The edge artifact extraction method for Si3N4 ceramic bearing rolling element microcracks characterization.

Aiming at the problem that the edge artifacts of Si3N4 ceramic bearing rolling element microcracks have low contrast, contain noise, and easily merge with the background, making it difficult to segment. A method based on 2D discrete wavelet transform and Otsu threshold segmentation is designed to achieve the extraction of microcrack edge artifact features. Wavelet decomposition is used to remove noise, while wavelet reconstruction features are used to restore lost details. Creation of 2D discrete wavelet transform functional equations combining wavelet reconstruction and wavelet decomposition to improve contrast and eliminate noise in images featuring edge artifacts. For the problem of feature edge artifacts that are difficult to remove, the threshold segmentation function equation is designed to maximize the interclass variance, and the optimal threshold value is selected to remove the edge artifacts. The experimental results show that the average PSNR of the Si3N4 ceramic bearing rolling body point, line, and surface microcrack edge artifact feature images enhanced by the method in this paper is close to 62.69 dB, and the average SSIM is about 0.77. The method in this paper improves the contrast of microcrack edge artifact features of Si3N4 ceramic bearing rolling bodies and makes the feature extraction effect of point, line, and surface microcrack edge artifacts more complete.