Matrix Texture Research Articles

Textures of neutrino mass matrix from S4-flavor symmetry

We study a texture of neutrino mass matrix characterized by two constraints consisting of one equality and another antiequality between two elements corresponding to two pairs of the matrix entries. Amidst such textures, we limit our study to three patterns which were realizable assuming an S4-symmetry within type II-seesaw scenario. Three such cases were found and studied: I (Mν22 = −Mν33 & Mν11 = +Mν23), II (Mν11 = −Mν33 & Mν22 = +Mν13) and III (Mν11 = −Mν22 & Mν33 = +Mν12). We specify the role of unphysical phases in the definition of the textures under study which were tested against experimental constraints, and were found to accommodate data with both hierarchies allowed. However, switching off the unphysical phases allows only for inverted hierarchy, except for the texture III which allows also, albeit for a very narrow parameter space region, for normal ordering. We stress that the different phenomenologies when including/excluding unphysical phases stem from the different definitions of the texture one has to adopt in order to make it insensitive to unphysical phases, rather than to any ‘absent’ physical effects of unphysical phases. In addition, we made clear how a texture definition should be independent of the PMNS parametrization. We present a complete phenomenological analysis of these three textures and justify analytically the resulting correlations. We detail the effect of the unphysical phases in diluting/deforming several correlations, which otherwise would have been “clear”. Finally, we give theoretical realizations within seesaw type II scenarios for such textures.

Computer Vision-Based Fire-Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its Counterfeits.

The authentication of Ziziphi Spinosae Semen (ZSS), Ziziphi Mauritianae Semen (ZMS), and Hovenia Acerba Semen (HAS) has become challenging. The chromatic and textural properties of ZSS, ZMS, and HAS are analyzed in this study. Color features were extracted via RGB, CIELAB, and HSI spaces, whereas texture information was analyzed via the gray-level co-occurrence matrix (GLCM) and Law's texture feature analysis. The results revealed significant differences in color and texture among the samples. The fire-ice ion dimensionality reduction algorithm effectively fuses these features, enhancing their differentiation ability. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) confirmed the algorithm's effectiveness, with variable importance in projection analysis (VIP analysis) (VIP > 1, p < 0.05) highlighting significant differences, particularly for the fire value, which is a key factor. To further validate the reliability of the algorithm, Back Propagation Neural Network (BP), Support Vector Machine (SVM), Deep Belief Network (DBN), and Random Forest (RF) were used for reverse validation, and the accuracy of the training set and test set reached 98.83-100% and 95.89-99.32%, respectively. The method provides a simple, low-cost, and high-precision tool for the fast and nondestructive detection of food authenticity.

Influence of forging on the microstructure and tensile properties of Al–based vortex plate

Abstract An AHS-2 alloy billet (~80 mm in diameter), with the composition of Al-10.13 wt.% Si-4.08 wt.% Cu-0.7 wt.% Mg-0.24 wt.% Fe, was hot forged to a vortex plate (~120 mm in diameter) product by hot die forging process (HDFP), through the back-pressure processing technology. The microstructure of both the billet and the product was investigated by optical microscope, X-ray diffraction, field emission scanning electron microscope, and electron backscatter diffraction. The main second phases in the alloy include Si, Al2Cu, Mg2Si and Al5FeSi. The phase particles were broken into smaller ones during the HDFP process, while the averaged grain size of α-Al matrix was slightly increased from 3.8 to 4.5 μm. The α-Al matrix of the billet has the texture close to &lt;100&gt; // ND (normal direction) and &lt;111&gt; // ND, while the product keeps the texture close to &lt;100&gt; // TeD (tensile direction) and &lt;111&gt; // TeD. The yield strength is increased from 142 ± 2 MPa for the billet to 430 ± 2 MPa for the product, while the ultimate tensile strength is simultaneously enhanced from 222 ± 3 MPa to 477 ± 2 MPa. The enhancement of yield strengthening is regarded mainly related to the texture difference of α-Al matrix, while the ultimate tensile strength difference has relationship with the variance of both matrix texture and size change of second phase particles. This study may be referred for material choosing and forging parameter designing in new energy automobile industries.

