Texture Correlation Research Articles

Diagnosis of winter-wheat water stress based on UAV-borne multispectral image texture and vegetation indices

Timely and accurate detection of crop water stress is vital for precision irrigation. Whether the accuracy of the prevailing diagnosis of crop water stress using vegetation indices (VIs) and spectral reflectance can be improved still remains to be investigated. The crop surface characteristics such as grayscale or color vary under different water stress, so in this study one more variable, image texture, was utilized together to diagnose water stress. For this end, the canopy image of winter wheat in bloom was obtained by unmanned aerial vehicle (UAV) equipped with multispectral sensor, and the effect of soil background was eliminated using vegetation index threshold method. On this basis, Grey level co-occurrence matrix (GLCM) was used to calculate the mean (MEA), variance (VAR), homogeneity (HOM), contrast (CON), dissimilarity (DIS), entropy (ENT), second moment (SEC) and correlation (COR) of the image texture under different spatial resolutions (0.008 m, 0.01 m, 0.02 m, 0.05 m, 0.1 m and 0.2 m). Next, the canopy vegetation indices were obtained by mathematical transformation of canopy reflectance, and then sensitive image texture and vegetation indices by full subset regression method. Finally, Cubist, BPNN (Back Propagation Neural Network) and ELM (Extreme Learning Machine) methods were adopted to build the estimation models of the stomatal conductance (Gs) of winter wheat (between the sensitive image texture and Gs, and between vegetation index and Gs), and the water stress map was plotted based on the optimal Gs estimation model. The result showed: (i) the image texture obtained from the high-resolution multispectral image had a high correlation with Gs, and the image texture (VAR, HOM, CON, DIS, ENT and SEC) at 550 nm had the most significant correlation; (ii) the higher the ground resolution, the higher the correlation between the Gs and the image texture, the vegetation indices, respectively. The image texture with a ground resolution of 0.008 m combined with VIs and Gs had the highest correlation, and combining image texture and vegetation index can significantly improve the estimation accuracy of winter wheat Gs; (iii) Among the three estimation models, the BPNN model constructed by combining the image texture and VIs (MEA, VAR, ENT, DWSI and EXG) had the best estimation performance (Calibration:Rc2 = 0.899, RMSEc = 0.01, MAEc = 0.006; Validation:Rc2 = 0.834, RMSEv =;0.018, MAEv = 0.014), and an accurate estimation could even be achieved at a lower Gs value. Compared with the BPNN model solely based on VIs or image texture, the Rc2 of the BPNN model based on the combined variables increased by 24% and 22.48%, respectively. Therefore, combining UAV multispectral image texture and VIs to estimate Gs provides a feasible and accurate method for water stress diagnosis of winter wheat.

The Correlation of Texture and the Formation of the Adiabatic Shear Band in 7XXX Aluminum Alloy during Dynamic Loading

The mechanical behavior of the extruded 7003-T6 aluminum profiles used as automotive buffer beams is investigated. The correlation of the texture and the formation of the adiabatic shear band is analyzed. Copper texture, R texture, and S texture are the main reasons for the anisotropy of mechanical behavior of the profile, resulting in that the stress of the profile along the extrusion direction is higher than that perpendicular to the extrusion direction. Through finite element modeling (FEM), it can be found that the adiabatic shear band is developed in the sample if the dynamic loading direction is parallel to the extrusion direction, but it does not appear if the loading direction is perpendicular to the extrusion direction. When the dynamic loading direction is parallel to the extrusion direction, higher stress results in a lower energy barrier for shear localization. Therefore, the formation of the adiabatic shear band is susceptible along but is not sensitive perpendicular to the extrusion direction. This study provides technical support for the service of 7003 aluminum alloy in automobiles, which has important academic and engineering application value.

