Texture Correlation Research Articles

Solidification and crystallographic texture modeling of laser powder bed fusion Ti-6Al-4V using finite difference-monte carlo method

Laser powder bed fusion (LPBF) additive manufacturing makes near-net-shaped parts with reduced material cost and time, rising as a promising technology to fabricate Ti-6Al-4 V, a widely used titanium alloy in aerospace and medical industries. However, LPBF Ti-6Al-4 V parts produced with 67° rotation between layers, a scan strategy commonly used to reduce microstructure and property inhomogeneity, have varying grain morphologies and weak crystallographic textures that change depending on processing parameters. This study predicts LPBF Ti-6Al-4 V solidification at three energy levels using a finite difference-Monte Carlo method and validates the simulations with large-area electron backscatter diffraction (EBSD) scans. The developed model accurately shows that a 〈001〉 texture forms at low energy and a 〈111〉 texture occurs at higher energies parallel to the build direction but with a lower strength than the textures observed from EBSD. A validated and well-established method of combining spatial correlation and general spherical harmonics representation of texture is developed to calculate a difference score between simulations and experiments. The quantitative comparison enables effective fine-tuning of nucleation density (N0) input, which shows a nonlinear relationship with increasing energy level. Future improvements in texture prediction code and a more comprehensive study of N0 with different energy levels will further advance the optimization of LPBF Ti-6Al-4 V components. These developments contribute a novel understanding of crystallographic texture formation in LPBF Ti-6Al-4 V, the development of robust model validation and calibration pipeline methodologies, and provide a platform for mechanical property prediction and process parameter optimization.

Persistent fatigue in post-acute COVID syndrome is associated with altered T1 MRI texture in subcortical structures: a preliminary investigation

Post-acute COVID syndrome (PACS) is a global health concern and is often associated with debilitating symptoms. Post-COVID fatigue is a particularly frequent and troubling issue, and its underlying mechanisms remain incompletely understood. One potential contributor is micropathological injury of subcortical and brainstem structures, as has been identified in other patient populations. Texture-based analysis (TA) may be used to measure such changes in anatomical MRI data. The present study develops a methodology of voxel-wise TA mapping in subcortical and brainstem regions, which is then applied to T1-weighted MRI data from a cohort of 48 individuals who had PACS (32 with and 16 without ongoing fatigue symptoms) and 15 controls who had cold and flu-like symptoms but tested negative for COVID-19. Both groups were assessed an average of 4–5 months post-infection. There were no significant differences between PACS and control groups, but significant differences were observed within the PACS groups, between those with and without fatigue symptoms. This included reduced texture energy and increased entropy, along with reduced texture correlation, cluster shade and profile in the putamen, pallidum, thalamus and brainstem. These findings provide new insights into the neurophysiological mechanisms that underlie PACS, with altered tissue texture as a potential biomarker of this debilitating condition.

Shear characterization in soft polyurethane foams: A critical comparison among experimental approaches

The study addresses the lack of standardization for the evaluation of the shear properties of hyperelastic soft foams. The results of three different standards used for sandwich structures or rigid materials (ASTM C 273, BS 4370, ASTM D 5379) are compared using specimens with different geometries and aspect ratios. Displacements are captured using Digital Image Correlation applying texture correlation and an incremental approach on three flame-retardant foams with densities around 85, 63, and 46 kg/m³. The study examines secondary effects like bending moments and normal stresses, finding the Iosipescu method effective for hyperelastic foams, ensuring minimal material wastage and test repeatability.

Correlation of microstructure, texture, and mechanical properties of friction stir welded Joints of AA7075-T6 plates using a flat tool pin profile

This study investigates the influence of square and hexagon tool pin profiles on the butt joint of AA7075-T6 plates through friction stir welding. In contrast to the AA7075-T6 base metal with a grain size of 32.736 μm, both square (4.43 μm) and hexagon (5.79 μm) pin profiles led to a significant reduction in grain size within the stir zone (SZ) of the weld cross-section. The SZ region exhibited a gradient in recrystallization and a notable fraction of high angle grain boundaries, attributed to continuous dynamic recrystallization influenced by variations in temperature and strain rate. Pole figure analysis revealed predominant shear texture elements (B/ B‾ and C) with minor A1*/A2* and A/ A‾, indicating elevated strains within the SZ. Orientation distribution function (ODF) analysis identified recrystallization texture elements such as Goss {110} <001>, cube {001} <101>, and P {011} <112>, along with shear texture components F {111} <112> and rotating cube (H) {001} <110>. Tensile and nanoindentation analyses demonstrated that the weld joint using a square-shaped pin profile exhibited higher strength, elongation, and elastic modulus compared to other weld joints. These findings suggest that the square tool pin geometry enhances material flow and grain refinement during welding, thereby improving the mechanical properties of the joint.

