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To predict the relation between extension evolution law and damage of line microcracks on silicon nitride bearing balls. Firstly, the 3D point cloud features of line microcracks are obtained based on multi-image 3D reconstruction. Next, the point cloud diagram data is optimized to fit the molecular dynamics geometric model of line microcracks extension process. Then, according to the Si 3 N 4 molecular dynamics deep learning potential, the molecular dynamics mechanical model is established. Finally, Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) is used to numerically solve the extension evolution of line microcracks. The experiment result shows that from the change analysis of single-layer surface mesh, the microcracks extension is concentrated in the central region of the indentation. The new microcracks spread along both sides of the original microcracks. After uniformly distributed load is applied to line microcracks, there is a phenomenon of concentrated swirling microcracks formation under the indentation region. By analyzing the atomic coordination number nephogram, the microcracks extension occurs when the proportion of amorphous atoms is up to 7 % during loading. And the plastic deformation occurs when the proportion of amorphous atoms is dropped to 6 % during unloading. In addition, in the process of indentation loading, plastic deformation and slip zone occur on Si 3 N 4 bearing balls surface. However, the strain is accumulated in the middle region during unloading. The slip zone moves inward and becomes thin, and the elastic deformation partially is recovered. [ABSTRACT FROM AUTHOR]

Aiming at the characteristics of parametric discrete and non-significant depth of silicon nitride bearing roller surface microcracks. A multi-scale split matching method is proposed. To realize the full reconstruction of the characteristics of the three-dimensional morphology of the surface microcracks. Combined with the parametric discretization characteristics of the surface microcracks, a box filter is defined to do second-order Gaussian differentiation on the image. Construct a multiscale Hessian matrix in the scale space of the surface microcrack image. Determine the covariance-discrete feature points, and obtain the global covariance-discrete sparse point cloud of silicon nitride bearing roller surface microcracks by non-maximum value suppression. For the non-significant depth features of silicon nitride bearing roller surface microcracks. Dissect the surface microcrack distortion images. Divide each image into multiple regional pixel blocks and construct an aggregation function to determine the exact depth value of each pixel block at the cost of matching each pixel block with its surrounding pixel blocks. Dense point cloud reconstruction of non-significant depth features of silicon nitride bearing roller surface microcracks is realized. The average distribution of dense clusters of reprojection errors of the dense point cloud features is less than 0.4pix, the median residual of the field of view of the surface microcrack image is 0.57pix, and the total number of effectively extracted feature points is about 92.9 %. The detailed lossless recovery reconstruction of the parametric discrete, non-significant depth features of silicon nitride bearing roller surface microcracks is realized. A new idea is provided for the establishment of molecular dynamics geometric modeling of the surface microcrack extension process on the silicon nitride bearings roller. [ABSTRACT FROM AUTHOR]

Aiming at solving the problems of low detection efficiency, poor accuracy rate, and low applicability of the traditional detection methods for surface defects on the silicon nitride (Si3N4) bearing cylindrical rollers. In this study, to detect the surface defects on Si3N4-bearing cylindrical rollers, a nondestructive testing (NDT) method based on an optimized convolutional neural network is proposed, which uses machine vision system to detect the surface defects. The optimized convolutional neural network is a two-stage network. It combines a semantic segmentation sub-network and a decision sub-network. The semantic segmentation sub-network performs an end-to-end learning based on the features of the original image. It completes defect feature extraction to segment the surface defect area from the normal area. The decision sub-network classifies the defects in the segmented surface defect area. The experimental results show that the detection time of surface defects on Si3N4-bearing cylindrical rollers by the proposed network was 72 ms. In addition, its Accuracy (Acc), Percision (Pre), Sensitivity (Sen), and Specificity (Spe) were 97.5%, 99.9%, 99.0%, 98.6%, respectively, which significantly improved the Acc and Pre of the detection of surface defects on Si3N4-bearing cylindrical rollers compared with the traditional detection method. On the other hand, its mIoU, mPA were 84.4%, 92.9%, respectively, meaning brilliant ability of image segmentation for this network. To sum up, the ability of the proposed network to detect and classify surface defects on Si3N4-bearing cylindrical rollers is excellent. [ABSTRACT FROM AUTHOR]