Comparing the mechanical properties, microstructure, texture and in-vitro degradation behavior of TNTZ/nano-fluorapatite composite and TNTZ bioalloy

The present study deals with (i) the fabrication of the composite surface layer of Ti–29Nb–13Ta-4.6Zr/nano-fluorapatite through friction stir processing, (ii) clarifying the correlation of microstructure and texture with in-vitro degradation behavior and bioactivity of fabricated nano-biocomposite, and (iii) comparing the mechanical and functional properties of nano composite whit those obtained for TNTZ bio-alloy. The results indicated that increasing the number of deformation passes effectively refines the microstructure, leading to the average grain sizes of less than 5 μm. The texture of the material was also evolved in the presence of the nano particles where the grains reorient along <111> direction in contrast to the <101>-oriented grains in the starting microstructure. In-vitro bioactivity of the processed samples was measured after being immersed in simulated body fluid solution for various duration up to 30 days. Enhanced corrosion resistance and bio-mineralization of nanocomposite surface layer was discussed relying on the occurrence of grain refinement, substructure development and evolution of texture components along <101> direction. Assessment of the mechanical properties of the fabricated composites and as-received material also indicated the positive effect of the applied processing route and the presence of nano particles.

Combined Spiral Transformation and Model-Driven Multi-Modal Deep Learning Scheme for Automatic Prediction of TP53 Mutation in Pancreatic Cancer.

Pancreatic cancer is a malignant form of cancer with one of the worst prognoses. The poor prognosis and resistance to therapeutic modalities have been linked to TP53 mutation. Pathological examinations, such as biopsies, cannot be frequently performed in clinical practice; therefore, noninvasive and reproducible methods are desired. However, automatic prediction methods based on imaging have drawbacks such as poor 3D information utilization, small sample size, and ineffectiveness multi-modal fusion. In this study, we proposed a model-driven multi-modal deep learning scheme to overcome these challenges. A spiral transformation algorithm was developed to obtain 2D images from 3D data, with the transformed image inheriting and retaining the spatial correlation of the original texture and edge information. The spiral transformation could be used to effectively apply the 3D information with less computational resources and conveniently augment the data size with high quality. Moreover, model-driven items were designed to introduce prior knowledge in the deep learning framework for multi-modal fusion. The model-driven strategy and spiral transformation-based data augmentation can improve the performance of the small sample size. A bilinear pooling module was introduced to improve the performance of fine-grained prediction. The experimental results show that the proposed model gives the desired performance in predicting TP53 mutation in pancreatic cancer, providing a new approach for noninvasive gene prediction. The proposed methodologies of spiral transformation and model-driven deep learning can also be used for the artificial intelligence community dealing with oncological applications. Our source codes with a demon will be released at https://github.com/SJTUBME-QianLab/SpiralTransform.

Correlation between Molecular Stacking and Anisotropic Texture in Spinnable Mesophase Pitch

The correlation of the anisotropic texture and (002) molecular stacking property of spinnable mesophase pitch was closely examined. Spinnable mesophase pitch was found to be a specific solution which consisted of mesogenic and solvent components. Here, the mesogenic component composed of the cluster unit of the (002) stacked molecular sheets. In addition, the anisotropic texture of spinnable mesophase pitch showed a strong correlation with the height of (002) molecular stacking. When the concentration of mesogenic component to the solvent one is higher than the threshold concentration, spinnable mesophase pitch always showed 100 vol% anisotropic texture and the similar or larger number of stacked (002) sheets than that of its mesogenic component in both the solid and melt states. The solvent component in the lamellar and hexagonal phases may help the orientation of mesogenic molecules and rearrangement of mesogenic cluster units, inducing a higher graphitization degree than that of the mesophase pitch with solvent-free. The schematic phase model of spinnable mesophase pitch was proposed by the authors based on a lyotropic liquid crystalline system and the hierarchical domain model.