Heterogeneous InSAR Tropospheric Correction Based on Local Texture Correlation

Heterogeneous InSAR Tropospheric Correction Based on Local Texture Correlation

Microstructure and Texture Correlation with Mechanical Properties and Formability of Friction Stir-Welded AA5754 Sheets

Microstructure and Texture Correlation with Mechanical Properties and Formability of Friction Stir-Welded AA5754 Sheets

Robust Texture-Aware Local Adaptive Image Watermarking With Perceptual Guarantee

Watermarking involves embedding a watermark in an image and later extracting it to prove the image’s copyright. In most cases, a complete image contains both smooth and textured regions. As a rule of thumb, the visual quality of an image with a watermark embedded in its textured regions is better than that of the same image with a watermark in smooth regions. This paper, by taking advantage of the fact, proposes a texture-aware local adaptive watermarking algorithm to maximize the watermark’s robustness while maintaining its imperceptibility. To identify textured regions in an image, we introduce the texture value, an efficient and proper metric of the richness of image texture. It combines the texture correlation of the AC coefficients, the luminance masking of the DC coefficient, and the distribution of image texture. A watermark is embedded adaptively into multiple non-overlapping textured regions of an image under the specified SSIM condition. Its adaptiveness comes from a novel texture-aware adaptive parameter model derived by multivariate regression analysis. Correct extraction of watermarks from multiple textured regions can be done by the cooperation of embedding and extraction strategies, with the assistance of RS-based watermark coding model. They allow for greater robustness, faster extraction, and adjustable watermark capacity. The simulation experiments on 100 images demonstrate that our proposed algorithm outperforms state-of-the-art algorithms with respect to imperceptibility, robustness, and adaptability.

Relating in-vivo Strain of the Flexor Digitorum Superficialis Tendon with Grip Force

Epidemiological studies have shown a relationship between hand exertion and risk of developing distal upper extremity musculoskeletal disorders (DUEMSDs). Recently, fatigue-failure models were proposed for estimating the risk of DUEMSD development. However, models that incorporate tendon strain are primarily based on in-vitro data and may be better informed using in-vivo data. This methodological pilot study aimed to establish an approach for correlating grip force and spatiotemporal strains of the flexor digitorum superficialis (FDS) tendon using ultrasound imaging. Three image texture correlation techniques to measure in-vivo strains were explored and compared: digital image correlation, direct deformation estimation, and StrainNet, a novel deep learning neural network for strain prediction. StrainNet resulted in more accurate strain measurements than conventional image assessment tools, enabled continuous prediction of FDS tendon strain, and allowed for comparison of median bulk tissue strain during isometric contraction to grip force. Future work will study more participants and viscoelastic behavior.

Deep learning approach for predicting lymph node metastasis in non-small cell lung cancer by fusing image–gene data

The determination of lymph node metastasis is critical to the selection of treatment options for non-small cell lung cancer. Invasive pathological examinations cannot be performed frequently in clinical practice, thus non-invasive and reproducible methods are needed. The current research on non-invasive prediction methods based on image and genetic information has shortcomings such as small data sample size, high data dimension, and poor multimodal fusion effect. In this research, we propose a method for predicting lymph node metastasis in non-small cell lung cancer by fusing imaging data and genetic data to overcome these challenges. An attention-based multimodal information fusion module is designed to fuse image data and genetic data in the mid-fusion, and a bilinear fusion module based on Tucker decomposition is inserted into the model for late fusion, which significantly improves the performance of multimodal fusion. The 3D spiral transformation method is used to extract 2D images from 3D data, and the transformed images inherit and retain the spatial correlation of the original texture and edge information while increasing the image data sample size for subsequent prediction. The random forest method of important measurement is used for feature selection, and redundant data in gene information is eliminated. The experiments are carried out on the NSCLC-Radiogenomics dataset. The accuracy and AUC of the proposed model are 0.968 and 0.963, respectively. The experimental results show that the model is ideal performance in predicting lymph node metastasis, providing a new method for non-invasive lymph node metastasis prediction, which is beneficial to the application of precision medicine.

