Recognition and classification of microscopic fatigue fracture images of high-strength bolt using deep learning methods

Abstract

The fracture surface of high-strength bolt after fatigue fracture contains a lot of information, such as the location of stress concentration and the distribution of fatigue cracks. In this study, a large number of scanning electron microscope (SEM) images of fatigue fracture surface of broken high-strength bolt were identified and classified using the method of deep learning. At the beginning, a data set of SEM images containing 1556 fatigue fractures of high-strength bolts was prepared. Then, three convolutional neural networks, VGG16, ResNets50 and MobileNets, were used to recognize and classify the images in the dataset. In this process, part of the convolution layer of ResNets50 was extracted for visualization. At the same time, the Loss-Epoch curves, accuracy, recall and confusion matrices of the three networks were derived to evaluate the nets. Finally, the network with the highest accuracy was selected to adjust the parameters to further improve the accuracy of the classification. It was found that the three nets can complete the classification of these images. MobileNets had the best performance for this classification task, and the accuracy rate after adjusting the parameters has reached 86.76%. For some images with obvious features, the recall rate of classification had reached 100%. However, images from the same fatigue area were prone to a small amount of confusion. Finally, the feature map of the network would become more abstract with the deepening of the network, and the features of the image concerned by each convolution layer were also different.