Multiscale Analysis of Alzheimer's Disease Using Feature Fusion in Cognitive and Sensory Brain Regions

Introduction: This research is focused on early detection of Alzheimer's disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images. Methods: Using 2D Gray Level Co-occurrence Matrix (2D-GLCM) texture features, volume, Standardized Uptake Value Ratios (SUVR), and obesity from different neuroimaging modalities, the study applies various classifiers, demonstrating a feature importance analysis in each Region of Interest (ROI). The research employs four classifiers, namely Linear Support Vector Machine (L-SVM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), and Logistic Regression with Stochastic Gradient Descent (LRSGD) Classifiers, to determine feature importance, leading to subsequent validation using a Probabilistic Neural Network (PNN) Classifier. Results: The research highlights the critical role of brain texture features, particularly in memory regions, for AD detection. Significant sex-specific differences are observed, with males showing significance in texture features in memory regions, volume in vision regions, and SUVR in speech regions, while females exhibit significance in texture features in memory and speech regions, and SUVR in vision regions. Additionally, the study analyzes how obesity affects features used in AD prediction models, clarifying its effects on speech and vision regions, particularly brain volume. Conclusion: The findings contribute valuable insights into the effectiveness of feature fusion, sex-specific differences, and the impact of obesity on AD-related biomarkers, paving the way for future research in early AD detection strategies and cognitive impairment classification.

Using structural equation modeling and intima-media complex texture features to assess cardiovascular disease risk in the common carotid artery

The potential for stroke risk in humans due to clinical cardiovascular disease (CVD), which leads to the hardening of the artery walls (atherosclerosis), can be assessed by examining the common carotid artery (CCA) in ultrasound images. Specifically, this can be done by measuring the intima media thickness (IMT), which represents the thickness of the arteries wall, and by analyzing texture features (TFs) of the CCA's intima-media complex (IMC) of the artery wall. In this paper, a sample of 612 longitudinal-section ultrasound images of the left and the right CCA from 158 men and 148 women, out of which 42 demonstrated clinical CVD, is studied. All images are intensity normalized and despeckled, with the IMC segmented through an in-house system of semi-automated segmentation, where the IMT is measured, and 40 different TFs are extracted. Then, employing structural equation modeling (SEM), these TFs, which are put in 6 groups (constructs), are collectively tested on how they are related to CVD. The influence of the IMT is also studied through a moderation analysis, while gender and age of the sub-jects of the study are tested with regard to their control effect. The main conclusions of the study are as follows. The 6 TFs groups and the IMT fit the measurement model very well. Also, 6 hypothesized paths for the impact of each TFs group on CVD are tested in a structural model, with 5 of them, namely Statistical Features (SF), Spatial Gray Level Dependence Matrices (SGLDM), Gray Level Difference Statistics (GLDS), Statistical Feature Matrix (SFM) and Laws Texture Energy Measures (LTEM), impacting CVD in a moderate manner (p-values between .04 and .07). The IMT is shown to play a strong moderating role (p-values of Δχ2 < .01), enhancing the level of impact of the TFs on CVD. Gender and age have a strong controlling effect on CVD (p-values of < .01). The precision of the findings of this study is considerably improved compared to those previously reported, because of the very good model fit (e.g., normed fit index (NFI) = 0.94) for both measurement and structural models. However, due to the moderate nature of the impact, supplementary work is required for testing either additional combinations of TFs, as well as testing other samples through the proposed model, for further evaluation of the CVD risk on symptomatic subjects at risk of atherosclerosis.

Data-Driven Analysis of Contributing Factors in Spectra Toward Quantitative Prediction of Photocatalytic Activity of BaTaO₂N

