Quantitative Analysis Of Microstructure Research Articles

Effect of sodium carbonate on the properties of seventy percent of Tartary buckwheat composite flour-based doughs and noodles and the underlying mechanism.

The impact of Na2 CO3 on the properties of doughs and noodles containing 70% Tartary buckwheat flour was investigated. Low-field 1 H nuclear magnetic resonance showed the mobility of water in the doughs significantly declined with the addition content of alkali from 0% to 0.9%. Na2 CO3 promoted the transformation from free sulfhydryl groups to disulfide bonds in doughs because the sulfhydryl groups in cysteine preferred to form thiolate anion and then oxidate under alkaline conditions. As for non-covalent chemical interactions, a significant increase of hydrogen bonds and a decrease of hydrophobic interactions were observed after Na2 CO3 addition. Quantitative analysis of microstructure showed that more uniform and denser gluten networks with higher branching rate and shorter average protein length and width formed in the doughs with 0.3%-0.6% of Na2 CO3 . The aggregated glutenin macropolymer and enhanced protein structure led to significantly stronger tensile of Tartary buckwheat dough sheets, which could meet the demand of continuous processing in the factory. Dough with alkali had higher swelling power and pasting viscosities, contributing to higher water absorption, and improved textural attributes of cooked noodles. This study demonstrated the possibility of adding Na2 CO3 at a moderate level for promoting the sheeting, cooking, and eating properties of high Tartary buckwheat flour composite noodles.

Research on the mechanism of composite improvement of loess based on quantitative analysis of microstructure and mechanical strength

Loess has poor engineering geological properties due to its loose and porous soil structure. To meet the engineering requirements of bearing capacity of foundations in loess areas, various stabilizers have been added to the loess during construction. Currently, cement, fly ash, fibers (natural and synthetic) and ionic curing agents have been widely used as stabilizers worldwide. In this study, the Composite Improvement Method (CIM) was used to improve the loess mechanical properties. A series of experimental tests with three factors and five-level orthogonal were conducted on loess samples with the inclusion of cement, curing agent and polypropylene fiber, to investigate the relative strength in terms of direct shear tests and unconfined compression tests. The test results were analyzed by range and variance analysis to quantify the significance of the improved materials, and the optimal improvement scheme was obtained. The results show that cement content has the greatest influence on shear strength, especially on unconfined compressive strength (UCS), followed by polypropylene fiber content and curing agent content. To reveal the internal mechanism of loess improvement by CIM, the quantitative microstructural information of the improved soil was extracted, and the microscopic geometric parameters such as apparent porosity and fractal dimension were calculated and analyzed as well. Based on the qualitative and quantitative analysis of microscopic images, it is observed that CIM increased the roundness of soil particles, decreased the total number of soil particles, and reduced the fractal dimension of particles and pores. Additionally, the composite improvement mechanism of CIM is further revealed. CIM can stimulate the activity of each other between the improved materials and work together to improve the engineering geological properties of the soil.

Quantitative Load Dependency Analysis of Local Trabecular Bone Microstructure to Understand the Spatial Characteristics in the Synthetic Proximal Femur.

Analysis of the dependency of the trabecular structure on loading conditions is essential for understanding and predicting bone structure formation. Although previous studies have investigated the relationship between loads and structural adaptations, there is a need for an in-depth analysis of this relationship based on the bone region and load specifics. In this study, the load dependency of the trabecular bone microstructure for twelve regions of interest (ROIs) in the synthetic proximal femur was quantitatively analyzed to understand the spatial characteristics under seven different loading conditions. To investigate the load dependency, a quantitative measure, called the load dependency score (LDS), was established based on the statistics of the strain energy density (SED) distribution. The results showed that for the global model and epiphysis ROIs, bone microstructures relied on the multiple-loading condition, whereas the structures in the metaphysis depended on single or double loads. These results demonstrate that a given ROI is predominantly dependent on a particular loading condition. The results confirm that the dependency analysis of the load effects for ROIs should be performed both qualitatively and quantitatively.

Noninvasive monitoring of muscle atrophy and bone metabolic disorders using dual-energy X-ray absorptiometry in diabetic mice.

