Convolution neural network fusion high power halogen lamp induced thermal-wave diffusion multidimensional features: An intelligent defect identification approach for aviation honeycomb sandwich composites

HSCs defects may have a serious impact on the mechanical properties of the material. Accurate and reliable non-destructive defect detection of materials can reduce safety hazards. Therefore, a reliable technology for HSCs defect detection is needed. In this present study, a new non-destructive detection method that integrates a convolutional neural network (CNN) and high-power halogen lamp-induced thermal-wave diffusion multidimensional features were introduced to realize the intelligent classification and quantitative characterization of honeycomb sandwich composites (HSCs) defects. Firstly, HSC specimens with delamination defects were detected by the halogen lamp-induced lock-in thermography (LIT) algorithm, and feature images were extracted. After image pre-processing, the images are used as defect feature datasets. Furthermore, combining the four-layer feature pyramid structure and Transformer, a CNN model named YOLOLIT is designed to achieve intelligent classification and identification of defects and quantitative characterization of defect sizes. Subsequently, the optimal hyperparameter combination is obtained by iterating the hyperparameters of the neural network using a genetic algorithm. In a complex environment, accurate recognition of circular defects with a minimum diameter of Φ6mm is achieved, and the mAP of up defect is 90.2%, down defect is 86.2%, remove defect is 72.9%, and the average recognition speed of 0.2 s/image is achieved. Finally, the relation between defect size and detection error as well as that between LIT frequency and detection error is studied. The detection error range and optimal LIT frequency are obtained. The experimental results illustrated this method may be a potential infrared non-destructive defect detection solution.