Due to the influence of mechanical vibration, high temperature creep and other factors, Si3N4 turbine blades are prone to surface defects. Besides, traditional algorithms are incapable to detect and classify surface defects simultaneously. Aiming at solving these problems, an algorithm for defect detection and classification of Si3N4 turbine blades based on convolutional neural network is proposed. The detection and classification network of this algorithm is optimized based on YOLOv5 network, the PAN structure and FPN structure of YOLOv5 are replaced by BiFPN structure. We establish the dataset of Si3N4 turbine blades, which is expanded by data enhancement. For the purpose of achieving a higher level of feature fusion, the PAN and FPN structures of the Neck part are replaced by BiFPN structure. As a result, the accuracy of detecting and classifying the surface defects by this algorithm is as high as 97.4%, and the detection speed is as low as 16ms. This optimized algorithm is able to solve the problems of traditional detection methods such as heavy workload, long time consuming and low accuracy. The algorithm provides a feasible approach for the quality detection of Si3N4 turbine blades and has certain engineering application value.
In order to improve the detection efficiency and image quality of Si 3 N 4 ceramic bearing balls surface defects, digital image processing technology is used to analyse the information characteristics of Si 3 N 4 ceramic bearing balls surface. A multi-scale decomposition enhancement algorithm for surface defect images of Si 3 N 4 ceramic bearing balls based on the stationary wavelet transform is proposed. By building the surface defects detection system of Si 3 N 4 ceramic bearing balls, the image enhancement program based on stationary wavelet transform with index low-pass filtering and nonlinear transform enhancement is designed. Finally, the effectiveness of the algorithm is verified by experiments. The experimental results show that the algorithm is applied to the surface defects image of Si 3 N 4 ceramic bearing balls can effectively weaken the background noise and surface grinding texture, and enhance the contrast between defects and background clearly. In addition, the binary image is obtained by an adaptive threshold binary algorithm. After removing the tiny points by morphological opening operation, the defects are accurately and completely segmented, and then the Canny operator is used for edge detection to extract the edge contour of defects. When the decomposition level is set to 3, the average calculation time is 0.88 s, which are relatively short and have sufficient precision, and the algorithm can be extended to other kinds of ceramic ball surface damage detection.
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.
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