Silicone-based polymer composites containing high atomic number additives are prioritized for the development of new materials for radiation shielding, due to their mechanical, thermal, electrical, and multifunctional properties. The X-ray attenuation properties, as well as mechanical properties, of the newly developed-lead-free multi-layered structures for radiation shielding, based on silicone composite layers containing tin, cerium oxide, tungsten oxide, and bismuth additives, are analyzed and discussed in this paper. It is shown that, by varying the additive concentrations in silicone composites, lead-free and flexible layered structures, exhibiting lead-equivalent X-ray shielding, can be fabricated.

This work is aimed at the development of finite element models and prediction of the mechanical behavior of MXene nanosheets. Using LS-Dyna Explicit software, a finite element model was designed to simulate the nanoindentation process of a two-dimensional MXene Ti₃C₂T_z monolayer flake and to validate the material model. For the evaluation of the adhesive strength of the free-standing Ti₃C₂T_z-based film, the model comprised single-layered MXene nanosheets with a specific number of individual flakes, and the reverse engineering method with a curve fitting approach was used. The interlaminar shear strength, in-plane stiffness, and shear energy release rate of MXene film were predicted using this approach. The results of the sensitivity analysis showed that interlaminar shear strength and in-plane stiffness have the largest influence on the mechanical behavior of MXene film under tension, while the shear energy release rate mainly affects the interlaminar damage properties of nanosheets.

During the frontal crash the longerons absorb most energy of all vehicles construction elements. In order to analyse the energy absorbing capabilities of longerons under axial compression loading and to evaluate the influence of longerons geometrical characteristics and materials degradation on the vehicles safety experimental investigations and numerical calculations were performed. To assess the crashworthiness of longerons the main objective was to study the behaviour of thin-walled structural elements under axial loading conditions using the Finite Element (FE) model. The numerical FE models were created using the computer code LS-DYNA. Two models of longerons were investigated with different sections shape and for each of them materials with the four different mechanical characteristics were applied. Validation of created FE model was performed according to the experimental investigation and the results were obtained of validated FE models of vehicles crash analysis [1]. The results of analyses show that the value of absorbed energy by the longerons of new vehides exceeds the value of the oldest cars. The degradation of structures in the old cars has the significant influence on the absorbed energy.

Investigation of Mechanical Properties of Hemp and Flax Fibers Hybrid Composites for Biomedical Applications

Digital image correlation is an experimental optical non-contact full field displacement and strain evaluation method based on the surface subsets tracking with photo cameras, digital images processing, and numerical computation. However, the full field of strain computation is a challenging problem, mainly because the displacement field to be differentiated is not continuous, individual subsets are tracked by the optical digital image correlation system. Moreover, the numerical differentiation can also amplify the noise of the displacement field inducing thus strain errors when the displacement data are poor. The peridynamics theory (which equations are cast in terms of spatial integrals of displacements, instead of spatial derivatives in the classical continuum mechanics) based algorithm is considered in this study and applied for the experimental digital image correlation displacement field to analyze possible peridynamic differentiation method advantages. A strains convergence analysis between the digital image correlation and peridynamic differentiation methods is done in this study. The integro-differential strain calculation as an alternative method is validated against digital image correlation and finite element simulation strain fields. It is also shown that the digital image correlation, a noisy displacement field, still provides an accurate and low level noise strain evaluation based on the proposed method.

Damage assessment in engineering materials is essential for structural reliability and safety. While traditional imaging techniques and Digital Image Correlation (DIC) provide valuable insights into deformation and crack evolution, they often require significant manual effort and suffer from accuracy limitations under complex loading conditions. Recent advances in Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), have enabled the development of automated, high-resolution, and near real-time damage assessment techniques. This paper reviews methods that integrate ML with DIC to assess damage in composites, metals, and other engineering materials. We compare conventional ML models with modern DL architectures, discuss key challenges, and propose future research directions. The findings demonstrate that coupling DIC with ML significantly improves the accuracy, speed, and reliability of damage identification in engineering materials.