Journal of Composites Science

In-plane shear is the dominant deformation mode during thermoforming of fiber-reinforced composites, and accurate characterization of shear behavior is essential for reliable forming simulations. The present work investigates the shear response of a unidirectional cross-ply UHMWPE material system (DSM Dyneema^® HB210) using the picture-frame test, with emphasis on sample configuration, normalization methods, and shear rate effects. Three cruciform sample sizes were tested at 120 °C, along with a configuration in which cross-arm material was removed to isolate the gage region. Finite element analyses using LS-DYNA^® were performed to evaluate the shear rate distribution during forming and to validate the experimental characterization. To maintain a constant shear rate during testing, a decreasing crosshead speed profile was implemented in the test software. Results showed that normalizing by the full specimen area yielded consistent shear stiffness curves across sample sizes, indicating that the arm region contributes equally to the load. Samples with cross-arm material removed exhibited greater scatter than those specimens without cross-arm material removed, confirming that preparation of cross-arm removal complicates repeatability. Rate dependence was observed at room temperature but not at elevated processing temperatures, suggesting that rate-dependent shear models are unnecessary for forming simulations of this material system. These findings provide a practical methodology for shear characterization of UHMWPE cross-ply laminates suitable for thermoforming analyses.

Radiation shielding in medical settings has traditionally relied on fixed structural models, with thicknesses and material composition determined by their shielding effect against direct X-rays. However, clinical practice increasingly demands lightweight and biocompatible shielding tools that can be locally applied to specific anatomical regions. Such tools should allow rapid installation and removal, skin protection, and disposable as well as continuous shielding. As a potential solution, this study aimed to improve the effectiveness of a cream-type material that directly coats the skin with shielding agents. A modeling pack was fabricated using bismuth oxide, an eco-friendly shielding material; zinc oxide, commonly utilized in cosmetics for ultraviolet protection; and alginate, which enhances skin adhesion by evaporating moisture. The effects of varying bismuth oxide and zinc oxide ratios on porosity and shielding performance were evaluated to establish assessment criteria for future commercialization. The experimental results demonstrated that higher proportions of bismuth oxide enhanced the shielding effect, while a linear change in shielding rate was observed at a thickness of 1.0 mm. Although pore structure variations were minimal, optimizing inter-particle arrangement may further improve skin adhesion. These findings suggest that cream-type radiation-shielding materials are highly promising for medical applications.

Composite structures are generally more susceptible to impact damage than non-composite structures, and early identification of damage is the primary goal of structural health monitoring (SHM). If such damage remains undetected or reaches a critical size, it can lead to sudden collapse and catastrophic failure. Modern SHM methods aim to preserve the integrity of composite structures through continuous inspection, monitoring, and damage assessment, including detection, localization, quantification, classification, and prognosis. These methods use sensor-based technologies to assess vibration, extension, and acoustic and thermal emission. This paper provides a review of various computational methods including physics-based methods (signal processing techniques, modal analysis, and finite element model updating) and optimization methods (inverse problems, particle swarm optimization, topology optimization, genetic algorithms, time series analysis, and hybrid techniques), alongside machine learning methodologies employing neural networks as well as deep learning for damage identification in composite structures. These computational and learning-based techniques are widely applied in the development of algorithms, optimization strategies, and hybrid frameworks for SHM. The review further summarizes the applications, advantages, and limitations of each method according to structure type and damage characteristics. The key emphasis of this review is on integrating computational approaches, as well as machine learning, to enhance the efficiency of damage identification. The conclusion is drawn based on an overview of the literature, focusing on the contributions of different computational methods and machine learning for damage identification in composites.