Correlation of microstructure, texture and intermetallic particles during multi-step cross-rolling and annealing in a strip-cast AA8011 alloy

In multi-step cross rolling (MSCR), twin roll cast (TRC)/strip-cast AA8011 aluminum sheet was cold-rolled up to 72 % thickness reduction by rotating +/- 90º with respect to normal direction (ND) after each rolling pass. The intermetallics particle size after MSCR got reduced to ∼ 0.5–3 µm compared to unidirectional cold rolling (UCR) ∼ 1.5 – 4.5 µm (Kumar et al. 2018) after the same thickness reduction of 72 %. Subsequently, it was annealed at 375 ºC for varying times (1–240 min). Both investigations (UCR/MSCR) showed no removal of centerline segregation (CLS) of Al-Fe-Si intermetallics. After 72 % cold rolling (UCR/MSCR), deformation texture (i.e., Brass (Bs) {110} < 112 > , copper (Cu) {112} < 111 > , Goss {110} < 011 > and S {123} < 634 > ) strengthened at the expense of Cube {100} < 001 > . However, cube improves during annealing at 375 ºC after MSCR than UCR. Moreover, it was observed that particle-stimulated nucleation (PSN) (intermetallics particle size ≥ 2 µm) played an important role in promoting random texture. For instance, the % volume fraction of Brass, Copper, Goss, S, Cube were 6.1 %, 5.3 %, 3.7 %, 9.0 %, 4.8 % (Kumar et al. 2018) and 6.5 %, 3.6 %, 1.9 %, 7.7 %, 5.3 % for UCR and MSCR, respectively. Hence, MSCR effectively reduces intermetallic particles size, decreases the overall anisotropy and improves the recrystallization texture (especially Cube component of texture) of TRC AA8011 aluminum alloy.

Multiscale Adaptive Fusion Network for Hyperspectral Image Denoising

Removing the noise and improving the visual quality of hyperspectral images (HSIs) is challenging in academia and industry. Great efforts have been made to leverage local, global or spectral context information for HSI denoising. However, existing methods still have limitations in feature interaction exploitation among multiple scales and rich spectral structure preservation. In view of this, we propose a novel solution to investigate the HSI denoising using a Multi-scale Adaptive Fusion Network (MAFNet), which can learn the complex nonlinear mapping between clean and noisy HSI. Two key components contribute to improving the hyperspectral image denoising: A progressively multiscale information aggregation network and a co-attention fusion module. Specifically, we first generate a set of multiscale images and feed them into a coarse-fusion network to exploit the contextual texture correlation. Thereafter, a fine fusion network is followed to exchange the information across the parallel multiscale subnetworks. Furthermore, we design a co-attention fusion module to adaptively emphasize informative features from different scales, and thereby enhance the discriminative learning capability for denoising. Extensive experiments on synthetic and real HSI datasets demonstrate that the proposed MAFNet has achieved better denoising performance than other state-of-the-art techniques. Our codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/summitgao/MAFNet</uri> .

Icing Detection Method of Transmission Line Based on Machine Vision

The conventional icing detection method is mainly based on the manual detection of icing status. The texture correlation characteristics of icing are variables, which lead to large errors in the icing edge detection and affect the subsequent maintenance of transmission lines. Therefore, the transmission line icing detection method based on machine vision is designed in this paper. The method comprises the following steps of extracting the icing texture feature of the transmission line, namely extracting the correlation feature of an icing image, identifying an icing area by using machine vision technology, carrying out edge detection on the transmission line and finally obtaining the icing thickness of the transmission line to realize the accurate detection on the icing of the power transmission line. The comparative experimental results show that the detection error of this method is small, the detection accuracy is high, and it can be applied to real life.

