Microstructure Images Research Articles

Development of Cordierite Based Carrier Refractory Sagar Bodies for Bone Porcelain Firing Process

During the firing process of porcelain tableware; Biscuit firing takes place at low temperatures (980-1000°C), while glazed firing takes place at high temperatures (1250-1280°C for soft porcelain, 1350-1380°C for hard porcelain). Biscuit firing in bone porcelain products, which is in the soft porcelain class, is done at higher temperatures than glazed firings. Due to the presence of bone ash in the Bone China recipe formulation, it causes the bodies to undergo vitrification in a narrow range and thus the final product to deform during sintering. Bone porcelain products are fired on carrier refractories called sagar so that they do not deform during sintering. Sagars are designed to support that model for each product model and do not shrink or deform during firing thanks to its low thermal expansion coefficient. In this study, a refractory body with a porous structure with the code of "PS1-Std" was developed by performing the characterization analyzes of refractory products with different technical properties supplied from different companies. In order to improve the mechanical properties by changing the ratios of talc, alumina, quartz and zircon in the recipe composition; A refractory product containing 8.47% zircon in its recipe composition and containing indialite, corundum, mullite, quartz and zircon phases after sintering has been developed. The microstructure images of the developed refractory product were examined with the support of SEM analysis. It has been observed that refractory products obtained as a result of recipe development studies offer a 10% longer service life than equivalent refractory products.

Effects of process parameters on selective laser melting of Ti6Al4V-ELI alloy and parameter optimization via response surface method

Selective laser melting (SLM) allows for manufacturing intricate and accurate component designs that are challenging, expensive, or impossible to achieve using traditional methods. To achieve a high-quality final product with SLM, it is necessary to optimize the process parameters carefully. This optimization helps to minimize internal structural defects and control the microstructure. In this study, the optimum production parameters of Ti6Al4V alloy were determined using the Response Surface Method (RSM). The micro-CT analysis was utilized to calculate the relative densities of the samples fabricated using different production parameters. Furthermore, the influence of heat treatment, conducted below and above the β transformation temperature, on the mechanical properties was examined. The results were analyzed by considering the microstructure images obtained from Scanning Electron Microscope (SEM) and X-ray Diffraction (XRD) analysis. The optimum production parameters for the SLM method were found to be 80W laser power, 1125 mm/s scanning speed, 25 μm layer thickness, and 45 μm hatch distance. Samples created with these optimized parameters achieved 99.919% relative density, 1050 MPa ultimate tensile stress, and 13.8% elongation, surpassing the minimum standards outlined in ASTM F3001-14. Additionally, the systematic parameter reduction approach used in the study led to savings in both powder consumption and time in SLM, a method that could be applied to various metallic materials.

Laser confocal positioning super-oscillatory optical microscopy

Optical superoscillation technologies have been successfully used to realize far-field sub-diffraction focusing, super-resolution imaging and nano metrology. A transmission-mode super-oscillatory super-resolution optical microscope has been built, in which both the best focal plane of super-oscillatory lens (SOL) and the sample to be imaged can be accurately positioned by the laser confocal detection method. The sub-diffraction focusing and super-resolution imaging experiments have been carried out based on a SOL with a diameter of 3 mm and a focal length of 0.8 mm. The developed super-oscillatory super-resolution optical microscope can be practically used in high-resolution scanning imaging of 2D thin subwavelength microstructures.

Modeling the 4D discharge of lithium-ion batteries with a multiscale time-dependent deep learning framework