Photocatalytic hydrogen production via water splitting has attracted attention as an energy infrastructure independent of fossil resources. However, it has not yet reached the practical energy conversion efficiency level (5%), necessitating the enhancement of photocatalyst materials. Various crystal characteristics such as crystal shape, surface state, and lattice defects are known to be influencing factors on the performance of photocatalysts. However, their quantitative correlation and synergistic effects of each factor on performance are unclear. Understanding the quantitative correlation of crystal properties with photocatalytic activity would give effective crystal design for improvement. In this study, we aimed to extract crystallographic properties and understand their correlation with photocatalytic water splitting by multivariate analysis. BaTaO2N (BTON), which responds to visible light, was selected as a model material for photocatalysis.First, internal crystal information was collected from X-ray diffraction (XRD) patterns and Ultraviolet-Visible (UV-Vis) absorption spectra. Interpretable feature values, such as intensity ratio and full width at half maximum at the diffraction peak, impurity, absorption edge wavelength and its area were automatically extracted using Python. The factors contributing to water splitting were then identified by using LASSO regression. These feature values were used to depict a two-dimensional dimension reduction diagram (t-SNE diagram), which describe photocatalytic activity as color map. The t-SNE diagram confirmed the clustering of data with similar water splitting activity, indicating that this diagram can be used as an effective guide to explore the crystal properties with high photocatalytic activity. Next, scanning electron microscopy (SEM) was analyzed to extract additional feature values. Image analysis was carried out for the comprehensive extraction of crystal surface information. Gray-Level Co-occurrence Matrix (GLCM) texture analysis was an effective method for detecting boundary parts, thus used to extract abstract crystal surface information. In addition, Variational Autoencoder (VAE) was used to extract specific crystal surfaces. The extracted feature values can contain information about crystal surface structures such as crystal morphology, particle agglomeration, and co-catalyst support state. Correlation analysis between these feature values and photocatalytic activity is now underway and will be reported in the presentation. AcknowledgementThis work was partly supported by the Strategic Innovation Program (SIP) of the Cabinet Office, the NEDO Green Innovation fund projects, and collaborative research with companies.

The μ − τ reflection symmetry breaking in a B − L model with T7 × Z8 × Z2 symmetry

We suggest a neutrino mass matrix texture satisfying the μ−τ reflection symmetry in a B−L model with T7 symmetry by introducing additional singlet scalars and invoking the Type-I seesaw mechanism in which the hierarchical patterns of charged lepton and neutrino masses and of lepton mixing are addressed. The Dirac and Majorana mass matrices hint for a realistic lepton mixing pattern in which the μ−τ symmetry is broken by the contribution from the charged lepton sector. Explicit relations between the Yukawa-like couplings and the observed neutrino oscillation parameters are established. The Yukawa-like couplings in the charged-lepton sector are expressed in terms of three charged lepton masses whereas the Yukawa-like couplings in the neutrino sector are expressed in terms of three observed neutrino mixing angles, two neutrino mass-squared splittings and the lightest neutrino mass, whose magnitudes are in the same order. The considered model is satisfied for both normal and inverted neutrino mass hierarchies and for both lower and higher octants in the light of the recent experimental data.

Two-zero textures for Dirac Neutrinos

Abstract We review the two-zero mass matrix textures approach for Dirac neutrinos with the most recent global fit in the oscillation parameters. We found that three of the 15 possible textures are compatible with current experimental data, while the remaining two-zero textures are ruled out. Two textures are consistent with the neutrino masses' normal hierarchy and are CP-conserving. At the same time, the other one is compatible with both mass orderings and allows for CP violation. We also present the correlations between the oscillation parameters for the allowed two-zero textures. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.

The breaking of μ − τ reflection symmetry in a B − L extended 3HDM with (Z2 × Z4)⋊Z2 (II) symmetry

We propose a new neutrino mass matrix texture satisfying the μ−τ reflection symmetry in a B−L model with three Higgs doublets based on non-Abelian discrete symmetry (Z2×Z4)⋊Z2(II). The Dirac and Majorana mass matrices, which is motivated in the framework of the Type I seesaw mechanism in association with (Z2×Z4)⋊Z2(II) symmetry, hint for a realistic lepton mixing pattern where the co-bimaximal mixing pattern is broken by the charged-lepton contribution. The hierarchy of lepton masses is naturally achieved and the constraints on the effective neutrino mass are consistent with the recent experimental limits.

Neutrino mixing from a fresh perspective

We propose a neutrino mass matrix texture bearing a suitable correlation m22=−2m13 and study its phenomenological implications. In light of both normal and inverted hierarchies, the texture imposes specific bounds on some observational parameters. As a potential application, the prediction of effective Majorana neutrino mass mββ is visualized for both hierarchies. To understand the proposed texture from first principle, we incorporate type-I+II seesaw mechanism in association with A4×Z10×Z2 group.

Are neutrino oscillation mixings linked to the smallness of solar neutrino scale?

Observed reactor and atmospheric neutrino oscillation mixing values appear to be related to the neutrino scale ratio p ∆m2 sol/∆2 ATM in a way that suggest that the neutrino mass matrix can be expanded as a power series by using this ratio as the smallness parameter. This approach provides a simple and natural way to expose the inner hierarchies among neutrino mass terms, which amounts to also explain the solar oscillation mixing as well as solar oscillation scale. We explore a class of mass matrix textures that realize this scenario, for both normal and inverted neutrino mass hierarchies, as well as CP violation and their stability under renormalization scaling.

