https://sam.ensam.eu:443 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Mon, 08 Jun 2026 14:11:23 GMT 2026-06-08T14:11:23Z http://hdl.handle.net/10985/26382 Damage Assessment of Polyamide-Based Woven Composites Using Multi-Directional Lamb Waves After Fatigue or Impact Loading MIQOI, Nada; POMAREDE, Pascal; MERAGHNI, Fodil; DECLERCQ, Nico; DELALANDE, Stéphane This study presents a novel experimental methodology designed to assess damage in woven glass fibers reinforced polyamide 6,6/6 composites, specifically subjected to low-velocity impact and cyclic tensile loading. Conventional ultrasonic testing techniques often fail to detect subtle material degradation, particularly when dealing with barely visible impact damage (BVID), which can go unnoticed but still significantly compromise structural integrity. In contrast, the proposed approach utilizes multi-directional ultrasonic Lamb wave analysis, a more advanced technique that offers greater sensitivity and precision in identifying damage at various stages of the composite’s lifespan. In this work, a damage indicator is defined based on the velocity profile of Lamb waves, which are sensitive to changes in material properties such as stiffness degradation. The Lamb wave-based methodology is rigorously validated through detailed comparisons with X-ray tomography. These comparisons reveal strong correlations between the two techniques, highlighting the effectiveness of the proposed ultrasonic approach in detecting BVID. Moreover, the study demonstrates that this methodology is not only highly sensitive but also scalable, making it suitable for industrial applications where automated inspection of composite components is essential. The proposed method offers a significant advancement in non-destructive testing (NDT) techniques based on Lamb wave diagnostic tools in composite material testing. Thu, 01 May 2025 00:00:00 GMT http://hdl.handle.net/10985/26382 2025-05-01T00:00:00Z MIQOI, Nada POMAREDE, Pascal MERAGHNI, Fodil DECLERCQ, Nico DELALANDE, Stéphane This study presents a novel experimental methodology designed to assess damage in woven glass fibers reinforced polyamide 6,6/6 composites, specifically subjected to low-velocity impact and cyclic tensile loading. Conventional ultrasonic testing techniques often fail to detect subtle material degradation, particularly when dealing with barely visible impact damage (BVID), which can go unnoticed but still significantly compromise structural integrity. In contrast, the proposed approach utilizes multi-directional ultrasonic Lamb wave analysis, a more advanced technique that offers greater sensitivity and precision in identifying damage at various stages of the composite’s lifespan. In this work, a damage indicator is defined based on the velocity profile of Lamb waves, which are sensitive to changes in material properties such as stiffness degradation. The Lamb wave-based methodology is rigorously validated through detailed comparisons with X-ray tomography. These comparisons reveal strong correlations between the two techniques, highlighting the effectiveness of the proposed ultrasonic approach in detecting BVID. Moreover, the study demonstrates that this methodology is not only highly sensitive but also scalable, making it suitable for industrial applications where automated inspection of composite components is essential. The proposed method offers a significant advancement in non-destructive testing (NDT) techniques based on Lamb wave diagnostic tools in composite material testing. http://hdl.handle.net/10985/27143 Automated classification of subsurface impact damage in thermoplastic composites using depth-resolved terahertz imaging and deep learning SILITONGA, Dicky Januarizky; POMAREDE, Pascal; BAWANA, Niyem Mawenbe; SHI, Haolian; DECLERCQ, Nico; CITRIN, David; MERAGHNI, Fodil; LOCQUET, Alexandre Reliable detection of barely visible impact damage is critical to ensure the structural integrity of composite components in service, particularly in safety-critical applications such as pressure vessels and transportation systems. This study presents a solution for detecting such damage in woven glass fiber-reinforced thermoplastic composites using terahertz (THz) time-of-flight tomography and convolutional neural networks. THz provides non-contact, non-ionizing, high-axial-resolution imaging of subsurface and back-surface damage, addressing key limitations of surface-based inspection methods. While THz imaging alone may not always permit conclusive damage identification, we bridge this gap by training neural network classifiers on depth-resolved THz B-scan images using ground truth from co-located X-ray micro-computed tomography. Among several pretrained architectures tested via transfer learning, DenseNet-121 exhibits the highest accuracy. The model remains robust even when trained on truncated B-scans excluding surface indentation features, confirming its ability to detect structural anomalies located internally or on the back surface. This is particularly relevant for applications where back-side access is not feasible. Experimental validation is performed on impacted glass-fiber-reinforced thermoplastic coupons prepared in accordance with ASTM D7136, with damage severity quantified through force–displacement data and micro-tomographic analysis. Labeling for supervised learning conforms to acceptance criteria from industrial standards for composite pressure vessels (ASME BPVC Section X, CGA C-6.2), ensuring regulatory alignment and enabling deployment in quality control workflows. The proposed method minimizes the need for expert interpretation or secondary validation and offers direct applicability to in-service inspection and manufacturing quality control. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10985/27143 2026-01-01T00:00:00Z SILITONGA, Dicky Januarizky POMAREDE, Pascal BAWANA, Niyem Mawenbe SHI, Haolian DECLERCQ, Nico CITRIN, David MERAGHNI, Fodil LOCQUET, Alexandre Reliable detection of barely visible impact damage is critical to ensure the structural integrity of composite components in service, particularly in safety-critical applications such as pressure vessels and transportation systems. This study presents a solution for detecting such damage in woven glass fiber-reinforced thermoplastic composites using terahertz (THz) time-of-flight tomography and convolutional neural networks. THz provides non-contact, non-ionizing, high-axial-resolution imaging of subsurface and back-surface damage, addressing key limitations of surface-based inspection methods. While THz imaging alone may not always permit conclusive damage identification, we bridge this gap by training neural network classifiers on depth-resolved THz B-scan images using ground truth from co-located X-ray micro-computed tomography. Among several pretrained architectures tested via transfer learning, DenseNet-121 exhibits the highest accuracy. The model remains robust even when trained on truncated B-scans excluding surface indentation features, confirming its ability to detect structural anomalies located internally or on the back surface. This is particularly relevant for applications where back-side access is not feasible. Experimental validation is performed on impacted glass-fiber-reinforced thermoplastic coupons prepared in accordance with ASTM D7136, with damage severity quantified through force–displacement data and micro-tomographic analysis. Labeling for supervised learning conforms to acceptance criteria from industrial standards for composite pressure vessels (ASME BPVC Section X, CGA C-6.2), ensuring regulatory alignment and enabling deployment in quality control workflows. The proposed method minimizes the need for expert interpretation or secondary validation and offers direct applicability to in-service inspection and manufacturing quality control.