Tracking metabolic changes in skeletal muscle and bone using animal models of diabetes mellitus (DM) provides important insights for the management of DM complications. In this study, we aimed to establish a method for monitoring changes in body composition characteristics, such as fat mass, skeletal muscle mass (lean mass), bone mineral density, and bone mineral content, during DM progression using a dual-energy X-ray absorptiometry (DXA) system in a mouse model of streptozotocin (STZ)-induced type 1 DM. In the DM model, STZ administration resulted in increased blood glucose levels, increased water and food intake, and decreased body weight. Serum insulin levels were significantly decreased on day 30 of STZ administration. The DXA analysis revealed significant and persistent decreases in fat mass, lower limb skeletal muscle mass, and bone mineral content in DM mice. We measured tibialis anterior (TA) muscle weight and performed a quantitative analysis of tibial microstructure by micro-computed tomography imaging in DM mice. The TA muscle weight of DM mice was significantly lower than that of control mice. In addition, the trabecular bone volume fraction, trabecular thickness, trabecular number, and cortical thickness were significantly decreased in DM mice. Pearson's product-moment correlation coefficient analysis showed a high correlation between the DXA-measured and actual body composition. In conclusion, longitudinal measurement of body composition changes using a DXA system may be useful for monitoring abnormalities in muscle and bone metabolism in animal models of metabolic diseases such as DM mice.

Quantitative image analysis of protein foam microstructure and its correlation with rheological properties: Egg white foam

Recent advances in software development and machine learning algorithms are revolutionizing the way microstructures are analyzed and quantified in fields like biology and neuroscience. With this, comes an upsurge in the opportunities to apply these tools to study food microstructure to gain a deeper understanding of food structure-function relationships. This article shows how a recently developed deep-learning cellular segmentation algorithm, ‘ Cellpose ’ can be used to identify foam microstructure for further quantification. It successfully identified the air bubbles in a protein foam matrix, from microscopic images captured on a simple brightfield microscope. The segmentation algorithm allowed further quantification of microstructural parameters of the air phase (bubbles) and of the liquid phase (lamella) of the foams. Egg white foams were made with basic ingredients for meringue and the effect of sugar concentration and acidic conditions were studied. Microstructural parameters were analyzed in relation to the rheological responses of the foams. The underlying microstructural mechanisms governing the changes in the foams' stiffness and linearity of the viscoelastic response are shown. Sugar changes bubble size distribution by thickening liquid egg white, it also shortened the linear response to deformation due to its decreasing lamella thickness. Acidity re-shaped the bubbles into more ‘hexagon-like’ structures allowing more air to be incorporated in the foams. The shape of the bubbles under acidic conditions also makes the foams extend their linear response to deformation. The understanding achieved with data from the algorithm-identified microstructures presents a new way to characterize and study food foams. • Microscopy images of egg white foam were captured using a simple brightfield microscope. • The air and liquid phases were successfully distinguished by the Cellpose algorithm. • Quantitative microstructural parameters of both phases were obtained in subsequent image analyses. • Sugar decreases bubble size while acidity re-shapes bubbles into a hexagon-like shape. • Sugar and acid microstructural mechanisms alter the linear viscoelastic response of the foams. • Obtaining microstructural paraments leads to new insights into the study of foams.

Exploring the underlying mechanisms on starch restricted swelling-induced reduction in digestion rate of buckwheat noodles

To elucidate the slower digestion rate of buckwheat starch in noodles-matrix, the digestion kinetics of Tartary (TBN) and common (CBN) buckwheat starch reconstituted noodles as well as wheat starch reconstituted noodles (WSN) were compared using a simulated oral-gastric-intestinal in vitro digestion. The swelling degree of starch in cooked noodles can be expressed by quantitative analysis of microstructure. The digestion rate of WSN was 2.3–2.9 times higher as compared to CBN and TBN. The swelling degree of starch in CBN and TBN was restricted due to their denser structure, which led to the reduction of the contact area between starch molecules and amylase. Additionally, the stronger water retention ability and higher cohesiveness of CBN and TBN could hinder the water migration, and ultimately affect the accessibility of enzymes. These results demonstrated that the restricted swelling of starch in CBN and TBN during cooking was the important reason for their slower digestibility.

