A Coupled 3d Morphological Reconstruction Approach for Surface Microcrack in Si3 n4 Ceramic Bearing Roller Based on Adaptive Nano Feature Extraction & Multiscale Depth Fusion.

To extract the fuzzy contour features, tiny depth features of surface microcracks in the Si3 N4 ceramic bearings roller. An adaptive nano feature extraction and multiscale deep fusion coupling method is proposed, to sufficiently reconstruct the three-dimensional morphology characteristics of surface microcracks. Construct an adaptive nano feature extraction method, form the surface microcrack image scale space and the Gaussian difference pyramid function equation, realize the detection and matching of global feature points. The sparse point cloud is obtained. Through polar-line correction, depth estimation, and fusion of feature points on the surface microcracks image, a multiscale depth fusion matching cost pixel function is established to realize a dense point cloud reconstruction of surface microcracks. The reconstruction results show that the highest value of the local convex surface reconstructed by the dense point cloud reaches 1183 nm, and the lowest local concave surface is accurate to 296 nm. Compared with the measurement results of the confocal platform, the relative error of the reconstruction result is 24.6%. The overall feature-matching rate of the reconstruction reaches 93.3%. It provides a theoretical basis for the study of surface microcrack propagation mechanism and the prediction of bearing life.

Molecular Dynamics Analysis of 6H-SiC Subsurface Damage by Nanofriction.

To investigate the subsurface damage of 6H-SiC nanofriction, this paper uses molecular dynamics analysis to analyze the loading process of friction 6H-SiC surfaces, thus providing an in-depth analysis of the formation mechanism of subsurface damage from microscopic crystal structure deformation characteristics. This paper constructs a diamond friction 6H-SiC nanomodel, combining the radial distribution function, dislocation extraction method, and diamond identification method with experimental analysis to verify the dislocation evolution process, stress distribution, and crack extension to investigate the subsurface damage mechanism. During the friction process, the kinetic and potential energies as well as the temperature of the 6H-SiC workpiece basically tend to rise, accompanied by the generation of dislocated lumps and cracks on the sides of the 6H-SiC workpiece. The stresses generated by friction during the plastic deformation phase lead to dislocations in the vicinity of the diamond tip friction, and the process of dislocation nucleation expansion is accompanied by energy exchange. Dislocation formation is found to be the basis for crack generation, and cracks and peeled blocks constitute the subsurface damage of 6H-SiC workpieces by diamond identification methods. Friction experiments validate microscopic crystal changes against macroscopic crack generation, which complements the analysis of the damage mechanism of the simulated 6H-sic nanofriction subsurface.

Machine vision system based on a coupled image segmentation algorithm for surface-defect detection of a Si3N4 bearing roller.

Defect detection is a critical way to ensure quality for silicon-nitride-bearing rollers. To improve detection efficiency and precision for silicon-nitride-bearing roller surface defects, in this paper, a novel machine vision system for the detection of its surface defects is designed. This method combines image segmentation and wavelet fusion to extract features from an image. In turn, the features are used in a classifier based on the K-nearest neighbor for defect classification. The optimized image segmentation algorithm that is combined with wavelet fusion is the innovation of the proposed method. It is evaluated using different defect images acquired by the machine vision system. Our experiments show that the proposed machine vision system's precision in anomaly detection of the silicon-nitride-bearing roller surface can achieve 98.5%; further, its classification precision of various defects is greater than 91.5%. It has resulted in a solution for the automatic identification of the silicon-nitride-bearing roller surface defects.