Enhancing mechanical and corrosion properties of GO and Al2O3 reinforced Cu composite coatings

Despite their significant functions and properties, the performance characteristics of Al2O3 ceramic particles and graphene oxide (GO) within hybrid copper composite coatings have been seldom investigated. This study successfully fabricated a Cu-GO- Al2O3 composite coating featuring fine particulate spherical network-like structures on St37 low carbon steel using an ultrasonic-electroless coating method. The effects of Al2O3-decorated graphene oxide hybrid reinforcement on the texture and nanomechanical properties of the copper matrix were examined. The tribological performance of the Cu-GO- Al2O3 composite coating under dry sliding conditions was evaluated, and the wear mechanism was investigated in detail. Results demonstrate that the Cu-GO- Al2O3 composite coating effectively reduces the wear rate and friction of the GO/ Al2O3 hybrid reinforced composite coating. Potentiodynamic polarization tests indicated that the Cu-GO- Al2O3 composite coating exhibits higher corrosion resistance compared to the copper matrix. The enhanced mechanical, tribological, and corrosion properties of the Cu-GO- Al2O3 composite coating are primarily attributed to: (i) the fine particulate spherical network-like structure; (ii) the synergistic effect of the GO and ceramic particle hybrid with a decorated structure; (iii) the formation of graphene oxide/ Al2O3 nanorolls in tribofilms and the excellent self-lubrication properties of graphene oxide. The Cu-GO- Al2O3 composite coating significantly improves the frictional and electrochemical properties of the copper matrix, offering new perspectives for next-generation electrical contacts and nanoelectromechanical systems.

Using Poisson’s ratio and acoustic anisotropy parameter to assess damage and accumulated plastic strain during fatigue loading of austenitic steel

The work investigated the effect of fatigue failure on the elastic characteristics of metastable austenitic steel AISI 321: Poisson’s ratio and acoustic anisotropy parameter. The elastic characteristics were calculated using ultrasonic measurements of the propagation time of longitudinal and shear elastic waves. The volume fraction of strain-induced martensite was determined by the eddy current method. Theoretical studies have shown that the main factors influencing Poisson’s ratio are the accumulation of microdamages and changes in phase composition. The change in the acoustic anisotropy parameter is associated with the influence of cyclic deformation on the crystallographic texture of the material matrix and the formation of oriented crystals of strain-induced martensite. Based on the analysis of experimental results, expressions were obtained for calculating damage and relative accumulated plastic deformation based on acoustic measurement data, which are widely used in engineering practice to determine the fatigue life of structural materials.

Texture feature-aware consistency for semi-supervised honeycomb lung lesion segmentation

Accurate segmentation of honeycomb lung lesions from lung CT images is crucial for the clinical diagnosis of a wide range of lung diseases. Existing semi-supervised segmentation methods ignore the rich texture feature information in honeycomb lung CT images, resulting in inaccurate predictions in the lesion region. Therefore, this paper proposes a semi-supervised honeycomb lung lesion segmentation framework using texture feature-aware consistency. The framework consists of a shared encoder, a main decoder, and three auxiliary decoders. The different outputs produced by the decoders are used to compute the Grey Level Co-occurrence Matrix and extract texture features, respectively, and further to compute the loss of texture feature-aware consistency. In pseudo-labeling supervision, the uncertainty of each output is computed to bootstrap the pseudo-labeling supervision loss. For the small lesion recognition omission problem, a pseudo-label refinement module is constructed to reuse the multi-scale features of the encoder, focusing on small-scale lesions, and refining the pseudo-labels. Experimental results show that the proposed method outperforms other methods in the honeycomb lung segmentation task, achieving a Dice coefficient of 77.62 using 20% labeled data. Additionally, experiments on publicly available datasets further demonstrate the generalizability of the proposed method. The code is available at https://github.com/Oran9er/TFC-Net-main.

Machine Learning on Ultrasound Texture Analysis Data for Characterizing of Salivary Glandular Tumors: A Feasibility Study.