SIFT Feature‐Based Video Camera Boundary Detection Algorithm

Aiming at the problem of low accuracy of edge detection of the film and television lens, a new SIFT feature‐based camera detection algorithm was proposed. Firstly, multiple frames of images are read in time sequence and converted into grayscale images. The frame image is further divided into blocks, and the average gradient of each block is calculated to construct the film dynamic texture. The correlation of the dynamic texture of adjacent frames and the matching degree of SIFT features of two frames were compared, and the predetection results were obtained according to the matching results. Next, compared with the next frame of the dynamic texture and SIFT feature whose step size is lower than the human eye refresh frequency, the final result is obtained. Through experiments on multiple groups of different types of film and television data, high recall rate and accuracy rate can be obtained. The algorithm in this paper can detect the gradual change lens with the complex structure and obtain high detection accuracy and recall rate. A lens boundary detection algorithm based on fuzzy clustering is realized. The algorithm can detect sudden changes/gradual changes of the lens at the same time without setting a threshold. It can effectively reduce the factors that affect lens detection, such as flash, movies, TV, and advertisements, and can reduce the influence of camera movement on the boundaries of movies and TVs. However, due to the complexity of film and television, there are still some missing and false detections in this algorithm, which need further study.

The correlation of microstructure, recrystallization texture and mechanical properties with second–phase content in Al/WO3/SiC hybrid nanocomposite during ARB process

In the present work, the evaluation of microstructure, recrystallization texture with second–phase content in Al/WO3/SiC hybrid nanocomposite during accumulative roll bonding process was investigated by electron back scatter diffraction (EBSD) and analysis of orientation distribution function (ODF). The results showed that the density of nanoparticles played a critical role in the microstructural changes, recrystallization texture and mechanical properties. When the volume fraction of nanoparticles increases, the recrystallized grains are elongated in the rolling direction (RD) due to the Zener-pinning of nanoparticles at high angle grain boundaries and consequently the HAGBs spacing in the normal direction (ND) are constantly reduced. The texture intensity increased sharply with increasing the volume fraction of nanoparticles and eventually reached Fg = 27.23 for S3 nanocomposite. In addition, with development of recrystallization, the random texture in S1and S2 nanocomposites increased significantly. Also, the low intensity of the texture components in S1 and S2 specimen is clearly evident for all fibers. Furthermore, mechanical properties were measured for all specimens and results of the tensile strengths and elongation showed that with decreasing the volume fraction of reinforcing nanoparticles and increasing the volume fraction of recrystallized grains, the tensile strength and elongation were decreased and increased, respectively.

Dilated multi-scale cascade forest for satellite image classification

ABSTRACT Satellite image classification is the core of satellite image interpretation. The traditional shallow learning classification algorithm is difficult to extract remote sensing image features effectively, resulting in low classification accuracy. The deep neural network can automatically realize the feature analysis. However, due to the similarity of the optical properties of the cloud and snow, the existing deep learning model is difficult to obtain satisfactory results for the cloud and snow classification in the plateau. Meanwhile, too many parameters of deep learning lead to slow detection speed, which is difficult to meet the real-time requirements of detection. Furthermore, the deep learning method is prone to model degradation. In view of the existing problems, this paper proposes a dilated multi-scale cascade forest method to realize the classification of satellite cloud images. Multi-scale scanning increases the diversity of feature vector extraction and improves the separability of features. The dilated structure is designed to expand the feature receptive field, it improves the efficiency of feature extraction without losing the texture and spatial correlation information of the original image. In addition, the number of layers of network cascading does not need to be customized, it can be determined automatically according to the performance of the system. The proposed method effectively improves the accuracy of cloud/snow classification. Experiments on the imagery of HuanJing satellites A and B (HJ-1A/1B) in China demonstrate that the proposed model can get better classification results than existing studies. The time complexity of the proposed model is lower than existing works, hence it is practical in real-time and accurate remote sensing image classification.