The lithium-ion battery (LIB) field is moving towards the direction of investigating spatially resolved physical phenomena in the 3D porous microstructure of electrodes. These pore-scale simulations give new insights into the local dynamics of lithiation/de-lithiation and charge transport. Nevertheless, the computational time of these simulations limits the integration of these models in optimization workflows of cycling conditions or electrode manufacturing processes.Machine learning models present a way of assessing in real-time the performance of materials. While several successful techniques for replicating simulations with machine learning have been proposed, this case study presents a more demanding problem, due to the necessity of understanding the behavior of heterogeneous 3D local data, as it evolves in time: this poses both a scientific and a technical challenge.To this end, we propose an autoregressive multiscale convolutional neural network model to predict relevant quantities at the pore-scale in the solid phase: the lithium concentration (in the active material) and potential (in the active material and carbon binder). These are ultimately used to reconstruct the battery discharge curve. 3D images of the electrode microstructures are the input to the network, trained with a dataset of finite element method simulations to predict the discharge behavior of the cathode side in lithium ion batteries.We propose this machine learning model as a proof-of-concept of the applicability of multiscale networks for time-dependent physics problems. The trained model exhibits very high accuracy (with errors lower than 2%) in forecasting the discharge behavior of new unseen cathodes.

Influence of in-situ annealing temperature upon structural, micro-structural, magnetic damping properties in sputtered Fe3O4 thin films

The influence of in-situ annealing temperature upon structural, micro-structural, magnetic properties and Gilbert damping parameters of sputtered Fe3O4 thin films (29–49 nm) upon SiO2/Si substrates have been systematically investigated. The oriented Fe3O4 structural phase was evaluated using grazing incidence X-ray diffraction, confocal Raman spectroscopy and X-ray photoelectron spectroscopy data. The atomic force and field-emission scanning electron microscope surface microstructural images reveal an enhanced grain growth with dominant anti-phase boundaries (APB) with in-situ annealing. The macroscopic spin dynamical response is evaluated using co-planar waveguide ferromagnetic resonance (CPW-FMR) spectroscopy from 15 to 35 GHz frequencies. The FMR data reveals a low Gilbert damping (α) ranging from 0.003 ± 0.001 to 0.009 ± 0.006 for all the in-situ annealed Fe3O4 thin films. Further, high inhomogeneity (ΔH0) parameter is correlated to the surface defects and APBs.

Segmentation of tooth enamel microstructure images using classical image processing and U-Net approaches

IntroductionTooth enamel is the hardest tissue in human organism, formed by prism layers in regularly alternating directions. These prisms form the Hunter–Schreger Band (HSB) pattern when under side illumination, which is composed of light and dark stripes resembling fingerprints. We have shown in previous works that HSB pattern is highly variable, seems to be unique for each tooth and can be used as a biometric method for human identification. Since this pattern cannot be acquired with sensors, the HSB region in the digital photograph must be identified and correctly segmented from the rest of the tooth and background. Although these areas can be manually removed, this process is not reliable as excluded areas can vary according to the individual‘s subjective impression. Therefore, the aim of this work was to develop an algorithm that automatically selects the region of interest (ROI), thus, making the entire biometric process straightforward.MethodsWe used two different approaches: a classical image processing method which we called anisotropy-based segmentation (ABS) and a machine learning method known as U-Net, a fully convolutional neural network. Both approaches were applied to a set of extracted tooth images.ResultsU-Net with some post processing outperformed ABS in the segmentation task with an Intersection Over Union (IOU) of 0.837 against 0.766.DiscussionEven with a small dataset, U-Net proved to be a potential candidate for fully automated in-mouth application. However, the ABS technique has several parameters which allow a more flexible segmentation with interactive adjustments specific to image properties.

Data augmentation and data mining towards microstructure and property relationship for composites

PurposeIn recent years, the convolutional neural network (CNN) based deep learning approach has succeeded in data-mining the relationship between microstructures and macroscopic properties of materials. However, such CNN models usually rely heavily on a large set of labeled images to ensure the accuracy and generalization ability of the predictive models. Unfortunately, in many fields, acquiring image data is expensive and inconvenient. This study aims to propose a data augmentation technique to enhance the performance of the CNN models for linking microstructural images to the macroscopic properties of composites.Design/methodology/approachMicrostructures of composites are synthesized using discrete element simulations and Potts kinetic Monte Carlo simulations. Macroscopic properties such as the elastic modulus, Poisson's ratio, shear modulus, coefficient of thermal expansion, and triple-phase boundary length density are extracted on representative volume elements. The CNN model is trained using the 3D microstructural images as inputs and corresponding macroscopic properties as the labels. The comparison of the predictive performance of the CNN models with and without data augmentation treatment are compared.FindingsThe comparison between the prediction performance of CNN models with and without data augmentation showed that the former reduced the weighted mean absolute percentage error (WMAPE) for the prediction from 5.1627% to 1.7014%. This significant reduction signifies that the proposed data augmentation method can effectively enhance the generalization ability and robustness of CNN models.Originality/valueThis study demonstrates that data augmentation is beneficial for solving the problems of model overfitting, data scarcity, and sample imbalance for CNN-based deep learning tasks at a low cost. By developing more and advanced data augmentation techniques, deep learning accelerated homogenization will boost the multi-scale computational mechanics and materials.

