Metallographic Images Research Articles

Microstructure-based crystal plasticity modeling of AA2024-T3 aluminum alloy defined as the α-Al, θ-Al2Cu, and S-Al2CuMg phases based on real metallographic image

The increase in the strength of the Al–Cu–Mg alloys is to a large extent due to the formation and distribution of submicroscopic θ-Al2Cu and S-Al2CuMg phases in the aluminum (α-Al) matrix, which causes these alloys to have unique properties such as high initial hardening rate. This paper aimed to examine the anisotropic plasticity and flow behavior of the AA2024-T3 aluminum alloy by measuring the distinctive properties of each of the θ-Al2Cu, S-Al2CuMg, and α-Al phases at the micro-level and using a microstructure-based crystal plasticity model during single-strand tensile strength. The real microstructure of AA2024-T3 aluminum alloy obtained from scanning electron microscopy (SEM) was utilized in the calculations using the polycrystalline image processing method of the phases as a Representative Volume Element (RVE). Each phase’s crystal orientations and texture were randomly generated, given the lattice parameters and their crystal structure. The computational results were compared with the experimental data of the AA2024-T3 aluminum alloy tensile test, the data of the crystal plasticity model without considering the microstructure, and the Johnson-Cook model. Given the heterogeneity of the microstructure and crystal orientations of the grains, it was shown that the maximum internal stress under tensile loading occurred along with local hardening in the α-Al phase adjacent to the grains of the θ-Al2Cu and S-Al2CuMg phases, which was about 43300 MPa.

A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges

Image segmentation is an important issue in many industrial processes, with high potential to enhance the manufacturing process derived from raw material imaging. For example, metal phases contained in microstructures yield information on the physical properties of the steel. Existing prior literature has been devoted to develop specific computer vision techniques able to tackle a single problem involving a particular type of metallographic image. However, the field lacks a comprehensive tutorial on the different types of techniques, methodologies, their generalizations and the algorithms that can be applied in each scenario. This paper aims to fill this gap. First, the typologies of computer vision techniques to perform the segmentation of metallographic images are reviewed and categorized in a taxonomy. Second, the potential utilization of pixel similarity is discussed by introducing novel deep learning-based ensemble techniques that exploit this information. Third, a thorough comparison of the reviewed techniques is carried out in two openly available real-world datasets, one of them being a newly published dataset directly provided by ArcelorMittal, which opens up the discussion on the strengths and weaknesses of each technique and the appropriate application framework for each one. Finally, the open challenges in the topic are discussed, aiming to provide guidance in future research to cover the existing gaps.

Pseudo Generation of Metallographic Images and Verification of Superiority for Discrimination Problems -Applying Adversarial Generative Networks-

Pseudo Generation of Metallographic Images and Verification of Superiority for Discrimination Problems -Applying Adversarial Generative Networks-

Processing and analysis of images of microstructure metals for determining the grain point

An algorithmic support for metallographic images preprocessing and analysis is presented. The software product implements metallographic methods for the grain size determination by comparison of rating scales, counting beans, calculation of grain boundaries intersections for equiaxed and elongated grains, measuring a chords length. Multiple digital images can be used as initial data. Pre-processing is used to remove noise, sharpen and improve contrast using Adaptive Contrast-Limiting Histogram Equalization (CLAHE). The next step is grain segmentation. A combination of distance transform and adaptive watershed binarization is used. Binary images filtration based on the operations of mathematical morphology is provided. Contour analysis is used to determine grain boundaries. The study’s results of the entire rating scales and on the real metallographic images are presented. High efficiency of an algorithmic support is confirmed by the experiments. The software implementation has the following main features: the ability to calibrate the actual grain size, automatic or manual image preprocessing, grain size analysis with saving the results as a report in jpg format. Batch processing provides the ability to download images for processing with the same type of algorithm.

