Infrared thermal imaging detection and defect classification of honeycomb sandwich structure defects

Objective In order to realize the accurate classification of GFRP/NOMEX honeycomb sandwich structure defect types, an infrared thermal imaging detection system was built to collect heat maps of defects and healthy areas, and a GFRP/NOMEX honeycomb sandwich structure defect classification model was constructed by using convolutional neural network and transfer learning technology to realize quantitative detection of defect categories. Methods The experimental study of pulse infrared thermal imaging detection was carried out on the specimen. The training data set was constructed using the data obtained from the experiment, and the fine-tuned convolutional neural network model after transfer learning was trained to realize the quantitative detection of defect categories. Firstly, GFRP/NOMEX honeycomb sandwich structure specimens with delamination, debonding, water accumulation and glue plugging defects were prefabricated, and a pulsed infrared thermal wave detection system was built. The FLIR A655SC infrared thermal imager was used to collect the surface temperature distribution field of the specimens under pulse excitation. Secondly, the defects in the heat map are cut into 90 pixel×90 pixel, and the data are expanded by rotating 90°, 180°, 270°, horizontal flipping, vertical flipping and adding Gaussian noise operations. The pre-trained VGG16, MobileNetV2, ResNet50, InceptionV3, and DenseNet201 convolutional neural network models use transfer learning technology to fine-tune the back-layer structure of the network. Finally, the constructed data set is randomly divided into training set, verification set and test set, and the network is trained. The value φ and Accuracy are used as evaluation indexes to evaluate the generalization ability and classification effect of the model. Results and Discussions The VGG16, MobileNetV2, ResNet50 network, InceptionV3 and DenseNet201 network fine-tuned models based on transfer learning technology are trained (Fig.9). The VGG-16-1 network model has the fastest convergence speed, the network is stable, and the training process has no large fluctuations. At the same time, the confusion matrix is used to describe the classification results of the test set data by the six networks (Fig.10). It can be seen that the six models can realize the classification task of five categories of defects prefabricated by GFRP/NOMEX honeycomb sandwich structure. The values of φ and Accuracy are shown (Tab.4). The classification Accuracy of VGG16 and ResNet50 fine-tuned models reaches 99.94%, 99.10% and 98.95% respectively, and the scores of five categories ofφ are all higher than 96%. Compared with the two fine-tuning models of VGG16 network, the Accuracy and value of VGG-16-1 are higher than those of VGG-16-2. VGG-16-1 has only one misjudgment for the 1 612 defect data of the test set, and the network convergence speed is fast and stable, achieving a better classification effect. Although the overall score of ResNet50 is not as good as VGG-16-1, its network training speed is fast and can also achieve better classification effect. Conclusions The data set is constructed by using the real infrared images collected by the infrared thermal imager detection test, and the data is expanded for small samples. Based on the transfer learning technology, the network model structures of VGG16, MobileNetV2, ResNet50, InceptionV3 and DenseNet201 are fine-tuned, and the stability and convergence speed of the training process are compared and analyzed. Besides, the performance of the network was evaluated using a test set that did not participate in the training. The results show that by fine-tuning the transfer learning operation of the pre-trained classical convolutional neural network model, different types of defects of GFRP/NOMEX honeycomb sandwich structure can be well classified, and the quantitative detection of defect categories can be accurately realized. © 2024 Chinese Society of Astronautics. All rights reserved.