Effect of existence state of asphaltenes on microstructure of wax crystals: Fractal dimension and unit cell structure

The influence of asphaltenes on the wax crystallization has long been studied, yet the quantitative analysis of microstructure of wax crystals are rarely involved. In this research work, paraffin (C20∼C40) and n-heptane asphaltenes were used to prepare model oils containing varying amounts of asphaltenes. By means of the Fourier transform infrared spectroscopy (FTIR), microscopy and X-ray diffraction (XRD), the influence of asphaltenes on the microstructure of wax crystals was systematically analyzed focusing on the fractal dimension (area box dimension, perimeter box dimension and average size) and unit cell. Four main findings were made: (1) The influence of asphaltenes on microstructure of wax crystals mainly depends on its existence state, that is, the dispersed asphaltenes and aggregated asphaltenes. (2) As the asphaltene concentration increases, asphaltenes gradually transform from dispersed to aggregated state, and the area box dimension of waxy crystals increases firstly and then decreases, and the maximum occurs at the transition of asphaltenes state, while asphaltenes have no significant effect on the perimeter box dimension of wax crystals. (3) At lower temperatures, the average size of wax crystals without asphaltenes is significantly larger than that cases with asphaltenes, but it can be ignored as the temperatures increase. (4) Existence state of asphaltenes can affect the structural integrity of wax crystals. When the asphaltenes are well dispersed, XRD shows that the structure of wax crystals is relatively intact, but when the asphaltenes are aggregated state, wax crystals show more defects. This study is expected to provide guidance for the control of flow assurance in the coexistence of wax and asphaltenes from the mechanism level.

A groupwise registration and tractography framework for cardiac myofiber architecture description by diffusion MRI: An application to the ventricular junctions.

BackgroundKnowledge of the normal myocardial–myocyte orientation could theoretically allow the definition of relevant quantitative biomarkers in clinical routine to diagnose heart pathologies. A whole heart diffusion tensor template representative of the global myofiber organization over species is therefore crucial for comparisons across populations. In this study, we developed a groupwise registration and tractography framework to resolve the global myofiber arrangement of large mammalian sheep hearts. To demonstrate the potential application of the proposed method, a novel description of sub-regions in the intraventricular septum is presented.MethodsThree explanted sheep (ovine) hearts (size ~12×8×6 cm3, heart weight ~ 150 g) were perfused with contrast agent and fixative and imaged in a 9.4T magnet. A group-wise registration of high-resolution anatomical and diffusion-weighted images were performed to generate anatomical and diffusion tensor templates. Diffusion tensor metrics (eigenvalues, eigenvectors, fractional anisotropy …) were computed to provide a quantitative and spatially-resolved analysis of cardiac microstructure. Then tractography was performed using deterministic and probabilistic algorithms and used for different purposes: i) Visualization of myofiber architecture, ii) Segmentation of sub-area depicting the same fiber organization, iii) Seeding and Tract Editing. Finally, dissection was performed to confirm the existence of macroscopic structures identified in the diffusion tensor template.ResultsThe template creation takes advantage of high-resolution anatomical and diffusion-weighted images obtained at an isotropic resolution of 150 μm and 600 μm respectively, covering ventricles and atria and providing information on the normal myocardial architecture. The diffusion metric distributions from the template were found close to the one of the individual samples validating the registration procedure. Small new sub-regions exhibiting spatially sharp variations in fiber orientation close to the junctions of the septum and ventricles were identified. Each substructure was defined and represented using streamlines. The existence of a fiber-bundles in the posterior junction was validated by anatomical dissection. A complex structural organization of the anterior junction in comparison to the posterior junction was evidenced by the high-resolution acquisition.ConclusionsA new framework combining cardiac template generation and tractography was applied on the whole sheep heart. The framework can be used for anatomical investigation, characterization of microstructure and visualization of myofiber orientation across samples. Finally, a novel description of the ventricular junction in large mammalian sheep hearts was proposed.

