蜂窝夹层结构缺陷红外热成像检测及缺陷分类

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

为实现GFRP/NOMEX蜂窝夹层结构常见缺陷的准确分类,基于红外热成像无损检测技术,采用卷积神经网络及迁移学习技术建立GFRP/NOMEX蜂窝夹层结构缺陷的分类模型,比较微调的VGG16、MobileNetV2、ResNet50、InceptionV3、DenseNet201模型对于缺陷的分类效果,结果表明以上模型针对GFRP/NOMEX蜂窝夹层结构蒙皮脱粘缺陷、夹层脱粘缺陷、积水缺陷、堵胶缺陷及健康区域均可实现准确的识别分类,其准确率均在94%以上。其中基于VGG16的两种迁移学习模型及ResNet50的迁移学习模型对于此数据集的分类效果优于其余几种经典模型的迁移学习网络,其准确率分别达到99.94%、99.10%、98.95%,五个类别φ值得分均高于96%,可实现缺陷区域及健康区域的有效分类。

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 backlayer 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 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 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 of are all higher than 96%.Compared with the two finetuning 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 1612 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 finetuning 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.