For the profile-distortion of Si 3 N 4 bearing rolling elements microcracks image. A coupling method of variable-scale truncated median filtering and detection-driven active contour model is proposed. Details are as follows. The sigmoid activation function is constructed to dynamically adjust the circular window. The local threshold truncation is designed to adapt to the characteristics of different regions in the image. The area threshold is defined to eliminate non-target features. The mean peak signal-to-noise ratio (PSNR) of filtered images is about 40.263. The edge overlap rate is as low as 0.1027. The mean accuracy of feature extraction is about 94.33 %. To sum up, the influence result from profile-distortion on the accuracy of feature extraction of Si 3 N 4 bearing rolling elements microcracks is effectively solved. [ABSTRACT FROM AUTHOR]

The features of shallow depth and gradual blurring are characterized by the point microcrack defects in Si 3 N 4 ceramic bearing rollers. Point clouds accuracy and mesh correctness of 3D morphological reconstruction are affected. A coupled method is proposed for reconstructing the 3D morphology, based on small-scale structure from motion and nonlinear image clustering vision. The fine-tuning motion supply system is designed based on the shallow depth characteristics of point microcrack defects. The sparse point clouds of point microcrack defects are obtained. The gradually blurring of the point microcrack defects is analyzed. Thus, the nonlinear image clustering and convergence functions are established. The dense point clouds and texture mesh of point microcrack defects are reconstructed. The experimental results show the diameter of the circular region containing the point microcrack defects is 17.798 μm. The maximum local longitudinal span of the point microcrack defects is 239 nm. The number of sparse point clouds vertex, dense point clouds vertex and texture mesh face is 535, 13206, 21086, respectively. The reconstructed results are in well agreement with the raw image segmentation results. The theoretical basis for the research on the mechanism of point microcrack defects propagation and the prediction of bearing life is provided. [ABSTRACT FROM AUTHOR]

• Quickly determine and locate whether the complete defective image is complete or not. • Stitch the complete defect image with smooth and clear pixels. • Extract background feature layers for grayscale equalization. • The image itself can realize self-defined defect enhancement. To address the problems of difficulty in extracting complete defects and high interference of surface background in the process of ZrO2 bearing ball defect detection. A method of ZrO2 bearing ball surface representation defect enhancement based on complete defect stitching and self-defined background balance algorithm is proposed. To determine the location of complete defects, we propose a scanning and acquisition scheme for sub-aperture images and quickly stitch the edges of sub-aperture images. The image feature point gradient definition is optimized, and a less error Euclidean distance finding method is used for feature point matching. The stitched complete defect image is smooth and has high pixel accuracy. The background feature layer of the complete defect image is extracted for background gray leveling, and this process is introduced into the γ-value calculation process. The γ setting allows the image to be self-defined enhancement, outputting a fully defective image with a balanced background and outstanding defects. The average gradient is reduced by more than 90% after complete defect image enhancement, and the information entropy is preserved by about 40%. The defect edges of the image are intact, and the fine texture of the defect features is preserved. [ABSTRACT FROM AUTHOR]

In order to investigate the deformation and damage of nano-twin 6 H-SiC after friction, a three-dimensional model was established. It is found that the barrier formed by layered faults can block the propagation of stress and hinder the formation and expansion of dislocation. The strength of the twins shows Hall-Petch phenomenon, and the strength is the highest when the thickness is 10.3 nm. The twin boundary is the initial point of deformation damage, and the greater the thickness of the twin, the greater the damage. The "Point-Line-Plane" destruction in twin crystals has a great influence on the processing of crystal materials. [ABSTRACT FROM AUTHOR]

In response to the difficulties in evaluating the thin layer interface bonding of composite materials accurately, an ultrasound delay time spectrum method is proposed. A mathematical model, with spring model simulates the stiffness coefficient of interfacial bonding, is established based on the ultrasound delay time spectrum evaluation of interfacial stiffness coefficient by the rule of the acoustic wave vertical incident to double-layer medium. Numerical simulation results show that with the increase of stiffness coefficient, the delay time peak becomes smaller, and the resonant frequency is independent of the interfacial stiffness coefficient but related to the thickness of the thin layer. The period of resonant frequency has a great positive correlation with the thickness of the thin layer. Finally, a series of experiments are carried out for the 1 mm thickness rubber thin layers with different bonded interfaces. Prepared the weakly bonded interface and the perfect interface compared with the infinite stiffness coefficient, respectively. The results show that the delay time peak of the weak bonding interface is much greater than that of the perfect interface. The delay time peak of the perfect interface is slightly larger than that of the infinite stiffness coefficient interface. Therefore, the experimental results illustrate that it is feasible to apply the ultrasound delay time spectrum method in evaluating the interfacial bonding. [ABSTRACT FROM AUTHOR]