Search Results (24)

Characterizing the microstructures of steel subjected to heat treatments is crucial in the metallurgical industry for understanding and controlling their mechanical properties. In this study, we present a novel approach for generating images of steel microstructures that mimic those obtained with optical microscopy, using the deep learning technique of generative adversarial networks (GAN). The experiments were conducted using different hyperparameter configurations, evaluating the effect of these variations on the quality and fidelity of the generated images. The obtained results show that the images generated by artificial intelligence achieved a resolution of 512 × 512 pixels and closely resemble real microstructures observed through conventional microscopy techniques. A precise visual representation of the main microconstituents, such as pearlite and ferrite in annealed steels, was achieved. However, the performance of GANs in generating images of quenched steels with martensitic microstructures was less satisfactory, with the synthetic images not fully replicating the complex, needle-like features characteristic of martensite. This approach offers a promising tool for generating steel microstructure images, facilitating the visualization and analysis of metallurgical samples with high fidelity and efficiency. Full article

Permeability is a fundamental property of porous media. It quantifies the ease with which a fluid can flow under the effect of a pressure gradient in a network of connected pores. Porous materials can be natural, such as soil and rocks, or synthetic, such as a densified network of fibres or open-cell foams. The measurement of permeability is difficult and time-consuming in heterogeneous and anisotropic porous media; thus, a numerical approach based on the calculation of the tensor components on a 3D image of the material can be very advantageous. For this type of microstructure, it is important to perform calculations on large samples using boundary conditions that do not suppress the transverse flows that occur when flow is forced out of the principal directions. Since these are not necessarily known in complex media, the permeability determination method must not introduce bias by generating non-physical flows. A new finite element-based method proposed in this study allows us to solve very high-dimensional flow problems while limiting the biases associated with boundary conditions and the small size of the numerical samples addressed. This method includes a new boundary condition, full permeability tensor identification based on the multiscale homogenization approach, and an optimized solver to handle flow problems with a large number of degrees of freedom. The method is first validated against academic test cases and against the results of a recent permeability benchmark exercise. The results underline the suitability of the proposed approach for heterogeneous and anisotropic microstructures. Full article

This study presents the results of applying the methods of synthesizing mesoporous carbon and mesoporous polymer materials with an extended porous mesostructure as adsorbents for cationic dye molecules. Both types of adsorbents are synthetic materials. The aim of the presented research was the preparation, characterisation, and utilisation of obtained mesoporous adsorbents. The physicochemical properties, morphology, and porous structure characteristics of the obtained materials were determined using low-temperature nitrogen sorption isotherms, X-ray diffraction (XRD), small angle X-ray scattering (SAXS), and potentiometric titration measurements. The morphology and microstructure were imaged using scanning electron microscopy (SEM). The chemical characterisation of the surface chemistry of the adsorbents, which provides information about the surface-active groups, the elemental composition, and the electronic state of the elements, was carried out using X-ray photoelectron spectroscopy (XPS). The adsorption properties of the mesoporous materials were determined using equilibrium and kinetic adsorption experiments for three selected cationic dyes (derivatives of thiazine (methylene blue) and triarylmethane (malachite green and crystal violet)). The adsorption capacity was analysed to the nanostructural and surface properties of used materials. The Generalized Langmuir equation was applied for the analysis of adsorption isotherm data. The adsorption study showed that the carbon materials have a higher sorption capacity for both methylene blue and crystal violet, e.g., 0.88–1.01 mmol/g and 0.33–0.44 mmol/g, respectively, compared to the polymer materials (e.g., 0.038–0.044 mmol/g and 0.038–0.050 mmol/g, respectively). The kinetics of dyes adsorption was closely correlated with the structural properties of the adsorbents. The kinetic data were analysed using various equations: first-order (FOE), second-order (SOE), mixed 1,2-order (MOE), multi-exponential (m-exp), and fractal-like MOE (f-MOE). Full article

