. Typically, large temperature gradients during the build stage are known to result in significant thermally induced residual stresses in parts made using the process. Such stresses are influenced by the underlying local microstructures. Given the extensive range of variations in microstructures, it is useful to have an efficient method that can detect and quantify cause and effect. In this work, an efficient workflow within the machine learning (ML) framework for establishing microstructure–thermal stress correlations is presented. While synthetic microstructures and simulated properties were used for demonstration, the methodology may equally be applied to actual microstructures and associated measured properties. The dataset for ML consisted of images of synthetic microstructures along with thermal stress tensor fields simulated using a finite element (FE) model. The FE model considered various grain morphologies, crystallographic orientations, anisotropic elasticity and anisotropic thermal expansion. The overall workflow was divided into two parts. In the first part, image classification and clustering were performed for a sanity test of data. Accuracies of 97.33% and 99.83% were achieved using the ML based method of classification and clustering, respectively. In the second part of the work, convolution neural network model (CNN) was used to correlate the microstructures against various components and measures of stress. The target vectors of stresses consisted of individual components of stress tensor, principal stresses and hydrostatic stress. The model was able to show a consistent correlation between various morphologies and components of thermal stress. The overall predictions by the model for all the microstructures resulted into R2≈0.96 for all the stresses. Such a correlation may be used for finding a range of microstructures associated with lower amounts of thermally induced stresses. This would allow the choice of suitable process parameters that can ensure that the desired microstructures are obtained, provided the relationship between those parameters and microstructures are also known.

Realistic Rendering in “Details”

Rendering is far from solved. Even today, the rendered results still look artificial and overly perfect. To make rendering more realistic, we need details. However, rendering a complex surface with lots of details is far from easy. Traditionally, the surface microstructure is approximated using a smooth statistical distribution, but this ignores all the details on the surface, completely eliminating the "glinty" visual effects that are easily observable in the real world. While modeling the actual surface microstructure is possible, the resulting rendering problem is prohibitively expensive using Monte Carlo point sampling. We consider the highly complicated distribution of normals on a surface patch seen through a single pixel, and evaluate this actual distribution efficiently with closed-form solutions, in both geometric and wave optics. Results show complicated, temporally varying glints from materials such as bumpy plastics, brushed and scratched metals, metallic paint and ocean waves-bringing the interesting and important details to Computer Graphics for the first time.

Finite element analysis to determine the role of porosity in dynamic localization and fragmentation: Application to porous microstructures obtained from additively manufactured materials

In this paper, we have performed a microstructurally-informed finite element analysis on the effect of porosity on the formation of multiple necks and fragments in ductile thin rings subjected to dynamic expansion. For that purpose, we have characterized by X-ray tomography the porous microstructure of 4 different additively manufactured materials (aluminium alloy AlSi10Mg, stainless steel 316L, titanium alloy Ti6Al4V and Inconel 718L) with initial void volume fractions ranging from ≈0.0007% to ≈ 2%, and pore sizes varying between ≈ 6 μm and ≈110 μm. Three-dimensional analysis of the tomograms has revealed that the voids generally have nearly spherical shape and quite homogeneous spatial distribution in the bulk of the four materials tested. The pore size distributions quantified from the tomograms have been characterized using a Log-normal statistical function, which has been used in conjunction with a Force Biased Algorithm that replicates the experimentally observed random spatial distribution of the voids, to generate ring expansion finite element models in ABAQUS/Explicit (2016) which include actual porous microstructures representative of the materials tested. We have modeled the materials behavior using von Mises plasticity, and we have carried out finite element calculations for both elastic perfectly-plastic materials, and materials which show strain hardening, strain rate hardening and temperature softening effects. Moreover, we have assumed that fracture occurs when a critical value of effective plastic strain is reached. The finite element calculations have been performed for expansion velocities ranging from 50 m/s to 500 m/s. A key point of this investigation is that we have established individualized correlations between the main features of the porous microstructure (i.e. initial void volume fraction, average void size and maximum void size) and the number of necks and fragments formed in the calculations. In addition, we have brought out the effect of the porous microstrucure and inertia on the distributions of neck and fragment sizes. To the authors’ knowledge, this is the first paper ever considering actual porous microstructures to investigate the role of material defects in multiple localization and dynamic fragmentation of ductile metallic materials.