NFSDense201: microstructure image classification based on non-fixed size patch division with pre-trained DenseNet201 layers

In the field of nanoscience, the scanning electron microscope (SEM) is widely employed to visualize the surface topography and composition of materials. In this study, we present a novel SEM image classification model called NFSDense201, which incorporates several key components. Firstly, we propose a unique nested patch division approach that divides each input image into four patches of varying dimensions. Secondly, we utilize DenseNet201, a deep neural network pretrained on ImageNet1k, to extract 2920 deep features from the last fully connected and global average pooling layers. Thirdly, we introduce an iterative neighborhood component analysis function to select the most discriminative features from the merged feature vector, which is formed by concatenating the four feature vectors extracted per input image. This process results in a final feature vector of optimal length 698. Lastly, we employ a standard shallow support vector machine classifier to perform the actual classification. To evaluate the performance of NFSDense201, we conducted experiments using a large public SEM image dataset. The dataset consists of 972, 162, 326, 4590, 3820, 3925, 4755, 181, 917, and 1624.jpeg images belonging to the following microstructural categories: “biological,” “fibers,” “film-coated surfaces,” “MEMS devices and electrodes,” “nanowires,” “particles,” “pattern surfaces,” “porous sponge,” “powder,” and “tips,” respectively. For both four-class and ten-class classification tasks, we evaluated NFSDense201 using subsets of the dataset containing 5080 and 21,272 images, respectively. The results demonstrate the superior performance of NFSDense201, achieving a four-class classification accuracy rate of 99.53% and a ten-class classification accuracy rate of 97.09%. These accuracy rates compare favorably against previously published SEM image classification models. Additionally, we report the performance of NFSDense201 for each class in the dataset.

CACTUS: a computational framework for generating realistic white matter microstructure substrates.

Monte-Carlo diffusion simulations are a powerful tool for validating tissue microstructure models by generating synthetic diffusion-weighted magnetic resonance images (DW-MRI) in controlled environments. This is fundamental for understanding the link between micrometre-scale tissue properties and DW-MRI signals measured at the millimetre-scale, optimizing acquisition protocols to target microstructure properties of interest, and exploring the robustness and accuracy of estimation methods. However, accurate simulations require substrates that reflect the main microstructural features of the studied tissue. To address this challenge, we introduce a novel computational workflow, CACTUS (Computational Axonal Configurator for Tailored and Ultradense Substrates), for generating synthetic white matter substrates. Our approach allows constructing substrates with higher packing density than existing methods, up to 95% intra-axonal volume fraction, and larger voxel sizes of up to 500μm3 with rich fibre complexity. CACTUS generates bundles with angular dispersion, bundle crossings, and variations along the fibres of their inner and outer radii and g-ratio. We achieve this by introducing a novel global cost function and a fibre radial growth approach that allows substrates to match predefined targeted characteristics and mirror those reported in histological studies. CACTUS improves the development of complex synthetic substrates, paving the way for future applications in microstructure imaging.

QOCT-Net: A Physics-Informed Neural Network for Intravascular Optical Coherence Tomography Attenuation Imaging.