Strengthening Thin-Walled Knives with Nitrogen Plasma

Introduction. The completed developments are aimed at creating a new technology for increasing the wear resistance of a thin-walled instrument of complex configuration made of steel 65G for cutting beets at sugar enterprises. The most important requirement to improve the operability and durability of such a tool is the preservation of its profile and cutting edge during operation. Materials and Methods. A new developed equipment and technological process of strengthening using low-temperature nitrogen plasma were used to solve this problem. There have been determined optimal processing parameters that ensure the formation of a quasi-morphic structure on the friction surface that reduces the defectiveness of the cutting edge after its machining and also provides a process of self-sharpening due to tool strengthening on one side. Results. The comparative studies of the friction surface of products after operational tests have shown that their resistance increase significantly when strengthening both new and used products. This is determined by the nature of the quasi-morphic structure formed and the specific relief in friction on the working surface. Discussion and Conclusion. To describe the new process of strengthening thin-walled products, the structure formation on the friction surface was analyzed in detail with the use of metallographic images and its phase relationship variability was estimated by the optic-mathematical analysis of various zones (compression and vacuum) formed as friction bands. This was done trough modeling with the estimation of the distribution density of the conditional colors of the analyzed fragments.

ESTIMATION OF LOCAL INHOMOGENEITY OF MULTILAYER NANOSTRUCTURED ZrN/ZrO2 COATING

Multilayer nanostructured ZrN/ZrO2 coatings were applied to increase the operational resistance of various machinery parts by using vacuum-arc deposition in Bulat-type facility. To describe the structure formation, a new approach based on optical-mathematical method for processing metallographic images is proposed. The structure formation of the multilayer coating with an assessment of the degree of its inhomogeneity and diffusion processes between the layers is studied. For a reliable assessment, the changes in the horizontal and vertical directions of the images with the choice of optimal intervals were comparatively analyzed. It has been found that the most stable results are achieved using 20 and 25 dots (pixels).

Artificial vision applied to manufacturing process

This article presents the results of the implementation of artificial vision and image processing projects applied to a parts manufacturing process. In a first scenario, three-dimensional computational geometric models of the parts to be manufactured were obtained from the capture and processing of thermo-graphic images. In a later stage of the manufacturing process, a direct measurement system for cutting tool wear was implemented using HD images with image processing software using NI Lab VIEW from National Instruments®. In the final stage of parts manufacturing, the development of software (UDmetaL Handbook) for the quantitative metallographic analysis of micrographs for hypoeutectoid steels is presented. The software determines the type of steel based on the percentage of carbon and finds the mechanical properties of the material in a database that is incorporated into the software. The software's basis of operation is computational analysis through digital processing of metallographic images obtained through experimental laboratory processes using NI Lab VIEW software from National Instruments®.

Modeling and simulation of dissolution process of bulk carbide in Fe–1C–1.44Cr low-alloy steel

Because of the complex morphology of bulk carbides in the core of as-cast bearing steel, their dissolution process is complicated to model, since it is difficult to rationally simplify the bulk carbides using a simple geometric shape. In this study, a metallographic image of an actual bulk carbide is transformed into a two-dimensional physical model. A dissolution model for such bulk carbides is established using thermodynamic and kinetic methods, where the diffusion coefficients for carbon and chromium in austenite and the solute concentrations at the phase interface are calculated assuming local equilibrium conditions. The calculation results from the model are compared with the experimental values, after the bearing steel was soaked at 1150 °C, 1175 °C and 1200 °C, the difference between the measured area of bulk carbides in the core and the model calculation results was 65 μm2, 12 μm2 and 30 μm2, respectively.

Quantitative relationship between microstructure and mechanical properties in Nd: YAG transparent ceramics