The relation between wheat starch properties and noodle springiness: From the view of microstructure quantitative analysis of gluten-based network

Understanding the effect of starch properties on the gluten network during noodle-making will allow rational selection of ingredients. The microstructure and texture of dough sheets, dried noodles, and cooked noodles prepared by adding different wheat starches were compared. Increasing starch solubility and swelling power could reduce the compactness and homogeneity of the network in dough sheet. Lower swelling power and higher solubility of starch made the elastic modulus of dried noodles increase, leading to increasing branching rate along with decreasing average network width and length of cooked noodles. After cooking, noodles with increasing springiness showed higher branching rate of gluten network in dough sheet, greater elastic modulus of dried noodles, and lower average network length of cooked noodles. It was thus proposed that wheat starch with lower swelling power could enhance the springiness of noodles. The quantitative analysis of microstructure was an appropriate method to predict the noodle springiness.

0601 Predicting Incident Outcomes from the Microstructure of Sleep

Abstract Introduction Sleep contains rich information about health status, relevant to future health outcomes including dementia, cerebrocardiovascular diseases, psychiatric diseases, and mortality. We hypothesized that the risk of these outcomes is predictable from quantitative analysis of sleep microstructure. Methods We included participants who underwent a diagnostic study and age older than 18 years. We excluded participants with missing demographics or PSGs < 2.5 hours in duration. We considered 11 outcomes including dementia, mild cognitive impairment or dementia, ischemic stroke, intracranial hemorrhage, atrial fibrillation, myocardial infarction, type 2 diabetes, hypertension, bipolar disorder, depression, and mortality. The outcomes were determined using ICD codes, brain imaging reports, medications, and/or cognition scores. We extracted 86 spectral and time-domains features from overnight sleep EEG recordings, including 57 features from NREM sleep epochs, 21 from REM, and 8 covariates including age, sex, body mass index, and medication prescriptions including benzodiazepines, antidepressants, sedatives, antiseizure medications, and stimulants. We modeled risk using Cox survival analysis with death as a competing risk. Model calibration was assessed using the difference in 10-year cumulative incidence (10y-CI) between Cox estimate vs. Aalon-Johansen estimate (ground truth). Results There were 8673 participants with an average age of 51 years; 51% were female. Participants were partitioned into three groups: poor sleep (hazard > 3rd quartile (Q3)), average sleep (Q1 ≤ hazard ≤ Q3), and good sleep (hazard < Q1). The model was able to predict the 10y-CI not significantly different from the ground truth, except for the risk of intracranial hemorrhage in the poor sleep group. The outcome-wise mean prediction difference in 10y-CI was 2.3% for the poor sleep group, 0.5% for the average sleep group, and 1.3% for the good sleep group. The outcomes with the top three poor-to-average risk ratios (RR) were dementia (RR = 6.2 95% confidence interval [4.5 – 9.3]), mortality (RR = 5.7 [5.0 – 7.5]), and MCI or dementia (RR = 4.0 [3.2 – 4.9]). Conclusion Sleep EEGs contain decodable information about the risk for future incidence of mortality, dementia, cerebrocardiovascular and psychiatric diseases. The findings strengthen the concept of sleep as a window into brain and general health. Support (If Any) This work is supported by the AASM Foundation 2019 Strategic Research Award.

Impact of the training loss in deep learning-based CT reconstruction of bone microarchitecture.

Computed tomography (CT) is a technique of choice to image bone structure at different scales. Methods to enhance the quality of degraded reconstructions obtained from low-dose CT data have shown impressive results recently, especially in the realm of supervised deep learning. As the choice of the loss function affects the reconstruction quality, it is necessary to focus on the way neural networks evaluate the correspondence between predicted and target images during the training stage. This is even more true in the case of bone microarchitecture imaging at high spatial resolution where both the quantitative analysis of bone mineral density (BMD) and bone microstructure is essential for assessing diseases such as osteoporosis. Our aim is thus to evaluate the quality of reconstruction on key metrics for diagnosis depending on the loss function that has been used for training the neural network. We compare and analyze volumes that are reconstructed with neural networks trained with pixelwise, structural, and adversarial loss functions or with a combination of them. We perform realistic simulations of various low-dose acquisitions of bone microarchitecture. Our comparative study is performed with metrics that have an interest regarding the diagnosis of bone diseases. We therefore focus on bone-specific metrics such as bone volume and the total volume (BV and TV), resolution, connectivity assessed with the Euler number, and quantitative analysis of BMD to evaluate the quality of reconstruction obtained with networks trained with the different loss functions. We find that using norm as the pixelwise loss is the best choice compared to or no pixelwise loss since it improves resolution without deteriorating other metrics. Visual Geometry Group (VGG) perceptual loss, especially when combined with an adversarial loss, allows to better retrieve topological and morphological parameters of bone microarchitecture compared to Structural SIMilarity (SSIM) index. This however leads to a decreased resolution performance. The adversarial loss enhances the reconstruction performance in terms of BMD distribution accuracy. In order to retrieve the quantitative and structural characteristics of bone microarchitecture that are essential for postreconstruction diagnosis, our results suggest to use norm as part of the loss function. Then, trade-offs should be made depending on the application: VGG perceptual loss improves accuracy in terms of connectivity at the cost of a deteriorated resolution, and adversarial losses help better retrieve BMD distribution while significantly increasing the training time.