In the existing deep learning modeling process for material microstructure image segmentation, the manual pixel labeling process is time-consuming and laborious. In order to achieve fast and high-accuracy modeling, this work proposes a convenient deep learning labeling method and a workflow for generating a synthetic image data set. Firstly, a series of label templates was prepared by referring to the distribution of the material microstructure. Then, the typical textures of different microstructures were box-selected in the images to be segmented to form texture templates. The manual pixel labeling was simplified to the box-selection of the typical microstructure texture. Finally, a synthetic data set can be generated using the label and texture templates for further deep learning model training. Two image cases containing multiple types of microstructures were used to verify the labeling method and workflow. The results show that the pixel segmentation accuracy of the deep learning model for the test images reaches 95.92% and 95.40%, respectively. The modeling workflow can be completed within 20 min, and the labeling time that requires manual participation is within 10 min, significantly reducing the modeling time compared to traditional methods where the labeling process may take several hours. Full article

The growing awareness of the environment and sustainable development has prompted the search for solutions involving the development of bio-based composite materials for insulating applications, offering an alternative to traditional synthetic materials such as glass- and carbon-reinforced composites. In this study, we investigate the thermal and microstructural properties of new biocomposite insulating materials derived from flaxseed-gum-filled epoxy, with and without the inclusion of reinforced flax fibers. A theoretical approach is proposed to estimate the thermal conductivity, while the composite’s microstructure is characterized using X-ray Computed Tomography and image analysis. The local thermal conductivity of the flax fibers and the flaxseed gum matrix is identified by using effective thermal conductivity measurements and analytical models. This study provides valuable insight into the thermal behavior of these biocomposites with varying compositions of flaxseed gum and epoxy resin. The results obtained could not only contribute to a better understanding the thermal properties of these materials but are also of significant interest for advanced numerical modeling applications. Full article

In this study, corrosion and wear tests of NiTi alloy (Ni 55%–Ti 45%) samples, known as shape memory alloy, which offer a shape recovery memory effect between memory temperatures ranging from 25 to 35 °C, have been carried out. The standard metallographically prepared samples’ microstructure images were obtained using an optical microscope device and SEM with an EDS analyzer. For the corrosion test, the samples are immersed with a net into the beaker of synthetic body fluid, whose contact with the standard air is cut off. Electrochemical corrosion analyses were performed after potentiodynamic testing in synthetic body fluid and at room temperature. The wear tests of the investigated NiTi superalloy were carried out by performing reciprocal wear tests under 20 N and 40 N loads in a dry environment and body fluid. During wear, a 100CR6-quality steel ball of the counter material was rubbed on the sample surface for a total of 300 m with a unit line length of 13 mm and a sliding speed of 0.04 m/s. As a result of both the potentiodynamic polarization and immersion corrosion tests in the body fluid, an average of 50% thickness reduction in the samples was observed in proportion to the change in the corrosion current values. In addition, the weight loss of the samples in corrosive wear is 20% less than that in dry wear. This can be attributed to the protective effect of the oxide film on the surface at high loads and the effect of reducing the friction coefficient of the body fluid. Full article

A modified SliceGAN architecture was proposed to generate a high-quality synthetic three-dimensional (3D) microstructure image of TYPE 316L material manufactured through additive methods. The quality of the resulting 3D image was evaluated using an auto-correlation function, and it was discovered that maintaining a high resolution while doubling the training image size was crucial in creating a more realistic synthetic 3D image. To meet this requirement, modified 3D image generator and critic architecture was developed within the SliceGAN framework. Full article

The greatest challenge when using deep convolutional neural networks (DCNNs) for automatic segmentation of microstructural X-ray computed tomography (XCT) data is the acquisition of sufficient and relevant data to train the working network. Traditionally, these have been attained by manually annotating a few slices for 2D DCNNs. However, complex multiphase microstructures would presumably be better segmented with 3D networks. However, manual segmentation labeling for 3D problems is prohibitive. In this work, we introduce a method for generating synthetic XCT data for a challenging six-phase Al–Si alloy composite reinforced with ceramic fibers and particles. Moreover, we propose certain data augmentations (brightness, contrast, noise, and blur), a special in-house designed deep convolutional neural network (Triple UNet), and a multi-view forwarding strategy to promote generalized learning from synthetic data and therefore achieve successful segmentations. We obtain an overall Dice score of 0.77. Lastly, we prove the detrimental effects of artifacts in the XCT data on achieving accurate segmentations when synthetic data are employed for training the DCNNs. The methods presented in this work are applicable to other materials and imaging techniques as well. Successful segmentation coupled with neural networks trained with synthetic data will accelerate scientific output. Full article