Objective quantitative texture characteristics may be helpful in salivary glandular tumor differential diagnosis. This study uses machine learning (ML) to explore and validate the performance of ultrasound (US) texture features in diagnosing salivary glandular tumors. 122 patients with salivary glandular tumors, including 71 benign and 51 malignant tumors, are enrolled. Representative brightness mode US pictures are selected for further Gray Level Co-occurrence Matrix (GLCM) texture analysis. We use a t-test to test the significance and use the receiver operating characteristic curve method to find the optimal cut-point for these significant features. After splitting 80% of the data into a training set and 20% data into a testing set, we use five machine learning models, k-nearest Neighbors (kNN), Naïve Bayes, Logistic regression, Artificial Neural Networks (ANNs) and supportive vector machine (SVM), to explore and validate the performance of US GLCM texture features in diagnosing salivary glandular tumors. This study includes 49 female and 73 male patients, with a mean age of 53 years old, ranging from 21 to 93. We find that six GLCM texture features (contrast, inverse difference movement, entropy, dissimilarity, inverse difference and difference entropy) are significantly different between benign and malignant tumors (p < 0.05). In ML, the overall accuracy rates are 74.3% (95%CI: 59.8-88.8%), 94.3% (86.6-100%), 72% (54-89%), 84% (69.5-97.3%) and 73.5% (58.7-88.4%) for kNN, Naïve Bayes, Logistic regression, a one-node ANN and SVM, respectively. US texture analysis with ML has potential as an objective and valuable tool to make a differential diagnosis between benign and malignant salivary gland tumors.

Coastal Zone Classification Based on U-Net and Remote Sensing

The coastal zone is abundant in natural resources but has become increasingly fragile in recent years due to climate change and extensive, improper exploitation. Accurate land use and land cover (LULC) mapping of coastal zones using remotely sensed data is crucial for monitoring environmental changes. Traditional classification methods based on statistical learning require significant spectral differences between ground objects. However, state-of-the-art end-to-end deep learning methods can extract advanced features from remotely sensed data. In this study, we employed ResNet50 as the feature extraction network within the U-Net architecture to achieve accurate classification of coastal areas and assess the model’s performance. Experiments were conducted using Gaofen-2 (GF-2) high-resolution remote sensing data from Shuangyue Bay, a typical coastal area in Guangdong Province. We compared the classification results with those obtained from two popular deep learning models, SegNet and DeepLab v3+, as well as two advanced statistical learning models, Support Vector Machine (SVM) and Random Forest (RF). Additionally, this study further explored the significance of Gray Level Co-occurrence Matrix (GLCM) texture features, Histogram Contrast (HC) features, and Normalized Difference Vegetation Index (NDVI) features in the classification of coastal areas. The research findings indicated that under complex ground conditions, the U-Net model achieved the highest overall accuracy of 86.32% using only spectral channels from GF-2 remotely sensed data. When incorporating multiple features, including spectrum, texture, contrast, and vegetation index, the classification accuracy of the U-Net algorithm significantly improved to 93.65%. The major contributions of this study are twofold: (1) it demonstrates the advantages of deep learning approaches, particularly the U-Net model, for LULC classification in coastal zones using high-resolution remote sensing images, and (2) it analyzes the contributions of spectral and spatial features of GF-2 data for different land cover types through a spectral and spatial combination method.

Surface roughness analysis of side-polished fiber based on the importance of texture features

In order to explore the use of side-polished fibre (SPF) for microprobe-type "lab-on-fibre", this study presents an analysis of the surface roughness in side-polished fiber (SPF) using the gray level co-occurrence matrix (GLCM) texture feature analysis method. Experimental results show that the flat areas of the SPF polished surface exhibit texture characteristics with high mean values in contrast and entropy, and low mean values in the angular second moment (ASM), homogeneity, and correlation. Employing the random forest (RF) feature importance ranking method based on the Gini coefficient and out-of-bag (OOB) error estimation, this study assesses the sensitivity of various GLCM texture parameters in classifying different roughness levels of the SPF polished surfaces. A feature subset comprising variance, ASM, entropy, and contrast is identified as optimal. Utilizing this subset, the paper conducts an RF classification validation experiment on the roughness of the SPF polished surfaces, with results showing an RF classification accuracy of 95.65%.This research provides evidence for exploring the impact of rough polished surfaces in SPF optic sensors on the light coupling mechanism with environmental materials and its influence on sensor sensitivity. It lays the foundation for exploring the precise identification of high-sensitivity areas on SPF polished surfaces.

Magnetic Resonance Image Radiomic Reproducibility: The Impact of Preprocessing on Extracted Features from Gross and High-Risk Clinical Tumor Volumes in Cervical Cancer Patients before Brachytherapy.

