Detection of Low-Velocity Impact Damage in Woven-Fabric Reinforced Thermoplastic Composite Laminates by Deep-Learning Classification Trained on Terahertz-Imaging Data

Abstract

Terahertz (THz) imaging is gaining attention as a nondestructive testing technique for assessing damage due to its high axial resolution and nonionizing nature, presenting a promising alternative to conventional methods such as ultrasound and X-ray imaging. Its practical implementation, however, remains limited by the reliance on expert interpretation and the frequent need for validation using supplementary techniques such as X-ray microcomputed tomography (µCT), particularly for complex damage modes. This study focuses on woven-fabric-reinforced thermoplastic composites subjected to low-velocity impact, which typically causes barely visible impact damage (BVID). The damage is subtle yet critical, potentially leading to failure under subsequent loading. The multilayered and spatially distributed characteristics of BVID make it especially challenging to identify. To overcome these challenges, this work integrates deep learning with pulsed THz time-of-flight tomography (TOFT) imaging to enable automated damage detection in composite laminates. In contrast to existing research that mainly targets delamination using A- or C-scan data, this study emphasizes the detection of low-velocity impact damage by leveraging THz B-scans, which offer nondestructive depth-resolved cross-sectional imaging. The training dataset is labeled by correlating THz TOFT scans with X-ray CT images used as ground truth. A transfer learning approach, based on convolutional neural network (CNN) architectures, is employed for binary classification to distinguish damaged from undamaged regions. The resulting classifier achieves over 95 % accuracy, demonstrating the viability of this method for industrial applications such as quality assurance and in-service inspection of composite structures.