Intravascular optical coherence tomography (IVOCT) provides high-resolution, depth-resolved images of coronary arterial microstructure by acquiring backscattered light. Quantitative attenuation imaging is important for accurate characterization of tissue components and identification of vulnerable plaques. In this work, we proposed a deep learning method for IVOCT attenuation imaging based on the multiple scattering model of light transport. A physics-informed deep network named Quantitative OCT Network (QOCT-Net) was designed to recover pixel-level optical attenuation coefficients directly from standard IVOCT B-scan images. The network was trained and tested on simulation and in vivo datasets. Results showed superior attenuation coefficient estimates both visually and based on quantitative image metrics. The structural similarity, energy error depth and peak signal-to-noise ratio are improved by at least 7%, 5% and 12.4%, respectively, compared with the state-of-the-art non-learning methods. This method potentially enables high-precision quantitative imaging for tissue characterization and vulnerable plaque identification.

Characterizing multi-scale shale pore structure based on multi-experimental imaging and machine learning

An accurate and comprehensive understanding of shale pore structure is fundamental and critical for accurate reserves evaluation and efficient hydrocarbon development. Thus, by taking the shale of Paleogene Eocene Shahejie Formation in the Jiyang Depression, Bohai Bay Basin, as an example, the 2D and 3D multi-resolution images of the shale microstructure are obtained by multiple imaging technologies, including X-ray computed tomography, large-field scanning electron microscopy, scanning electron microscopy and focused ion beam scanning electron microscopy. By integrating image processing and machine learning algorithms, the shale pore structure is characterized at a single scale and multi scales. The results are obtained as follows. First, the shale pore space in the study area is mainly composed of microfractures, inorganic pores, organic matters and organic pores, and exclusively shows multi-scale characteristics. Second, there are various types of inorganic pores, and abundant dissolution pores; organic matters are distributed as strips and patches, and no organic pores are found in some organic matters. Third, pores with radius less than 20 nm account for 25%, those with radius between 20 and 50 nm account for 19%, those with radius between 50 and 100 nm account for 29%, those with radius between 100 and 500 nm account for 14%, those with radius between 500 nm and 20 μm account for 11%, and those with radius between 20 and 50 μm account for 2%. Fourth, the organic pores are less connected than the inorganic pores. The connectivity between organic pores and inorganic pores plays a crucial role in hydrocarbon migration, and microfractures control fluid flow channels. Fifth, pores with radius less than 50 nm are dominantly organic pores, those with radius between 50 and 500 nm are mainly organic and inorganic pores, and microfractures mainly contribute to the pores with radius more than 500 nm. It is concluded that a single imaging experiment cannot accurately and comprehensively reveal the multi-scale micro pore structure of a shale reservoir. Through integration of multiple imaging technologies and machine learning algorithms, the shale pore structure can be recognized and characterized at both single scale and multi scales. The proposed new method provides accurate and comprehensive information of multi-scale pore structures.

A framework for predicting grain morphology during incremental sheet metal forming using generative adversarial networks

Properties and service performance of parts and components depend upon microstructural characteristics including grain morphology, size and size distribution, and crystallographic orientation. Microstructure itself is dictated by manufacturing process parameters. Microstructural analysis is often necessary for evaluating product quality. Past research has attempted to map product/process characteristics using analytical, numerical, and experimental approaches. To address the limitations of conventional modeling approaches, researchers have begun to explore the application of artificial intelligence (AI) techniques in correlating and predicting the effect of process parameters on material microstructure and mechanical properties. For instance, AI-based approaches have been used to model the influence of process parameters on forming force, formability, geometric accuracy, tool path, and surface quality in incremental sheet forming (ISF), a flexible forming process suitable for low-volume and custom product manufacturing. The research reported herein introduces a framework to map ISF process parameters to microstructural images using Generative Adversarial Networks (GAN). This approach can reduce the experimental and analytical effort and costs typically necessary to evaluate the effects of ISF process settings on material microstructure and mechanical properties.