Characterisation and correlation of areal surface texture with processing parameters and porosity of High Speed Sintered parts

High Speed Sintering is an advanced powder bed fusion polymer Additive Manufacturing technique aimed at economical production of end-use parts in series manufacture. Surface finish is thus of high importance to end users. This study investigates the surface topography of High Speed Sintered parts produced using a range of different energy-related process parameters including sinter speed, lamp power and ink grey level. Areal surface texture was measured using Focus Variation microscopy and the sample porosity was systematically examined by the X-ray Computed Tomography technique. Surface topography was further characterised by Scanning Electron Microscopy, following which the samples were subject to tensile testing. Results showed that areal surface texture is strongly correlated with porosity, which can be further linked with mechanical properties. Certain texture parameters i.e. arithmetic mean height Sa, root-mean-square Sq and maximum valley depth Sv were identified as good indicators that can be used to compare porosity and/or mechanical properties between different samples, as well as distinguish up-, down-skins and side surfaces. Sa, Sq and Sv for up- and down-skins were found to correlate with the above energy-related process parameters. It was also revealed that skewness Ssk and kurtosis Sku are related to sphere-like protrusions, sub-surface porosity and re-entrant features. Energy input is the fundamental reason that causes varying porosity levels and consequently different surface topographies and mechanical properties, with a 10.07 μm and a 30.21 % difference in Sa and porosity, respectively, between the ‘low’ and ‘high’ energy input.

Image Retrieval Based on the Combination of Region and Orientation Correlation Descriptors

A large number of growing digital images require retrieval effectively, but the trade-off between accuracy and speed is a tricky problem. This paperwork proposes a lightweight and efficient image retrieval approach by combining region and orientation correlation descriptors (CROCD). The region color correlation pattern and orientation color correlation pattern are extracted by the region descriptor and the orientation descriptor, respectively. The feature vector of the image is extracted from the two correlation patterns. The proposed algorithm has the advantages of statistic and texture description methods, and it can represent the spatial correlation of color and texture. The feature vector has only 80 dimensions for full color images specifically. Therefore, it is very efficient in image retrieving. The proposed algorithm is extensively tested on three datasets in terms of precision and recall. The experimental results demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms.

Clinical Utility of Triplicate En Face Image Averaging for Optical Coherence Tomography Angiography in Glaucoma and Glaucoma Suspects.

The purpose of this study was to test the hypothesis that triplicate averaging of the RPC layer improves visualization and diagnostic accuracy of OCTA for glaucoma. This is a cross-sectional study involving 63 primary open-angle glaucoma patients and 70 age-matched glaucoma suspects. Triplicate 6×6 mm OCTA scans of the optic nerve head were acquired, and the RPC layer was extracted. RPC en face images were registered and averaged. Parameters of global entropy, global standard deviation, local texture correlation, local homogeneity, signal-to-noise ratio, and intercapillary distance were used to measure the change in visualization with averaging. Vessel area density (VAD), vessel skeleton density (VSD), and flux parameters were calculated in a 2.8 mm annulus excluding the optic disc. The diagnostic accuracy of these parameters for glaucoma was assessed by calculating the area under the receiver operating curve (AUC) values. Three-frame averaging resulted in decreased global entropy and global standard deviation (Ps<0.001), and increased local texture correlation, local homogeneity, signal-to-noise ratio, and intercapillary distance (Ps<0.001). Averaged images also had reduced VAD, VSD, and flux (Ps<0.001). AUC was significantly increased for VSD after image averaging (P=0.018), while no significant change in AUC was observed for VAD (P=0.229) or flux (P=0.193). Triplicate averaging improves visualization of the RPC layer and the diagnostic accuracy of VSD for glaucoma. The impact of image averaging on OCTA diagnostic performance and other potential applications warrants further exploration.

Correlation of Strain Path, Texture, Twinning, and Mechanical Properties in Twinning-Induced Plasticity Steel during Wire Drawing.

The effect of changing the strain path on texture development, twin kinetics, and mechanical properties in twinning-induced plasticity steel was investigated to understand twinning behavior in more detail. Among the various plastic deformation processes, the wire drawing process was selected to achieve the aims of the study. Specimens of cold-drawn TWIP steel wire under the same effective strain but with different crystallographic textures were successfully fabricated using the effect of the wire drawing direction. Electron backscatter diffraction results showed that the drawn wires using both unidirectional (UD) and reverse-directional (RD) wire drawing processes were characterized as duplex fiber textures of major <111> and minor <100>. It was found that the RD wire had a higher fraction of <111> component at both the center and surface areas compared to the UD wire, because the metal flow of the RD wire was beneficial for the development of a <111> orientation. The pronounced <111> crystallographic orientation of the RD wire activated the twinning rate and geometrically necessary dislocation density, leading to an increase in strength but a decrease in ductility. The strain path is as important as the amount of strain for strengthening the materials, especially those that are deformed by twinning.

Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering

Sparse representation (SR) has been widely used in image fusion in recent years. However, source image, segmented into vectors, reduces correlation and structural information of texture with conventional SR methods, and extracting texture with the sliding window technology is more likely to cause spatial inconsistency in flat regions of multi-modality medical fusion image. To solve these problems, a novel fusion method that combines separable dictionary optimization with Gabor filter in non-subsampled contourlet transform (NSCT) domain is proposed. Firstly, source images are decomposed into high frequency (HF) and low frequency (LF) components by NSCT. Then the HF components are reconstructed sparsely by separable dictionaries with iterative updating sparse coding and dictionary training. In the process, sparse coefficients and separable dictionaries are updated by orthogonal matching pursuit (OMP) and manifold-based conjugate gradient method, respectively. Meanwhile, the Gabor energy as weighting factor is utilized to guide the LF components fusion, and this further improves the fusion degree of low-significant feature in the flat regions. Finally, the fusion components are transformed to obtain fusion image by inverse NSCT. Experimental results demonstrate the more competitive results of the proposal, leading to the state-of-art performance on both visual quality and objective assessment.

An Analysis of Patients with DNA Repair Pathway Mutations Treated with a PARP Inhibitor.

Molecular analysis has revealed four subtypes of pancreatic ductal adenocarcinoma (PDAC). One subtype identified for the presence of DNA damage repair deficiency can be targeted therapeutically with the poly (ADP-ribose) polymerase (PARP) inhibitor olaparib. We performed a single institution retrospective analysis of treatment response in patients with PDAC treated with olaparib who have DNA damage repair deficiency mutations. Patients with germline or somatic mutations involving the DNA repair pathway were identified and treated with olaparib. The primary objective was to examine the objective response rate (ORR). The secondary objectives were assessing tolerability, overall survival, and change in cancer antigen 19-9. Quantitative texture analysis (QTA) was evaluated from CT scans to explore imaging biomarkers. Thirteen individuals with metastatic PDAC were treated with Olaparib. The ORR to Olaparib was 23%. Median overall survival (OS) was 16.47 months. Four of seven patients with BRCA mutations had an effect on RAD51 binding, with a median OS of 24.60 months. Exploratory analysis of index lesions using QTA revealed correlations between lesion texture and OS (hepatic lesion tumor texture correlation coefficient [CC], 0.683, p = .042) and time on olaparib (primary pancreatic lesion tumor texture CC, 0.778, p = .023). In individuals with metastatic PDAC who have mutations involved in DNA repair, Olaparib may provide clinical benefit. BRCA mutations affecting RAD51 binding domains translated to improved median OS. QTA of individual tumors may allow for additional information that predicts outcomes to treatment with PARP inhibitors. Pursuing germline and somatic DNA sequencing in individuals with pancreatic ductal adenocarcinoma may yield abnormalities in DNA repair pathways. These individuals may receive benefit with poly (ADP-ribose) polymerase (PARP) inhibition. Radiomics and deep sequencing analysis may yet uncover additional information that may predict outcome to treatment with PARP inhibitors.

Value of Texture Analysis of Intravoxel Incoherent Motion Parameters in Differential Diagnosis of Pancreatic Neuroendocrine Tumor and Pancreatic Adenocarcinoma.

