| Title: |
Detection of Low-Velocity Impact Damage in Woven-Fabric Reinforced Thermoplastic Composite Laminates by Deep-Learning Classification Trained on Terahertz-Imaging Data |
| Authors: |
Silitonga, Dicky J.; Pomarede, Pascal; Bawana, Niyem M.; Shi, Haolian; Declercq, Nico F.; Citrin, D.S.; Meraghni, Fodil; Locquet, Alexandre |
| Contributors: |
Laboratoire d'Etude des Microstructures et de Mécanique des Matériaux (LEM3); Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Arts et Métiers Sciences et Technologies; Georgia Tech Lorraine Metz; Georgia Institute of Technology Atlanta -Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC); Université Bourgogne Franche-Comté COMUE (UBFC)-Université Bourgogne Franche-Comté COMUE (UBFC)-Arts et Métiers Sciences et Technologies; School of Electrical and Computer Engineering - Georgia Insitute of Technology (ECE GeorgiaTech); Georgia Institute of Technology Atlanta; George W. Woodruff School of Mechanical Engineering; Roberval (Roberval); Université de Technologie de Compiègne (UTC); The authors acknowledge the support of Institut Carnot ARTS, Conseil Régional Grand Est, and theFrench National Research Agency (ANR) under the PIA project “Lorraine Université d’Excellence”:ANR-15-IDEX-04-LUE.; Laboratoire d’Étude des Microstructures et de Mécanique des Matériaux LEM3; ANR-15-IDEX-0004,LUE,Isite LUE(2015) |
| Source: |
26ème Congrès Français de Mécanique; https://hal.science/hal-05319253; 26ème Congrès Français de Mécanique, Laboratoire d’Étude des Microstructures et de Mécanique des Matériaux LEM3, Aug 2025, Metz, France |
| Publisher Information: |
CCSD; Association Française de Mécanique (AFM) |
| Publication Year: |
2025 |
| Collection: |
Université de Technologie de Compiègne: HAL |
| Subject Terms: |
Convolutional Neural Networks (CNN); Nondestructive Evaluation; Terahertz; BVID; Glass-fiber reinforced polymer; [SPI.MECA.MEMA]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Mechanics of materials [physics.class-ph]; [SPI.MECA.SOLID]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Solid mechanics [physics.class-ph] |
| Subject Geographic: |
Metz; France |
| Description: |
National audience ; 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. |
| Document Type: |
conference object |
| Language: |
English |
| Availability: |
https://hal.science/hal-05319253; https://hal.science/hal-05319253v1/document; https://hal.science/hal-05319253v1/file/LEM3_CFM_2025_MERAGHNI4.pdf |
| Rights: |
https://about.hal.science/hal-authorisation-v1/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.FE83F4FF |
| Database: |
BASE |