Polyethylenes are the most widely used polymers and are gaining more and more interest due to their easy processability, relatively good mechanical properties and excellent chemical resistance. The disadvantage is their low temperature stability, which excludes particular high-density polyethylenes (HDPEs) for use in engineering applications where the temperature exceeds 100 °C for a long time. One of the possibilities of improving the temperature stability of HDPE is a modification by accelerated electrons when HDPE is cross-linked by this process and it is no longer possible to process it like a classic thermoplastic, e.g., by injection technology. The HDPE modified in this way was thermally stressed five times at temperatures of 110 and 160 °C, and then the dynamic tensile behavior was determined. The deformation and surface temperature of the specimens were recorded by a high-speed infrared camera. Furthermore, two thermal methods of specimen evaluation were used: differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). The result of the measurement is that the modification of HDPE by accelerated electrons had a positive effect on the dynamic tensile behavior of these materials.

Background/Objectives: Developing functional tissue constructs via 3D bioprinting relies heavily on scaffold architecture, demanding precise mechanical tunability and high-resolution feature fidelity. Methods: This paper presents a novel approach utilising photocurable resins and resin 3D printing to fabricate auxetic axisymmetric chiral structures (ACSs), which can be used for advanced scaffold engineering. Results: The experimental tests showed that the optimised ACS (optACS) possess superior mechanical properties compared to their non-optimised counterpart. Both analysed structures possess an auxetic behaviour up to 40% longitudinal strain, with a Poisson’s ratio of about −0.1. Conclusions: This auxetic capability is promising for biomedical applications, particularly in developing enhanced stents or tissue scaffolds.

Digital Image Correlation (DIC) and infrared thermography (IRT) are widely used for full-field experimental analysis of materials and structures; however, direct thermal measurements are often constrained by limited access, thermally opaque safety enclosures, or the availability of infrared equipment. This study presents a deep learning-based framework for predicting full-field temperature distributions directly from a DIC-derived effective strain field. A supervised U-Net regression model was trained on paired effective strain–temperature data obtained from high-speed three-point bending experiments on aluminum specimens. The network learns a direct mapping between effective strain fields and corresponding temperature fields without requiring explicit thermomechanical modelling. The model’s performance was evaluated on an independent test set using RMSE, MAE, SSIM, and the coefficient of determination. The proposed framework achieved a coefficient of determination of up to R² = 0.985 and showed strong spatial agreement with measured temperature fields, particularly during highly mechanically active deformation stages. These results demonstrate that reliable full-field temperature distributions can be reconstructed solely from strain measurements, providing a practical alternative to infrared thermography in experimental configurations where thermal imaging is impractical or unavailable.

Standard fringe-based structured-light processing estimates wrapped phase from phase-shifted sinusoidal images and commonly relies on phase unwrapping to obtain a globally consistent phase representation. In practical measurements, this approach may become unstable on reflective objects and under low or non-uniform illumination, where the recorded fringe signal is distorted and the recovered phase becomes unreliable. To address these limitations, we propose a correspondence extraction method based on subpixel peak localization performed directly on phase-domain images. The wrapped phase is transformed into absolute value phase profiles, $\mathsf{\Phi }=\left|{\varphi }_w\right|$, whose local structure follows the projected fringe pattern and is less affected by object-dependent intensity variations. The proposed method reformulates correspondence extraction as a local signal-based estimation problem in the phase-domain, thereby reducing reliance on global phase-consistency constraints at the correspondence stage. A practical advantage observed in the evaluated examples is that the method remained usable in some regions where the phase became locally flat because of low modulation, saturation, or reflective surface effects. In such regions, conventional processing relies on sufficiently reliable phase gradients and subsequent unwrapping, whereas the proposed method uses local peak geometry in the transformed phase representation. In the implementation used here, Gray-code information is employed only for pixel-wise phase extension and reference indexing, not as a spatial phase-unwrapping mechanism. The method does not require machine learning models or training data and can be integrated as a correspondence analysis stage in practical structured-light systems.