Objective To evaluate the value of texture features derived from intravoxel incoherent motion (IVIM) parameters for differentiating pancreatic neuroendocrine tumor (pNET) from pancreatic adenocarcinoma (PAC).Methods Eighteen patients with pNET and 32 patients with PAC were retrospectively enrolled in this study. All patients underwent diffusion-weighted imaging with 10 b values used (from 0 to 800 s/mm 2). Based on IVIM model, perfusion-related parameters including perfusion fraction (f), fast component of diffusion (Dfast) and true diffusion parameter slow component of diffusion (Dslow) were calculated on a voxel-by-voxel basis and reorganized into gray-encoded parametric maps. The mean value of each IVIM parameter and texture features [Angular Second Moment (ASM), Inverse Difference Moment (IDM), Correlation, Contrast and Entropy] values of IVIM parameters were measured. Independent sample t-test or Mann-Whitney U test were performed for the between-group comparison of quantitative data. Regression model was established by using binary logistic regression analysis, and receiver operating characteristic (ROC) curve was plotted to evaluate the diagnostic efficiency.Results The mean f value of the pNET group were significantly higher than that of the PAC group (27.0% vs. 19.0%, P = 0.001), while the mean values of Dfast and Dslow showed no significant differences between the two groups. All texture features (ASM, IDM, Correlation, Contrast and Entropy) of each IVIM parameter showed significant differences between the pNET and PAC groups (P=0.000-0.043). Binary logistic regression analysis showed that texture ASM of Dfast and texture Correlation of Dslow were considered as the specific imaging variables for the differential diagnosis of pNET and PAC. ROC analysis revealed that multiple texture features presented better diagnostic performance than IVIM parameters (AUC 0.849-0.899 vs. 0.526-0.776), and texture ASM of Dfast combined with Correlation of Dslow in the model of logistic regression had largest area under ROC curve for distinguishing pNET from PAC (AUC 0.934, cutoff 0.378, sensitivity 0.889, specificity 0.854).Conclusions Texture analysis of IVIM parameters could be an effective and noninvasive tool to differentiate pNET from PAC.

Fast 3D-HEVC inter mode decision algorithm based on the texture correlation of viewpoints

To reduce the computational complexity of 3D-HEVC, a fast inter mode decision algorithm based on texture correlation of adjacent viewpoints is proposed (FMD_inter). By studying the statistical probability of inter prediction mode distributions in 3D-HEVC coding scheme, the mode of dependent view is highly correlated with the base one, which enables the early mode decision. Moreover, by analyzing the relationship between video texture and the inter prediction mode, selectively skip checking symmetric motion partition (SMP) mode and asymmetric motion partition (AMP) mode according to the texture features of coding unit is reasonable. The proposed algorithm is able to achieve a reduction of computational complexity by 18.71% on average, while maintaining the coding efficiency. The subjective quality of the synthesized views also shows the effectiveness of the algorithm.

Fractal box-counting technique for shelf life study of grapes by laser speckle image processing

Background: In agriculture, it is essential to follow the ripening of fruits and vegetables which is a subject of interest economically. The use of optical non-destructive method is the most suitable technique for evaluating the ripening stage of fruits when they ripen off-tree and eventually to predict the optimum storage life. Many methods are recently developed in optical image processing. The texture correlation function and the fractal box counting (FBC) of biospeckle pattern have been described. Materials and Methods: The dynamic speckle patterns known as boiling speckle are produced by a coherent light illumination incident on the active material of biological tissue. A high coherent laser source He-Ne with 632 nm with neutral density filter and charge-coupled device camera are the major components of the experimental setup. The same experiment may be done by a green laser of wavelength 541 nm (green color). The same procedure continues to take the speckle images in periodically. Results and Discussion: With the help of FBC method in image processing technique, we can assess optically rough surface fruits such as grapes in this work. According to the FBC theory, the speckle images (R1) give a binary counting value D = 1.7640 at the initial stage. Gray image converted into binary image which gives the fractal counting value D = 1.7554 at later on phase. It means that the specimen (grapes) becomes dry periodically and its value is affected by the surrounding environmental condition. Conclusion: Using the dynamic speckle phenomenon or biospeckle method, we demonstrated the assessment of shelf life activity of grapes. The visual phase of the autocorrelation function gives reliable results showing the variation in activity because of the internal changes caused by decreasing water content and then becomes dry grape stage. The correlation function of speckle patternand the Fractal Box Counting (FBC) technique has been proposed as a tool to estimate the postharvest stage of grapes. The bioactivities of fruits and vegetable materials are based on Brownian motion, due to subcellular organs. This indicates the senescence or biological aging characteristics. The quantification of the biospeckle phenomena is associated with the moment of inertia, time history of speckle pattern and grey level co-occurrence matrix. We proposed a new FBC technique to assess the shelf life of grapes.