Gold nanoparticles (AuNPs) with interesting optical properties have attracted much attention in recent years. The synthesis and plasmonic properties of AuNPs with a controllable size and shape have been extensively investigated. Among these AuNPs, gold nanotriangles (AuNTs) exhibited unique optical and plasmonic properties due to their special triangular anisotropy. Indeed, AuNTs showed promising applications in optoelectronics, optical sensing, imaging and other fields. However, only few reviews about these applications have been reported. Herein, we comprehensively reviewed the synthesis and self-assembly of AuNTs and their applications in recent years. The preparation protocols of AuNTs are mainly categorized into chemical synthesis, biosynthesis and physical-stimulus-induced synthesis. The comparison between the advantages and disadvantages of various synthetic strategies are discussed. Furthermore, the specific surface modification of AuNTs and their self-assembly into different dimensional nano- or microstructures by various interparticle interactions are introduced. Based on the unique physical properties of AuNTs and their assemblies, the applications towards chemical biology and sensing were developed. Finally, the future development of AuNTs is prospected. Full article

This study examines the characteristics, statistics, and mixing effects of internal solitary waves (ISWs) observed in the northern Yellow Sea (YS) during the summers of 2018 and 2019. The mooring stations are located between offshore islands with rough topographic features. Throughout the observation period, the ISWs with vertical displacements of up to 10 m induced prevailing high-frequency (3–10 min period) temperature variations. Synthetic aperture radar (SAR) images showed that the observed ISWs propagate in zonal directions generated around the islands where internal-tide-generating body force is strong. The estimated ISW propagation speed ranges from 0.16 to 0.25 m s⁻¹, which agrees with the Korteweg-de Vries (KdV) model. The ISW intensity exhibits a clear spring-neap cycle corresponding to the local tidal forcing. The constant occurrence of ISWs at low tide suggests an important generation site where the ISWs are tidally generated. The ray-tracing result indicates that this generation site appears to be located at a strait between Dahao and Xiaohao islands. A generalized KdV model successively reproduces the propagation process from the generation site to the mooring station. Following the passage of ISWs, microstructure profiling observations reveal a high turbulent kinetic energy dissipation rate (10⁻⁶ W kg⁻¹). The prevalence of ISWs in the study area is believed to play a crucial role in regulating vertical heat and nutrient transport, thereby modulating the biogeochemical cycle. Full article

Modification of surface structure for the promotion of food safety and health protection is a technology of interest among many industries. With this study, we aimed specifically to develop a tenable solution for the fabrication of self-cleaning biomimetic surface structures for agricultural applications such as post-harvest packing materials and greenhouse cover screens. Phytopathogenic fungi such as Botrytiscinerea are a major concern for agricultural systems. These molds are spread by airborne conidia that contaminate surfaces and infect plants and fresh produce, causing significant losses. The research examined the adhesive role of microstructures of natural and synthetic surfaces and assessed the feasibility of structured biomimetic surfaces to easily wash off fungal conidia. Soft lithography was used to create polydimethylsiloxane (PDMS) replications of Solanum lycopersicum (tomato) and Colocasia esculenta (elephant ear) leaves. Conidia of B. cinerea were applied to natural surfaces for a washing procedure and the ratios between applied and remaining conidia were compared using microscopy imaging. The obtained results confirmed the hypothesis that the dust-repellent C. esculenta leaves have a higher conidia-repellency compared to tomato leaves which are known for their high sensitivities to phytopathogenic molds. This study found that microstructure replication does not mimic conidia repellency found in nature and that conidia repellency is affected by a mix of parameters, including microstructure and hydrophobicity. To examine the effect of hydrophobicity, the study included measurements and analyses of apparent contact angles of natural and synthetic surfaces including activated (hydrophilic) surfaces. No correlation was found between the surface apparent contact angle and conidia repellency ability, demonstrating variation in washing capability correlated to microstructure and hydrophobicity. It was also found that a microscale sub-surface (tomato trichromes) had a high conidia-repelling capability, demonstrating an important role of non-superhydrophobic microstructures. Full article

