An automatic delamination characterization method for detection of CFRP curved structure using laser infrared thermography based on Res-Transformer

Carbon fiber reinforced polymer (CFRP) is one of the most vital structural materials used in the aviation industry. Automatic identification is becoming a key method for timely detection of the internal defects in CFRP structural components to ensure the aviation safety. In this paper, an innovative automatic characterization framework based on a Res-Transformer neural network is proposed. To identify the pre-embedded delamination defects with varying depths in CFRP curved structures, an infrared thermography detection system has been built to collect relevant signals. In the neural network, a data processing module is designed to enhance the characteristic differences between the raw temperature-time series signals from delamination defects with different depth. The residual module and Transformer encoder are developed to improve both the local and global features extraction ability while reduce the computational cost. Results indicate that our method is capable of accurately characterizing all pre-embedded delamination defects while effectively reducing the misidentification of non-defective areas. The comprehensive evaluation index F1-score of this method reaches 92.81%, which is superior to other mainstream methods, showing its applicability and advancement.