In this work, stereology and fractals were applied to identify the quantitative relation between stereology parameters, fractal dimension, and mechanical properties of Nd: YAG transparent ceramics sintered at 1750 °C for 8–50 h. Mechanical properties and microstructure of the samples were investigated by using universal testing machine, micro-hardness tester, and scanning electron microscopy (SEM), respectively. When the ceramics were sintered at 1750 °C for 50 h, the compressive strength, flexural strength, and Vickers hardness reached 381.6 ± 5.2 MPa, 275.0 ± 5.5 MPa, and 1330.4 ± 18.5 MPa, respectively. Besides, the fracture toughness of ceramic samples was calculated by Vickers hardness. Micrographs of the sample surface and frequency distribution of crystal grains were analyzed by using metallographic image analyzer software. Findings suggest that compressive strength, flexural strength, and Vickers hardness linearly increase upon an increase in equivalent sphere diameter (D3S). However, compressive strength, flexural strength, and Vickers hardness decrease as a function of specific surface area per unit volume of the grains (SV) and discrete grains (SVP) and mean free distance (λ). Perimeter and area of crystal grains were obtained by using Image-Pro Plus image analysis software. The relationship between the fractal dimension of grain boundary and mechanical properties was analyzed based on the area-perimeter (small-island) method. When the grain boundary fractal dimension is close to 1.0, the geometry of ceramic grains tends to be regular, and mechanical properties of ceramic samples increases.

Quantification of Mechanical Twins in Metallographic Images of Twinning-Induced Plasticity Steels Using a New Image Processing Method

A new image-processing method based on block-gradient segmentation was proposed to measure the volume fraction of mechanical twins in metallographic images. The processing results showed that this automatic method could be used for rapid and effective measure of the twin-volume fractions of twinning-induced plasticity steels in metallographic images under different strain conditions. The entire processing time for each image is relatively short, which would benefit researchers processing a large number of metallographic images. This method may pave a way to develop a simple, effective plug-in to measure rapidly the twin-volume fractions in the software for metallographic observations, and it would be beneficial for preliminary analysis of microstructural evolutions and subsequent characterization of mechanical twins by high-resolution microscopy using electron microscopy.

Effect of Multipass Hot Rolling on the Grain Uniformity of a 316L/EH40 Composite Plate

The grain uniformity of a 316L/EH40 composite plate during multipass forming was studied by numerical simulation and hot rolling tests. In view of the total fixed deformation with a different number of passes, grain size variations on the surface, in the center, and at the interface of the base layer, and those on the surface and at the interface of the clad layer after rolling were simulated and analyzed. The rolling schedule was established to meet the adequate uniformity level of the base and clad layers. The most effective process was verified by the vacuum hot rolling test of a four-layer symmetrical billet. The metallographic images were taken in different locations of the composite plate, and the grain sizes in the images were measured by the cut-point method. Experimental results were compared with simulation data, demonstrating their reliability; the error between them was maintained within 5.5%.

Strain distribution measurement with digital image correlation method by using metallographic image under cyclic loading

Strain distribution measurement with digital image correlation method by using metallographic image under cyclic loading

An Improved Adaptive Weighted Mean Filtering Approach for Metallographic Image Processing

Abstract Background As noise brings great error in the analysis of metallographic images, an adaptive weighted mean filtering method proposed to overcome the shortcomings of the standard mean filtering method. Methods The method used to detect the pulse noise points in the image, and then the modified mean method used to filter out the detected noise points. Patents on metallographic image processing have discussed for the development of the proposed methodology. Results It is shown that filter window can be filtered in comparison with the conventional 3×3, 5×5 and 7×7 filt window to reduce noise detection and reduce the complexity of the weight calculation. Conclusion It can be concluded that this method can better protect the details of the image, has better filtering effect than the standard mean filtering, and its processing speed is faster than the median filtering of the large window, which has profound significance for the edge detection and processing of the metallographic image.

Semi-Supervised Learning Framework for Aluminum Alloy Metallographic Image Segmentation

The purpose of aluminum alloy metallographic image segmentation is to automatically recognize and segment the microstructures of aluminum alloy, which is an important topic in the fields of materials science research and product assessment. In order to achieve satisfactory segmentation results, we always need to label each pixel in metallographic image. This labeling work is very costly in terms of time and human effort. In this paper, we propose a semi-supervised learning framework which is able to use a small number of labeled images to achieve outstanding performance. Based on this framework, we first implement a semi-supervised segmentation method for aluminum alloy metallographic images. The U-Net is used as the basic segmentation model in this method. Second, we propose a self-paced semi-supervised segmentation method to reduce the interfere caused by the mislabeled images. To further improve the segmentation performance, we embed the improved pseudo-label selection strategies and loss functions in our framework, and implement four improved semi-supervised segmentation methods. In addition, we construct a new aluminum alloy metallographic image dataset to evaluate the proposed semi-supervised learning framework. The experiments indicate that our method is able to achieve very competitive results with a small number of labeled images compared to the state-of-the-art supervised method using a large number of pixel-level labels.