Radiomic feature reproducibility assessment is critical in radiomics-based image biomarker discovery. This study aims to evaluate the impact of preprocessing parameters on the reproducibility of magnetic resonance image (MRI) radiomic features extracted from gross tumor volume (GTV) and high-risk clinical tumor volume (HR-CTV) in cervical cancer (CC) patients. This study included 99 patients with pathologically confirmed cervical cancer who underwent an MRI prior to receiving brachytherapy. The GTV and HR-CTV were delineated on T2-weighted MRI and inputted into 3D Slicer for radiomic analysis. Before feature extraction, all images were preprocessed to a combination of several parameters of Laplacian of Gaussian (1 and 2), resampling (0.5 and 1), and bin width (5, 10, 25, and 50). The reproducibility of radiomic features was analyzed using the intra-class correlation coefficient (ICC). Almost all shapes and first-order features had ICC values > 0.95. Most second-order texture features were not reproducible (ICC < 0.95) in GTV and HR-CTV. Furthermore, 20% of all neighboring gray-tone difference matrix texture features had ICC > 0.90 in both GTV and HR-CTV. The results presented here showed that MRI radiomic features are vulnerable to changes in preprocessing, and this issue must be understood and applied before any clinical decision-making. Features with ICC > 0.90 were considered the most reproducible features. Shape and first-order radiomic features were the most reproducible features in both GTV and HR-CTV. Our results also showed that GTV and HR-CTV radiomic features had similar changes against preprocessing sets.

Effect of hydrogen on the microstructural evolution and local mechanical properties of 430 ferritic stainless steel at high-temperature conditions

The present study investigated the impact of hydrogen on the microstructural evolution and mechanical properties of 430 ferritic stainless steel at high-temperature conditions. Hydrogen trap sites were analyzed at high temperatures using thermal desorption spectrometry through high-temperature hydrogen atmosphere charging and electrolytic hydrogen charging. The microstructural evolution was characterized using scanning electron microscopy and electron backscatter diffraction. The nanoindentation technique was employed to analyze the local mechanical properties of ferrite matrix, grain boundaries, and M23C6/matrix interfaces. Hydrogen trap sites in 430 ferritic stainless steel included ferrite crystal lattice, grain boundaries, intragranular dislocations, and M23C6/matrix interfaces where hydrogen could not intrude at room temperature. At high temperatures, hydrogen caused a decrease in grain size. Furthermore, at high temperatures, hydrogen altered the crystal orientation and texture of the ferrite matrix. There was an increase in the number of ferrite grains with (001) and (101) orientation increase and the formation of a new α-fiber texture. Hydrogen inhibited the triggering of the slip system in body centered cubic crystal lattice and induced an increase in dislocation density and internal stress. While hydrogen at high temperatures had a minimal impact on the matrix strength limit, it reduced the strength limit of the near grain boundaries and (Fe, Cr)23C6 precipitates. The influence of hydrogen on the microstructural evolution and mechanical properties of 430 ferritic stainless steel could be elucidated by the mechanisms of hydrogen enhanced decohesion, hydrogen-enhanced localized plasticity, and hydrogen enhanced specific crystal plane rotation suggested in the present study.

Second phase and matrix texture evolution influence mechanical properties of Al-Mg-Al composite plate by HPR with annealing temperature

Annealing treatment can determine the quality of heterogeneous composite plate by regulating the matrix and interface structures and interlayer bonding ability. Al-Mg-Al composite plate was used as an example. Al-Mg-Al composite plate was rolled with 50 % reduction by hard plate rolling. After that, it was annealed at 250 ℃, 300 ℃, 350 ℃ and 400 ℃ for one hour respectively. Through microstructure observation and performance test, the results show that the reaction phases of Al3Mg2 and Al12Mg17 were formed at the interface during annealing treatment. As the temperature continued to climb, yield strength and elongation after fracture of Al-Mg-Al composite plate showed a trend of increasing first and then decreasing, in addition, the tensile strength gradually decreased. As temperature reached 300 °C, its performance reached the best, the tear strength increased to 0.367 KN, and yield strength and elongation reached 172 MPa and 14.5 %, respectively. The study showed that although annealing temperature didn't change types of texture configuration of magnesium alloy, it affected the grain growth, resulting in the change of base surface texture and the overall performance of heterogeneous composite plate. This research provides a theoretical basis for precise regulation of microstructure and comprehensive performance of heterogeneous composite plate by annealing treatment.