Influence of manufacturing-induced defects on the fatigue performances of autoclave moulded laminates

In the present work, cross-ply and multidirectional laminates were produced by autoclave moulding. Changes in the process parameters led to different microstructural features in terms of fibre volume fraction, global void content, and void size. Fatigue tests revealed a strong influence of the microstructure on the long-term performances of the laminates, in terms of life to crack initiation, crack propagation, crack density evolution and associated stiffness drop. A criterion recently proposed by the authors to predict the formation of the first fatigue cracks accounting for the actual material microstructure, including voids, was then validated on the new experimental data. The results show the need to properly account for the manufacturing induced defects for a more efficient and safer design of composite parts, and remark the necessity of developing models that link manufacturing process parameters, micro-scale morphology, and mechanical performances to enable a cost-effective production that maximizes the performance/cost ratio.

Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks

Although knowledge of the microstructure of food of vegetal origin helps us to understand the behavior of food materials, the variability in the microstructural elements complicates this analysis. In this regard, the construction of learning models that represent the actual microstructures of the tissue is important to extract relevant information and advance in the comprehension of such behavior. Consequently, the objective of this research is to compare two machine learning techniques—Convolutional Neural Networks (CNN) and Radial Basis Neural Networks (RBNN)—when used to enhance its microstructural analysis. Two main contributions can be highlighted from this research. First, a method is proposed to automatically analyze the microstructural elements of vegetal tissue; and second, a comparison was conducted to select a classifier to discriminate between tissue structures. For the comparison, a database of microstructural elements images was obtained from pumpkin (Cucurbita pepo L.) micrographs. Two classifiers were implemented using CNN and RBNN, and statistical performance metrics were computed using a 5-fold cross-validation scheme. This process was repeated one hundred times with a random selection of images in each repetition. The comparison showed that the classifiers based on CNN produced a better fit, obtaining F1–score average of 89.42% in front of 83.83% for RBNN. In this study, the performance of classifiers based on CNN was significantly higher compared to those based on RBNN in the discrimination of microstructural elements of vegetable foods.

Ductility Anisotropy Induced by Ferrite in Direct Laser Deposited 17-4 PH Steel: Combined Microstructure and Dislocation Density Simulation

The anisotropic ductility of a direct laser deposited 17-4 PH cubic part was investigated. Anisotropic elongations in the specimens from varied surfaces of the part were obtained: ~ 6.2%, ~ 1.5%, and ~ 4.5% in XY, YZ, and XZ samples, respectively. Furthermore, various orientations of ferrite were found in different specimens, taking the loading direction as reference. A finite element analysis depending on actual microstructures and dislocation density revealed that the orientation of ferrite caused the ductility anisotropy. The orientation of ferrite affected its plastic deformability and the deformation compatibility between phases during the uniaxial loading. The ferrite parallel to the tensile direction in the YZ sample had the worst deformability and induced severe strain localization and stress triaxiality, which resulted in inferior ductility. The ferrite perpendicular to the tensile direction showed the best deformability, whereas strain localization remained intense in the XZ sample owing to the unmatched deformability of martensite. The inclined ferrite in the XY sample exhibited moderate deformability and was found to enhance the plastic flow of martensite, leading to the best deformation compatibility and ductility.

Progressive modeling of transverse thermal conductivity of unidirectional natural fiber composites

The effective transverse thermal conductivity and localized thermal fields of unidirectional natural fiber composites is investigated through the proposed progressive micro-modeling technique. Different from the classical models, the present model starts from recovering the actual natural fibrous microstructures with fiber bundles filled with lumens and surrounded with matrix phase. Based on the concept of hierarchical microstructures, the fiber bundles with lumen and hollow fibers are treated as the lower-level composite unit cells while the composites with reinforced fiber bundles and matrix as the higher-level unit cells. The information transfer between two levels is guaranteed by a progressive homogenization technique – local exact homogenization theory that is presently extended with thermal conductivity for composites with nano-surface effects. The predicted effective transverse thermal conductivity coefficients of nano-/micro-composites are validated against the analytical and numerical simulations, as well as experimental data, where good agreement is generally obtained. An optimization procedure is also connected to the present micromechanics to search for the optimal microstructural parameter for the designed effective thermal coefficients at various fiber volume fractions. Finally, the local hierarchical thermal fields are also recovered to get insights into the microstructural concentrations. We have also encapsulated our progressive model into a black-box program with inputs/outputs, facilitating its application by other researchers.

Effect of the distribution of lip-shaped matrix cracks in continuous fibre reinforced ceramic matrix composites on failure progress under transverse tension

ABSTRACT This paper develops a numerical model that contained random lip-shaped matrix cracks based on the maximum energy release rate criterion for the prediction of failure progress of Continuous Fibre-reinforced Ceramic Matrix Composites (CFCC) under transverse tension. Fibre distribution layout is created based on the actual microstructure and the Random Sequential Adsorption algorithm is used to generate the distribution of lip-shaped matrix cracks. The influence of the lip-shaped matrix crack distribution and crack density on the failure progress and stress–strain response was investigated. It was found that the onset of damage initiation commenced from the lip-shaped matrix crack when the interface fracture energy was smaller than the critical interface fracture energy. The failure progress and the predicted results were in good agreement with the experimental results. The different matrix cracks distribution affected the failure progress. Finally, the tensile strength and the elastic modulus both decreased with the crack density increasing.

