Quantitative detection of composite defects based on infrared technology and FA-LSTM

Abstract

Abstract Given the difficulties in obtaining the defect data of composite materials and the low accuracy of defect size detection, this paper proposes a quantitative defect detection method for composite materials based on infrared technology and a firefly algorithm (FA)-optimized long short-term memory (LSTM) network. First, composite plates with different defects were periodically heated and photographed using a specific heat source and infrared camera to obtain the maximum surface temperature difference and the best detection time data of the composite plates. Then, the maximum temperature difference and the best detection time of each cycle of the material plate are weighted averages, and a vector model containing the defect feature information is established. Next, the optimal number of hidden neurons and the learning rate are obtained by the FA optimization of the LSTM model parameters. Finally, the eigenvector model is input into the FA-LSTM for fitting and outputting the defect diameter and depth to realize the quantitative detection of material plate defects. The experimental results show that the FA-LSTM can accurately identify the defect diameter and depth of the material plate, with an average relative error of only 2.59%.