Single event effect and total dose effect of GaN high electron mobility transistor using heavy ions and gamma rays

The single event effect (SEE) and the total ionizing dose (TID) effect of a commercial enhancement mode gallium nitride (GaN) high electron nobility transistor (HEMT) with p-type gate structure and cascode structure are studied by using the radiation of heavy ions and <sup>60</sup>Co gamma in this paper. The safe operating areas ofSEE, the sensitive parameters degradation of TID effect and the SEE and TID characteristics of GaN HEMT device are respectively presented. The experimental results show that the SEE and TID effect have less influence on the p-type gate GaN device. The linear energy transfer (LET) threshold of the single event Burnout effect (SEB) is higher than 37 MeV·cm<sup>2</sup>/mg and the failure threshold of TID effect is above 1M rad (Si) for p-type gate GaN device. However, the GaN HEMT device with cascode structure is much more sensitive to SEE and TID effect than p-type gate GaN device. Under heavy ions at LET of 22 MeV·cm<sup>2</sup>/mg and a cumulative dose of 200 krad (Si), the SEB phenomenon and parameters-degradation of cascode-type GaN HEMT are respectively observed. Besides, the circuit structure of the cascode-type GaN HEMT device is analyzed by using metallographic microscope imaging and focused ions beam technology. It reveals the possible reason why it is sensitive to SEB and TID for cascode-type GaN HEMT. These results show that the extra carriers caused by heavy ion radiation can tunnel the Schottky barrier formed by gate metal and AlGaN layer, leading to a large source-drain current in GaN HEMT device. Meanwhile, it is shown that the metal oxide semiconductor field-effect transistor in cascode circuit for TP90H180PS GaN HEMT may be the main reason why the cascode-type GaN HEMT is sensitive to TID.

Quantitative Analysis of Metallographic Image Using Attention-Aware Deep Neural Networks.

As a detection tool to identify metal or alloy, metallographic quantitative analysis has received increasing attention for its ability to evaluate quality control and reveal mechanical properties. The detection procedure is mainly operated manually to locate and characterize the constitution in metallographic images. The automatic detection is still a challenge even with the emergence of several excellent models. Benefiting from the development of deep learning, with regard to two different metallurgical structural steel image datasets, we propose two attention-aware deep neural networks, Modified Attention U-Net (MAUNet) and Self-adaptive Attention-aware Soft Anchor-Point Detector (SASAPD), to identify structures and evaluate their performance. Specifically, in the case of analyzing single-phase metallographic image, MAUNet investigates the difference between low-frequency and high-frequency and prevents duplication of low-resolution information in skip connection used in an U-Net like structure, and incorporates spatial-channel attention module with the decoder to enhance interpretability of features. In the case of analyzing multi-phase metallographic image, SASAPD explores and ranks the importance of anchor points, forming soft-weighted samples in subsequent loss design, and self-adaptively evaluates the contributions of attention-aware pyramid features to assist in detecting elements in different sizes. Extensive experiments on the above two datasets demonstrate the superiority and effectiveness of our two deep neural networks compared to state-of-the-art models on different metrics.

Processing of Spark Plasma Sintered Fe Alloy and Enhancing Its Surface Properties by AlCrN Monolayer Coating by Cathodic Arc Plasma Physical Vapor Deposition Process

