A Transformer-based neural network for automatic delamination characterization of quartz fiber-reinforced polymer curved structure using improved THz-TDS

Abstract

Quartz fiber-reinforced polymer (QFRP) is a vital non-polar material used in aviation wave-transparent structural components. Automatic characterization of delamination defects in QFRP is critical to aviation structural component safety. Terahertz time-domain spectroscopy (THz-TDS) is one of the new non-destructive testing (NDT) methods with highly accurate characterization of internal defects in non-polar material. Hence, attempts to extract features of THz time-domain signals for automatic characterization have been made by using deep learning algorithms. In this work, a Transformer-based neural network to classify the THz time-domain signals collected from a QFRP curved structure for automatic characterization of pre-embedded delamination defects has been reported. A THz-TDS system combined with a collaborative robot for collecting the THz signals from QFRP curved structure has been built. An automatic characterization method framework is developed. Results show that the precision rates of Transformer-based neural network for $1_{st}$ delamination to $5_{th}$ delamination are 1.0, 1.0, 1.0, 0.985, 1.0, and $F_1$ score of it is 0.982. During the process of testing, delamination defects inside the QFRP curved structure were visualized using pixels with different colors. Results indicate that the Transformer-based neural network can characterize all pre-embedded delamination defects while minimizing false identification of non-defective areas, performing outstanding generalization.