Correlation of strain path and crystallographic texture with electrochemical behavior of Ti-15V-3Cr-3Sn-3Al alloy

Effect of strain path and crystallographic texture on electrochemical properties of cold-rolled Ti-15V-3Cr-3Sn-3Al (all elements in weight%, designated as Ti-15333) alloy has been studied in the present investigation. The as-received specimens with and without solution annealing (SA) were subjected to different strain paths to reduction ratio (RR) of 40% and 80%. The microstructure showed the presence of shear bands (SBs) in all the deformed specimens and its fraction increased with increase in the RR. With the increase in deformation, α- and γ- fibers were observed in all the specimens. Electrochemical potentio-dynamic tests followed by open circuit voltage were used to investigate the passivity and pitting corrosion in 3.5% NaCl solution for both the undeformed (as-received, SA) and deformed Ti-15333 alloy specimens. A complementary assessment was employed to analyze the influence of work-hardening on passivity and pitting corrosion behaviors of Ti-15333 alloy systematically, combined with additional investigation of electrochemical impedance spectroscopy (EIS). High corrosion resistance was observed for the SA specimen than non-SA (as-received) specimen. Increase in the RR from 40% to 80% increased the corrosion rate for all the strain paths, except multi-step cross-rolled (MSCR). Corrosion resistance of the 80% deformed specimens were dependent on both the evolved microstructure (SBs, grain fragmentation) as well as crystallographic texture. Multi-step cross rolling resulted in the formation of a strong Rotated Cube ({100}<110>) texture, which was expected to be the reason for its enhanced corrosion resistance.

Lightweight Texture Correlation Network for Pose Guided Person Image Generation

Pose Guided Person Image Generation (PGPIG) is a popular task in deepfake, which aims at generating a person image with the given pose based on the source image. However, existing methods cannot comprehensively model the correlation between the source and the target domain. Most of them only focus on the correlation of the keypoints but ignore detail textures. In this paper, we propose a novel Texture Correlation Network (TCN) to simultaneously build pose and texture correlations. Specifically, our TCN adopts a two-stage design, including two networks: Pose Guided Person Alignment Network (PGPAN) and Texture Correlation Attention Network (TCAN). The PGPAN generates a coarse person image aligned with the target pose, while the TCAN produces a target generated image with the guidance of multiple correlations. The key component of TCAN is our new module, Texture Correlation Attention Module (TCAM), which explicitly builds geometry and texture correlation between the source image and the coarse target image. Those kinds of correlations facilitate to transfer real textures from the source to the target. Extensive experiments on the DeepFashion and Market1501 benchmarks demonstrate the superior performance of the proposed method. In addition, our model only uses 8.5 million parameters, which is significantly smaller than other methods.

A multi-level feature-fusion-based approach to breast histopathological image classification

Previously, convolutional neural networks mostly used deep semantic feature information obtained from several convolutions for image classification. Such deep semantic features have a larger receptive field, and the features extracted are more effective as the number of convolutions increases, which helps in the classification of targets. However, this method tends to lose the shallow local features, such as the spatial connectivity and correlation of tumor region texture and edge contours in breast histopathology images, which leads to its recognition accuracy not being high enough. To address this problem, we propose a multi-level feature fusion method for breast histopathology image classification. First, we fuse shallow features and deep semantic features by attention mechanism and convolutions. Then, a new weighted cross entropy loss function is used to deal with the misjudgment of false negative and false positive. And finally, the correlation of spatial information is used to correct the misjudgment of some patches. We have conducted experiments on our own datasets and compared with the base network Inception-ResNet-v2, which has a high accuracy. The proposed method achieves an accuracy of 99.0% and an AUC of 99.9%.

Deep Learning-Based Super-Resolution Reconstruction and Algorithm Acceleration of Mars Hyperspectral CRISM Data

In Mars exploration, hyper-spectrometry plays an important role due to its high spectral resolution. However, due to the technical difficulty and the data size, the spatial resolution or the coverage of hyperspectral data is often limited. This limitation can be alleviated by deep learning-based super-resolution (SR) reconstruction. But the spatial size and batch size of the input training data is limited due to the large number of spectral channels. To improve the efficiency of model training and SR reconstruction, a dataset based on CRISM hyperspectral data is created in this paper, and its redundancy is analyzed in both spectral and spatial spital dimensions. Compression algorithms based on data selection and PCA are used to reduce the size of the input training data. A network that can perform spatial SR and spectral enhancement is also proposed to make the network can be trained with the compressed data. With these compression algorithms and network, high-resolution data with 235 bands can be reconstructed from the low-resolution data with only 40 bands. Compared with the network trained on the original low-resolution data with 235 bands, the model training time and the SR reconstruction runtime can be reduced to 30% and 23% with practically no accuracy loss. The effectiveness of compression algorithms based on data selection also indicates that maybe not all the bands need to be transmitted from the Mars probes or be collected. Furthermore, it would, in principle, help improve the efficiency of satellite data transmission and simplify the design of the hyper-spectrometer. Additionally, a method for spatial dimension correlation evaluation is also proposed in this paper. The spatial compression shows that the proposed method can reflect the correlation of spatial texture between patches, and the model can be acceptably trained with only half of the original data.

