DA-CNN-based similar terahertz signal identification for intelligent characterization of internal debonding defects of composites under high-resolution mode

Abstract

With the prevalent occupation of glass fiber reinforced polymer (GFRP) composites in engineering structures, quality inspection of GFRPs is particularly urgent to evaluate their health state. As a typical damage form during the manufacturing and lifetime service of GFRP, debonding defects not only degrades the structural strength and remaining performance of composite materials, but also brings about unpredictable challenge to overall safety of the system. Recently, the combination of terahertz (THz) spectroscopy and artificial intelligence (AI) technique has emerged great potential for automatic defect identification inside composites. However, conventional AI algorithms are difficult to classify similar THz signals and may degrade THz detection accuracy of defects due to limited feature extraction capability. Here we propose a deformable attention convolutional neural network (DA-CNN) framework-based THz characterization system, in which the defect datasets are established firstly by the THz time domain spectroscopy (THz-TDS), and then the DA-CNN framework is adopted to realize the automatic defect location and imaging by accurate THz signals classification. It is worth noting that the proposed DA-CNN framework has powerful feature extraction capability to automatically identify internal GFRP defects, especially for similar THz signals at the edge of debonding defects. A series of experiments have been performed to validate the effectiveness of proposed system, which will provide a new solution for intelligent and automatic THz characterization of internal debonding defects of composites.