DL-MSTO+: A deep learning-based multi-scale topology optimization framework via positive definiteness ensured material representation network

A multi-scale approach to topology optimization has recently emerged due to its lightweight, robust, and multi-functional characteristics. Considering material diversity, an increasing number of materials leads to a computational burden due to numerous design variables and homogenization equations. This study proposes DL-MSTO+, a deep learning-based multi-scale topology optimization framework. The framework consists of two distinct deep neural networks for multi-scale topology optimization problems to reduce the dimensionality of design variables and predict homogenized material properties. First, a generator network learns the low-dimensional representation of a material microstructure in an unsupervised manner. Thus, the trained generator produces a micro-structural image from a low-dimensional vector. Second, a predictor network is trained in a supervised manner to predict the homogenized elasticity matrix for a given micro-structural image. Additionally, the predictor is specifically designed to ensure the positive definiteness of the predicted elasticity matrix. Lastly, the networks are integrated into the algorithm to improve the efficiency of multi-scale topology optimization. The numerical experiments demonstrate higher efficiency for the proposed algorithm than the conventional multi-scale approach. Moreover, the proposed method provides connectable multi-scale designs, and the low-dimensional latent representation enables semantic interpolation between solutions.

Maximal sparse convex surrogate-assisted evolutionary convolutional neural architecture search for image segmentation

Designing reasonable architectures of convolutional neural network (CNN) for specific image segmentation remains a challenging task, as the determination of the structure and hyperparameters of CNN depends heavily on expertise and requires a great deal of time. Evolutionary algorithm (EA) has been successfully applied to the automatic design of CNNs; however, the inherent stochastic search of EA tends to cause “experience loss” and requires very large computational resources. To deal with this problem, a maximal sparse convex surrogate model with updated empirical information is proposed in this paper to guide the evolutionary process of CNN design. This sparse convex function is transformed from a non-convex function to a maximized sparse convex function, which can better utilize the prior empirical knowledge to assist the evolutionary search. In addition, a balance strategy between computational resources and accuracy is proposed in the selection of reasonable network architectures. The proposed fully automatic design method of CNN is applied to the segmentation of steel microstructure images, and experimental results demonstrate that the proposed method is competitive with the existing state-of-the-art methods.

Methodology for Collecting and Aligning Correlative SEM, CLSM and LOM Images of Bulk Material Microstructure to Create a Large Machine Learning Training Dataset.

Methodology for Collecting and Aligning Correlative SEM, CLSM and LOM Images of Bulk Material Microstructure to Create a Large Machine Learning Training Dataset.

Deep Learning Design of Graphene-Reinforced Polyurethane Foams from SEM Microstructure Images and Style-based Generative Adversarial Networks.

Deep Learning Design of Graphene-Reinforced Polyurethane Foams from SEM Microstructure Images and Style-based Generative Adversarial Networks.

Exploiting the use of deep learning techniques to identify phase separation in self-assembled microstructures with localized graphene domains in epoxy blends

In this study for the first time, convolutional neural networks (CNN) are applied to identify phase-separated microstructures in a novel nano-modified polymer composite. The input data consist of a mixed labelled dataset of homogeneous microstructure and phase-separated microstructure images collected using an optical microscope; the problem is modelled as a binary classification. The initial dataset consisted of microstructural images collected in house from past experiments, expanded in number using the data augmentation method, and upon testing, the model showed an accuracy of 65.4% and a 0.5 F1 score. Additional experiments were performed to generate more ground truth images and the CNN model built on this second data set showed improved accuracy of 80.1% and a 0.9 F1 score; The CNN classifier mimicked the receiver operating characteristic (ROC) curve of perfect classifier. Using the trained model, the phase separated microstructure is distinguished from homogeneous microstructure in a matter of minutes, saving hours of manual screening and cost intensive characterisation.