Hyperspectral Image Classification Based on Fusion of Guided Filter and Domain Transform Interpolated Convolution Filter

In recent years, the spatial texture features obtained by filtering have become a hot research topic to improve hyperspectral image classification, but spatial correlation information is often lost in spatial texture information extraction. To solve this problem, a spectral-spatial classification method based on guided filtering and by the algorithm Large Margin Distribution Machine (LDM) is proposed. More specifically, the spatial texture features can be extracted by a Guided filter (GDF) from hyperspectral images whose dimensionality is reduced by a Principal Component Analysis (PCA). Spatial correlation features of the hyperspectral image are then obtained using a Domain Transform Interpolated Convolution Filter. The last step is to fuse spatial texture features and correlation features for classification by LDM. The experimental results using the actual hyperspectral image indicate that the proposed GDFDTICF-LDM method is superior to other classification methods, such as the original Support Vector Machine (SVM) with raw spectral features, dimensionality reduction features and spatial-spectral information, methods of edge-preserving filter and recursive filter, and LDM-based methods.

The Correlation of Macrostructure, Microstructure, and Texture with Room Temperature Mechanical Properties of a Twinning‐Induced Plasticity Automotive Steel after Friction Stir Spot Welding/Processing

The microstructure and texture evolution of a commercialized twinning‐induced plasticity automotive steel during friction stir spot welding/processing are addressed. The welding is conducted at various rotational speeds of 1000, 1600, and 2500 rpm, considering effects of strain rate and heat input on microstructural evolution. Sub grains with low angle grain boundaries are mainly developed at the thermomechanically‐affected and stirred zones. At low rotational speed of 1000 rpm, continuous dynamic recrystallization occurred at the stirred zone, which eventually led to considerable grain refinement. At higher rotational speeds, dynamic recrystallization is also observed near the stirred zone. The static/dynamic microstructural evolution increased the extent of bimodality of the grain size distribution, and enhanced mechanical properties. The hook formation is hindered owning to the influence of the dynamic recrystallization on the material's flow during welding. Some fiber textures belonging to shear components are detected under low rotational speed. As deformation increased with rotational speed, shear texture further developed. Different types of shear textures of A‐Fiber and are observed at 1600 and 2500 rpm, respectively. The mechanical properties are explored in connection with macrostructure, microstructure, and texture evolutions.

Chondron curvature mapping in growth plate cartilage under compressive loading

The physis, or growth plate, is a layer of cartilage responsible for long bone growth. It is organized into reserve, proliferative and hypertrophic zones. Unlike the reserve zone where chondrocytes are randomly arranged, either singly or in pairs, the proliferative and hypertrophic chondrocytes are arranged within tubular structures called chondrons. In previous studies, the strain patterns within the compressed growth plate have been reported to be nonuniform and inhomogeneous, with an apparent random pattern in compressive strains and a localized appearance of tensile strains. In this study we measured structural deformations along the entire lengths of chondrons when the physis was subjected to physiological (20%) and hyper-physiological (30% and 40%) levels of compression. This provided a means to interpret the apparent random strain patterns seen in texture correlation maps in terms of bending deformations of chondron structures and provided a physical explanation for the inhomogeneous and nonuniform strain patterns reported in previous studies. We observed relatively large bending deformations (kinking) of the chondron structures at the interface of the reserve and proliferative zones during compression. Bending in this region may induce dividing cells to align longitudinally to maintain column formation and drive longitudinal growth.