Automatic radiographic testing for aeroengine turbine blades based on deep learning

Radiographic testing for aeroengine turbine blades usually depends on artificial detection. To avoid the influence of various artificial factors such as experience difference，eye fatigue and standard under⁃ standing，and to solve the problem of high cost，time consuming and low efficiency，a defect detection algorithm named DBFF-YOLOv4 was proposed for aeroengine turbine blade X-ray images by employing two backbones to extract hierarchical defect features based on YOLOv4. A novel concatenation form containing all feature maps was designed as the neck of defect detection framework. An automatic defect detection model for turbine blade X-ray images was established. Nine cropping cycles for one defect，flipping，brightness increasing and decreasing were applied for expansion of training samples and data augmentation. Finally，an automatic defect detection model was trained and test based on these defect samples. The results show that the defect detection model，which ob⁃ tained 96.7% average precision and 91.87% average recall within the score threshold of 0.5 for complete turbine blade，outperformed others built by using the common object detection algorithm YOLOv4 directly. In addition，cropping nine times and data augmentation methods can significantly improve the defect detection accuracy of the model（mean average precision increased by 59.19% and 2.53% respectively）. This study provides a new method of automatic radiographic testing for turbine blades. © 2024 Journal of Propulsion Technology. All rights reserved.