The current investigation observes the outcome of enhancing the surface properties by AlCrN monolayer coating using the cathodic arc plasma method on the Fe–Cu–C–Mo alloys. The compacts were sintered in spark plasma sintering (SPS) with the heat transfer rate of 100 °C/min at 1120 °C for 5 minutes. The Fe–2Cu–0.8C–0.6Mo sample has the highest relative sintered density (97.20%), hardness (96 HRB), and ultimate tensile strength (1000 MPa) compare with the other sintered compacts. AlCrN coating was deposited on Fe, Fe–2Cu, Fe–2Cu–0.8C, Fe–2Cu–0.8C–0.2Mo, Fe–2Cu–0.8C–0.4Mo, and Fe–2Cu–0.8C–0.6Mo samples, using the cathodic arc plasma–physical vapor deposition (CAP-PVD) process. The coated compact samples’ metallography images were examined using a Scanning Electron Microscope (SEM); the Fe–2Cu alloy sintered sample has obtained a uniform structure with high density and a smaller amount of corrosion penetration rate (0.6579 mmpy) as compared to the counterparts. The phase formed in the AlCrN coating was analyzed using the X-ray Diffraction (XRD). The Fe–2Cu–0.8C–0.6Mo coated compact sample exhibited higher hardness (1134.85 HV0.3) than the other coated compact samples. The Fe–2Cu–0.8C–0.2Mo coated compact sample has proven better corrosion resistance compared to the other coated compact sample.

Assessment of High-Temperature Hydrogen Attack Using Advanced Ultrasonic Array Techniques

The ability to measure early-stage high-temperature hydrogen attack (HTHA) has been improved by the use of optimized ultrasonic array probes and techniques. First, ultrasonic modeling and simulations were performed to design a set of array probes. The data was then collected using phased array ultrasonic testing (PAUT) and full matrix capture (FMC) techniques. Damage visualization, characterization, and sizing was completed with PAUT, total focusing method (TFM), and adaptive total focusing method (ATFM) advanced algorithms. The detection and sizing capabilities were initially validated on steel calibration samples with micromachined defects and synthetic HTHA damage. Vessels with suspected HTHA damage were removed from service, inspected with multiple array techniques, and then destructively evaluated for a results comparison with metallographic images. This study concluded that the FMC/TFM/ATFM techniques and algorithms improve detectability, characterization, and sizing of early-stage HTHA damage as compared to PAUT.

Defect Structure and Evolution of GaN-Based Light-Emitting Diodes on Planar and Cone-Patterned Sapphire Substrates

Epitaxial layers of GaN and InGaN/GaN multi-quantum well light emitting diodes (LEDs) were prepared both on cone-shaped patterned sapphire substrates and conventional sapphire substrates. The mechanism of nucleation of GaN epilayers was studied using metallographic and atomic force microscope images of the surface of GaN epilayers, and the internal factors of cone-shaped patterned sapphire substrates that accelerate GaN transverse growth and improve the quality of epitaxial layers were analyzed. The microstructural properties of GaN epitaxial layers grown on a patterned sapphire substrate were studied in detail using transmission electron microscopy to determine the distribution and growth. The surface morphology of p-GaN was characterized by scanning electron microscopy, and some other information about the defects was obtained. The full width at half maximum of electroluminescence spectra of LED grown on the patterned sapphire substrate is small, and its monochromaticity is good.

Surface Characteristics and Cavitation Damage in 8090Al–Li Alloy by Using Cavitation Water Jet Peening Processing

The cavitation strengthening test of Aluminum lithium Alloy (8090Al–Li) was carried out with different pressure process parameters. The roughness, residual stress, and morphology of the treated samples were studied. A high dislocation density was formed in the cavitation area of the workpiece, which resulted in surfacing hardening. The cavitation effect occurred in the collapse area. It was beneficial for the process to be the right choice for the surface treatment of metal materials. The results confirmed that surface roughness, grain size, micro-strain, erosive effect, and micro-hardness of the alloy were significantly affected by different cavitation peening pressures. In this case, surface roughness increased as the impact pressure increased. Besides, the microcrystalline structure decreased in the cavitation treatment area. The study showed on how the impact of the different cavitation peening pressure could suppress the material surfaces, the compressive residual stress, attained the maximum values of −137 MPa to −162 MPa, which increased from 7.87 to 27.56%, as compared to the original sample. In contrast, the corresponding surface roughness average increased to 3.04 μm. Experimental observation shows that the cavitation collapse boundaries obtained by the proposed sample surface and metallographic images were highly complete and accurate.