Research on TFM localization and detection of damage in plates based on CNN-BiLSTM and ResNet networks
To address the limitations of Total Focusing Method(TFM)imaging technology in industrial applications due to its time-consuming nature,this paper presents a rapid TFM imaging approach based on a CNN-BiLSTM(Convolutional Neural Network-Bi-directional Long Short-Term Memory)network.This method initially employs a CNN to extract key features from the full matrix data,followed by leveraging a BiLSTM network to predict the location of the damage on metal plates.Subsequently,TFM technology is used for precise imaging in the damaged areas.Furthermore,to enhance the accuracy of the damage detection,this paper also introduces a damage size detection method based on the ResNet network to achieve precise measurement of the damage size.To validate the effectiveness of the proposed method,a three-dimensional aluminum plate simulation model was established using the finite element analysis software ABAQUS,and a neural network dataset was constructed through model transformation.Experimental results demonstrate that compared to traditional TFM imaging methods,the CNN-BiLSTM network exhibits higher region localization precision,with an accuracy rate of 95.26％,and a significant efficiency advantage,with an average positioning speed increased by 46.4％.Additionally,the detection results of the damage size have validated the effectiveness and accuracy of the method based on the ResNet network in damage size assessment,achieving an accuracy rate of 99.26％on the test set.
万方数据
维普