Correlation between molecular stacking and anisotropic texture in spinnable mesophase pitch

The correlation of the anisotropic texture and (002) the molecular stacking property of spinnable mesophase pitch (SMP) was closely examined. SMP was found to be a specific solution consisting of mesogenic and solvent components, and the mesogenic component was composed of a cluster unit of (002) stacked molecular sheets. The anisotropic texture of SMP was strongly correlated with the height of (002) molecular stacking. When the concentration ratio of the mesogenic component to the solvent component was higher than the threshold concentration, the SMP always had 100 vol% anisotropic texture and a similar or larger number of stacked (002) sheets compared with its mesogenic component in both the solid and molten states. The solvent component, in both lamellar and hexagonal phases, assisted orientation of mesogenic molecules and rearrangement of mesogenic cluster units, thereby inducing a higher degree of graphitization than that of the solvent-free mesophase pitch. A schematic phase model of SMP was proposed based on a lyotropic liquid crystalline system and a hierarchical domain model.

Correlation of Microstructure, Texture and Dynamic Softening Mechanism of Uns S32101 Duplex Stainless Steel During Elevated Temperature Tensile

Correlation of Microstructure, Texture and Dynamic Softening Mechanism of Uns S32101 Duplex Stainless Steel During Elevated Temperature Tensile

Correlation of Texture and Residual Stress in Twist Channel Angular Pressed Laminated Al/Cu Composites

Composites are generally materials consisting of a minimum of two phases or components, which are selected for their advantageous properties the combination of which provides the final composite material with better specific properties than those of (the original) single-phase materials and alloys. One of the composite types is clad composites, or laminated composites, consisting of layers of individual (metallic) materials. The presented study deals with laminated Al/Cu composite billets, which were processed via the severe plastic deformation (SPD) method of twist channel angular pressing (TCAP). The primary advantage of the SPD methods in general is their ability to introduce significant shear strain, resulting in substantial refinement of the grains of the processed materials, which consequently enhances their properties. The study is specifically focused on assessing the effects of single and double TCAP pass on the specific lattice rotations induced via the severe shear strain within the reinforcing Cu wires, which typically manifest in the changes in residual stress distribution and modifications in texture. The results revealed that after the first TCAP pass, the imposed strain was not homogeneous across the cross-section of the TCAP-ed composite sample as the individual Cu wires featured differences in the distribution of residual stress, as well as in the observed ideal texture orientations. Nevertheless, the second TCAP pass introduced substantial homogenization of the imposed shear strain, which manifested in a more homogeneous distribution of residual stress across the individual wires compared to the first pass. Also, the distribution of ideal texture orientations was more homogenized after the second pass, as all the Cu fibres exhibited the dominance of ideal shear A fibre texture.

Microstructure and texture correlation of secondary heating assisted dissimilar friction stir welds of aluminum alloys

Present work aims to study the influence of secondary heating on material behavior during the friction stir welding (FSW) and examine it through the texture analysis. In the present context, the secondary heating pertains to the additional heat provided using a gas torch other than the heat primarily produced due to the process itself. Stop tool action technique is used to appraise the material flow around the pin. The material flow and evolution of textures during the process is studied by employing electron backscatter diffraction analysis. For analysis, the transverse sections of dissimilar FSW joints of AA6061-AA7075 and AA7075-AA2014 prepared with and without the application of secondary heating keeping the tool pin embedded in the joint are used. The basic assertions on strain rate based upon texture analysis and grain boundary engineering are used to explain the nature of material behavior during the process. The misorientation angle distribution is observed to shift towards random distribution for severe plastic deformation and additional heat supplied. Texture analysis reveals dominance of B/B‾ shear texture with sufficient C component in joints supplied with additional heating. For AA7075-AA2014 (without secondary heating) strong α-fiber is observed in the texture orientation distribution function plots. An equation is established to estimate the material volume flow around the pin in one revolution. The optical micrographs of welded joints prepared with and without use of secondary heating are compared to visualize the increase in material flow volume. The hardness of the joints obtained tend to decrease along the weld section on application of additional heat due to the dissolution of precipitates.