Research on the Mechanism of Composite Improvement of Loess Based on Quantitative Analysis of Microstructure and Mechanical Strength

Research on the Mechanism of Composite Improvement of Loess Based on Quantitative Analysis of Microstructure and Mechanical Strength

Effect of temperature, time, and shear force on the morphology and size of dendrites in A356-Al2O3 composites

Dendrite arm spacing (DAS) plays a vital role in determining mechanical properties of aluminum casting composites. In this study, the effects of three different methods of gravity sand casting, squeeze casting, and semi-solid compo-casting with different electromagnetic stirring currents and times on the microstructural morphology of A356-Al2O3 composites were investigated. In order to investigate effects of shear forces on the formation of the globular-like structure, samples with different stirring electric current and times were produced by compo-casting in semi-solid state and analyzed. Optical emission microscopy was used to study the microstructure and DAS of nanocomposites. Quantitative analysis of microstructure was performed by utilizing an image analysis system. In this study, the optimum temperature for composite fabrication for this type of composite was calculated to obtain the most appropriate morphology and size of the microstructure. Also, the appropriate time to keep this composite at this temperature was obtained, and finally the relevant microstructures were fully discussed.

Quantitative analysis of microstructure and mechanical properties of Nb–V microalloyed high-strength seismic reinforcement with different Nb additions

Abstract HRB500E seismic steel bars are mainly used in high-rise buildings near the seismic zone. The influence of different niobium contents (0–0.023%) on the microstructure and mechanical properties of HRB500E seismic reinforcement was studied. Results showed that the grain size of ferrite was between 3.6 and 8.3 μm when only V was added. Meanwhile, as the niobium content increases, the ferrite particles are further refined. After adding niobium, the grain contribution increased by 9%. The addition of niobium significantly refined the grain size of HRB500E seismic reinforcement. The second-phase nano-elliptic precipitate is NbC. The precipitated phase is dispersed on the grain boundary and the matrix, and the dislocation density on the matrix promotes the precipitation of NbC particles along the dislocation line. The second-phase precipitation of niobium can form an effective pinning effect and then refine the pearlite spacing. The microhardness and the tensile strength also significantly improved. The yield strength increased from 509 to 570 MPa.

Study on high-cycle fatigue fracture mechanism and strength prediction of RuT450

Fatigue failure is the most common failure mode of structural materials. In this study, the high-cycle fatigue properties at different temperatures, fracture surface morphologies and corresponding damage mechanisms of a widely used vermicular graphite cast iron RuT450 were investigated. It is found that the fatigue strength values are 150 MPa, 143 MPa and 108 MPa for room temperature, 400 °C and 500 °C, respectively. The decrease rate of fatigue strength is affected by the change of morphology. At high temperature, the proportion of pearlite decreases and graphite becomes blunt. In general, the cracks initiate from the graphite phase boundary and propagate through the pearlite lamellae. In addition, according to the change of matrix micro-structure and crack growth processes under different temperature conditions, and by the quantitative analysis of micro-structure, the effect of temperature can be transformed into related parameters of matrix. Based on the changes, a fatigue strength prediction model for vermicular graphite cast iron at different temperatures was proposed, which predicts the trend well.

Quantitative analysis of compatible microstructure by electron backscatter diffraction.

We propose a scheme for assigning the martensite variant using electron backscatter diffraction in a martensite material that undergoes a solid-solid phase transformation. Based on the solutions of the crystallographic equations of martensite, we provide an algorithm to assign martensite variants to a particular microscopic region, and to check the elastic compatibility of the microstructure corresponding to low hysteresis and high reversibility in shape memory alloys. This article is part of the theme issue 'Topics in mathematical design of complex materials'.