Effect of void nucleation on microstructure and stress state in aluminum alloy tailor-welded blank

The microscopic damage initiation characteristic in welded joint greatly determines the subsequent damage evolution and fracture behavior of aluminum alloy tailor-welded blank (TWB) during plastic forming. In this study, the interactive dependence of void nucleation on microstructure and stress state in the welded joint of a 2219 aluminum alloy TWB was quantitatively explored by in-situ SEM testing. Moreover, a micromechanical model based on actual microstructure was adopted to reveal the underlying mechanisms from the perspective of microscopic heterogeneous deformation. The results showed that three void nucleation mechanisms, including particle-cracking, interface-debonding and matrix-cracking, coexisted in the deformation at different microstructure regions and stress states. The nucleation strain of each mechanism mainly depended on the particle volume fraction, grain size and stress triaxiality. Besides, the proportions of particle-cracking and interface-debonding greatly varied with the grain size and particle volume fraction, and the variation law changed with the stress state. The proportion of matrix-cracking had a weak dependence on the microstructure, while increased with the stress triaxiality decreasing. It made the damage initiation in aluminum alloy welded joint transit from particle-cracking dominance to matrix-cracking dominance with the stress triaxiality decreasing. The micromechanical modeling results suggested that the changes of evolutions and distributions of Mises stress in particle, hydrostatic stress at interface and plastic strain in matrix with microstructure and stress state were responsible for the above effects.

The Concept of Effective Porosity in the Discharge Rate Performance of High-Density Positive Electrodes for Automotive Application

The discharge rate performance of NMC532-based electrodes designed for EV application were measured between 0 °C and 40 °C and were compared to the predictions of the electrolyte limited penetration depth model [Gallagher et al., J. Electrochem. Soc. 163, A138 (2016)], also called diffusion limited current density model [Heubner et al., J. Power Sources 419, 119 (2019)]. To support this analysis, we took into account the actual microstructure of the electrodes, previously characterized by FIB/SEM tomography, and their measured and/or simulated transport properties. We show that the performance of NMC532 electrodes, even with a low carbon content below the percolation threshold, are not limited by electrons transport through the electrode due to the high intrinsic conductivity of this active material. At 40 °C, the swelling of the PVdF by the electrolyte solvents penalizes performance, especially as the binder content is high. Above all, the current density at which a brutal decrease in capacity occurs is well predicted by the model, although by reducing the porosity to its percolated micrometer sized fraction. This is in good agreement with the numerical simulations of the ionic transport properties.

Phase field predictions of microscopic fracture and R-curve behaviour of fibre-reinforced composites

We present a computational framework to explore the effect of microstructure and constituent properties upon the fracture toughness of fibre-reinforced polymer composites. To capture microscopic matrix cracking and fibre-matrix debonding, the framework couples a phase field fracture method and a cohesive zone model in the context of the finite element method. Virtual single-notched three point bending tests on fibre reinforced composites are conducted. The actual microstructure of the composite is simulated by an embedded cell in the fracture process zone, while the remaining area is homogenised to be an anisotropic elastic solid. A detailed comparison of the predicted results with experimental observations reveals that it is possible to accurately capture the crack path, interface debonding and load versus displacement response. The sensitivity of the crack growth resistance curve (R-curve) to the matrix fracture toughness and the fibre-matrix interface properties is determined. The influence of porosity upon the R-curve of fibre-reinforced composites is also explored, revealing a higher crack growth resistance with increasing void volume fraction. These results shed light into microscopic fracture mechanisms and set the basis for efficient design of high fracture toughness composites.

Rapid Thermal Annealing of Double Perovskite Thin Films Formed by Polymer Assisted Deposition.

The annealing process is an important step common to epitaxial films prepared by chemical solution deposition methods. It is so because the final microstructure of the films can be severely affected by the precise features of the thermal processing. In this work we analyze the structural and magnetic properties of double perovskite La2CoMnO6 and La2NiMnO6 epitaxial thin films prepared by polymer-assisted deposition (PAD) and crystallized by rapid thermal annealing (RTA). It is found that samples prepared by RTA have similar values of saturation magnetization and Curie temperature to their counterparts prepared by using conventional thermal annealing (CTA) processes, thus indicating low influence of the heating rates on the B-B’ site cationic ordering of the A2BB’O6 double perovskite structure. However, a deeper analysis of the magnetic behavior suggested some differences in the actual microstructure of the films.