The study of the micromechanical performance of materials is important in explaining their macrostructural behavior, such as fracture and fatigue. This paper is aimed, among other things, at reducing the deficiency of microstructural models of grey cast irons in the literature. For this purpose, a numerical modeling approach based on the crystal plasticity (CP) theory is used. Both synthetic models and models based on scanning electron microscope (SEM) electron backscatter diffraction (EBSD) imaging finite element are utilized. For the metal phase, a CP model for body-centered cubic (BCC) crystals is adopted. A cleavage damage model is introduced as a strain-like variable; it accounts for crack closure in a smeared manner as the load reverses, which is especially important for fatigue modeling. A temperature dependence is included in some material parameters. The graphite phase is modeled using the CP model for hexagonal close-packed (HCP) crystal and has a significant difference in tensile and compressive behavior, which determines a similar macro-level behavior for cast iron. The numerical simulation results are compared with experimental tensile and compression tests at different temperatures, as well as with fatigue experiments. The comparison revealed a good performance of the modeling approach. Full article

The strong demand for plastic and polymeric materials continues to grow year after year, making these industries critical to address sustainability. By functioning as a filler in either a synthetic or natural starch matrix, nanoclay enables significant reductions in the impact of nonbiodegradable materials. The effect of treated nanoclay (NC) loading on the mechanical and morphological properties (EP) of epoxy is investigated in this research. The NC-EP nanocomposites were prepared via casting. The investigation begins with adding NC at concentrations of 1, 2, and 3 weight percent, followed by the effect of acid treatment on the same nanocomposites. The evaluation is focused on four mechanical tensile strength parameters: Young’s modulus, maximum load, and % elongation. The addition of NC improved the mechanical properties of the four components by 27.2%, 33.38%, 46.98%, and 43.58%, respectively. The acid treatment improved 35.9%, 42.8%, 51.1%, and 83.5%, respectively. These improvements were attributed to NC’s ability to alter the structural morphology as assessed by field emission scanning electron microscopy (FESEM), a tool for analysing the microstructure. FESEM images were used to visualise the interaction between the NC and EP nanocomposites. The dynamic mechanical properties of the hybrid nanocomposites were investigated using storage modulus, loss modulus, and tan(delta). The results have shown that the viscoelastic properties improved as the fraction of NC increased. The overall findings suggest that these nanocomposites could be used in various industrial and biomedical applications. Full article

The O/W isocyanate emulsion can be used as a sizing agent to improve the waterproof performance of paper. However, the -NCO content in the emulsion diminishes with the prolongation of standing time. What is happening to this seemingly stable emulsion, especially concerning its microstructure evolution? We propose to monitor the emulsions deteriorative process by combining freeze-drying technique and SEM. Thus, the emulsion containing -NCO active group was obtained by the synthetic polymer emulsification of HDI trimers. The results of SEM demonstrate that the emulsion deteriorative process actually represents the collapsing and fusion of stable honeycomb structure with the prolongation of standing time and increasing temperature. This is possibly due to the fact that the inner aggregative HDI trimers are reacting with outside water to form urethane macromolecules, and this results in the collapsing and fusion of the honeycomb structure, as observed in SEM images. Moreover, the measurement results of -NCO content and FT-IR spectroscopy present the -NCO content as reducing with increasing standing time and temperature. This conclusion further proves our hypotheses. Additionally, the emulsions are used to treat the paper by site-directed reaction. The results show that the with the increase of the standing time and temperature, the contact angles and surface free energy show a decrease and an increase, respectively, whereas surface free energy appeared at a minimum of 29.19 mJ·m⁻² when the standing time and temperature was 1 h and 25 °C. Full article

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 $R^2\approx 0.96$ 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. Full article