Three dimensional characterization of micronized soybean seeds using X-ray microtomography

The efficacy of critical processing techniques, such as micronization, relies on the microstructural changes that take place in foods as they go through the various unit operations. X-ray microtomography or computed microtomograhy (μCT) can serve as a means to non-invasively and non-destructively characterize the complex structure of food matrices. Herein, high resolution X-ray μCT was used to visualize and quantify the effect of micronization or infrared (IR) treatment on the microstructure of soybeans at different micronization durations (60, 90, and 120 s), and initial moisture contents (12, 16, 20, and 24% wb). The study includes a quantitative and qualitative analysis of soybean microstructure using 2D and 3D images to assess total porosity, volumetric changes, bulk and particle densities, drying rate, and colour. All of these parameters showed significant changes as micronization time and initial moisture content increased. Although higher initial moisture contents (20 and 24%) improved volumetric expansion and total porosity of soybean kernels, formation of large surface cracks and darkening due to IR heating rendered 24% moisture content unsuitable. Soybeans with an initial moisture content of 20%, subjected to 90 s and 120 s of micronization showed comparable lightness L*values (63.23 ± 3.9 and 61.1 ± 4.1, respectively) to the unprocessed soybean (67.22). These beans also showed the minimal increase in open porosity (5.72–6.06%) but an appreciable increase in total porosity (9.8–11%) was noticed. The results of this study on microstructure and qualitative parameters of micronized soybeans could be used to further optimize the microwave assisted roasting and drying processes of beans and pulses.

Characterization of Ultrahigh-Performance Concrete Materials for Application in Modular Construction

In this paper, a new class of self-consolidating ultrahigh-performance concrete (UHPC) was investigated. The UHPC featured a compressive strength higher than 150 MPa with self-consolidating characteristics desirable for modular construction. This paper describes the development and characterization of UHPC, its large-scale processing techniques with conventional equipment, its verification with internal and surface microstructure analysis techniques, and the structural behavior of large-scale steel plate UHPC (S-UHPC) beams under out-of-plane loads. Based on a particle size distribution study, an optimum packing density was achieved in the mixture that uses an uncommonly used undensified silica fume. The physical parameters of the ingredients and the resulting microstructure after hydration are considered essential for the design of self-consolidating UHPC materials. Distinguishing the three phases of the material after hydration using three-dimensional X-ray microtomography allowed the quantitative analysis of the UHPC microstructure. The internal microstructure study showed a significant reduction of macropores in UHPC compared to mortar specimens, which confirmed the physical attributes behind the improved material characteristics. A microstructure study of the material was also performed using scanning electron microscopy. In addition, a comparison of the experimental results of four large-scale steel plate concrete (SC) beam specimens cast with either conventional concrete or UHPC materials under three-point shear loading indicated that the developed UHPC materials significantly enhanced the ductility and capacity of the S-UHPC beams.

The Analysis of Microstructure and Texture Evolution in Polycrystal and Single Crystals of Nickel after Hydrostatic Extrusion Process

The present work describes the influence of plastic deformation process on microstructureand texture evolution in face centered cubic materials. The investigations were carried out on polycrystal and single crystals with the initial orientation 〈111〉 and 〈110〉 of pure nickel (99.95%). Severe plastic deformation was realized by hydrostatic extrusion process with total strain value 0.5. The paper contains the qualitative and quantitative analysis of microstructure and description of texture evolution. The microstructure observations show the strong defected structure with many dislocations and average grain size about 0.4 µm. The texture results have shown the fibrous character of a texture only in polycrystalline material and strong texture in single crystals after deformation process.

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

We investigate the methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy (uranium–molybdenum) that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on the F1 score. A F1 score of 95.1% was achieved for distinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions. We find that our newly developed microstructure representation describes image data well, and the traditional approach of utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relatively small, imbalanced original dataset of 272 images. To explore the applicability of generative methods for supplementing such limited datasets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image datasets are also discussed. Our work has implications for quantitative microstructure analysis and development of microstructure–processing relationships in limited datasets typical of metallurgical process design studies.