An automated fluorescent defect detection system in precision castings by convolutional neural network

Abstract

Abstract Fluorescent Penetrant Inspection (FPI) is a widely used Non-Destructive Testing (NDT) method in the aerospace industry to inspect precision casting components. Currently, FPI inspection relies on visual examination, which can lead to challenges in distinguishing between defects and false indications. Moreover, human factors introduce variability in the results, impacting the consistency and reliability of the inspection process. This highlights the desirability of the automation of FPI to increase consistency, reliability and productivity. The deep learning method is gradually replacing the traditional approaches that involve image processing and machine learning classifiers in automated defect detection system. Deep learning method offers automatic feature extraction and high robustness, which contribute to more accurate and efficient defect detection. The use of various convolutional neural networks (CNN) in defect detection for flat superalloy plates processed with FPI and photographed to create digital images was investigated. Among the CNN models, MobileNetV2 exhibited outstanding performance, with a remarkable recall rate of 99.2% and an accuracy of 99.2%. Additionally, the effect of dataset imbalance on model performance was carefully examined. Moreover, the features extracted by the model are visualized using Guided Grad-CAM to reveal the attention of the CNN model to the fluorescent display features. These results underscore the strong capability of deep learning architectures in detect defects in aerospace precision casting components, paving the way for the automation of the entire FPI process.