The Variegation of Human Brain Vulnerability to Rare Genetic Disorders and Convergence With Behaviorally Defined Disorders

BackgroundDiverse gene dosage disorders (GDDs) increase risk for psychiatric impairment, but characterization of GDD effects on the human brain has so far been piecemeal, with few simultaneous analyses of multiple brain features across different GDDs. MethodsHere, through multimodal neuroimaging of 3 aneuploidy syndromes (XXY [total n = 191, 92 control participants], XYY [total n = 81, 47 control participants], and trisomy 21 [total n = 69, 41 control participants]), we systematically mapped the effects of supernumerary X, Y, and chromosome 21 dosage across a breadth of 15 different macrostructural, microstructural, and functional imaging–derived phenotypes (IDPs). ResultsThe results revealed considerable diversity in cortical changes across GDDs and IDPs. This variegation of IDP change underlines the limitations of studying GDD effects unimodally. Integration across all IDP change maps revealed highly distinct architectures of cortical change in each GDD along with partial coalescence onto a common spatial axis of cortical vulnerability that is evident in all 3 GDDs. This common axis shows strong alignment with shared cortical changes in behaviorally defined psychiatric disorders and is enriched for specific molecular and cellular signatures. ConclusionsUse of multimodal neuroimaging data in 3 aneuploidies indicates that different GDDs impose unique fingerprints of change in the human brain that differ widely depending on the imaging modality that is being considered. Embedded in this variegation is a spatial axis of shared multimodal change that aligns with shared brain changes across psychiatric disorders and therefore represents a major high-priority target for future translational research in neuroscience.

Comparison of Cold-Sprayed Coatings of Copper-Based Composite Deposited on AZ31B Magnesium Alloy and 6061 T6 Aluminum Alloy Substrates.

Copper-coated graphite and copper mixture powders were deposited on AZ31B magnesium alloy and 6061 T6 aluminum alloy substrates under different process parameters by a solid-state cold spray technique. The microstructure of the copper-coated graphite and copper composite coatings was visually examined using photographs taken with an optical microscope and a scanning electron microscope. The surface roughness of the coatings was investigated with a 3D profilometer. The thickness of the coatings was determined through the analysis of the microstructure images, while the adhesion of the coatings was characterized using the scratch test method. The results indicate that the surface roughness of the coatings sprayed on the two different substrates gradually decreases as gas temperature and gas pressure increase. Additionally, the thickness and adhesion of the coatings deposited on the two different substrates both increase with an increase in gas temperature and gas pressure. Comparing the surface roughness, thickness, and adhesion of the coatings deposited on the two different substrates, the surface roughness and adhesion of the coatings on the soft substrate are greater than those of the coatings on the hard substrate, while the thickness of the coatings is not obviously affected by the hardness of the substrate. Furthermore, it is noteworthy that the surface roughness, thickness, and adhesion of the copper-coated graphite and copper composite coatings sprayed on the two different substrates exhibit a distinct linear relationship with particle velocity.

Prediction of the effective viscoelastic properties of polymer-based microstructure with randomly-placed linear elastic inclusions using convolutional neural network

This work proposes a prediction of the effective transverse relaxation modulus of a two-phase material with randomly-placed linear elastic circular inclusions inside a linear viscoelastic matrix using a microstructure image of a representative volume element (RVE) and a convolutional neural network. For this purpose, 1900 RVEs with volume fractions ranging from 20 % to 65 % are generated using the random sequential expansion (RSE) algorithm. The RVEs are then simulated under a stress relaxation test using the finite element (FE) method. Then, by applying homogenization-based methods, the effective transverse relaxation modulus of the RVE, which represents the properties of the whole material, is obtained. The validity of the results of the FE simulation is assessed by mesh convergence check and comparison with numerical data available in the literature. Next, data augmentation is utilized to make the dataset four times larger by flipping the RVE images in vertical, horizontal, and both directions to have a dataset containing 7600 images of RVE and their effective relaxation modulus. In the following step, 80 % of the dataset is randomly selected and used to train a convolutional neural network (CNN) to establish an ability for the network to predict the effective relaxation modulus of the RVE. The other 20 % of the dataset is employed to measure the accuracy of the proposed CNN model by three different cost functions. It is observed that the CNN model predictions are in excellent agreement with the results obtained from FE simulations while requiring much lower computational and experimental time and cost. The proposed convolutional neural network model in this study provides a powerful tool to accurately predict the effective viscoelastic properties of heterogeneous materials.