Automated detection of multi-type defects of ultrasonic TFM images for aeroengine casing rings with complex sections based on deep learning

The manufacturing processes of casing rings are prone to multi-type defects such as holes, cracks, and porosity, so ultrasonic testing is vital for the quality of aeroengine. Conventional ultrasonic testing requires manual analysis, which is susceptible to human omission, inconsistent results, and time-consumption. In this paper, a method for automated detection of defects is proposed for the ultrasonic Total Focusing Method (TFM) inspection of casing rings based on deep learning. First, the original datasets of defect images are established, and the Mask R-CNN is used to increase the number of defects in a single image. Then, the YOLOX-S-improved lightweight model is proposed, and the feature extraction network is replaced by FasterNet to reduce redundant computations. The Super-Resolution Generative Adversarial Network (SRGAN) and Convolutional Block Attention Module (CBAM) are integrated to improve the identification precision. Finally, a new test dataset is created by ultrasonic TFM inspection of an aeroengine casing ring. The results show that the mean of Average Precision (mAP) of the YOLOX-S-improved model reaches 99.17%, and the corresponding speed reaches 77.6 FPS. This study indicates that the YOLOX-S-improved model performs better than conventional object detection models. And the generalization ability of the proposed model is verified by ultrasonic B-scan images.