Surface defect detection and classification of Si3N4 turbine blades based on convolutional neural network and YOLOv5

Abstract

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.