Polymer-based composite materials are widely used in a number of engineering applications owing to their light weight, mechanical strength, chemical resistance and versatility in design. Using these materials in foam form offers several additional advantages, including reduced density, improved heat and sound insulation, and increased impact resistance. In the contemporary era, intensive research is being conducted into the evaluation of petrochemical-based polymers, such as polypropylene (PP) and polyamide 6 (PA6), and biopolymers, particularly polylactic acid (PLA), in foamed composite structures. Furthermore, hybrid systems formed by the combination of these different polymer types offer great potential for balancing environmental sustainability and performance. This review discusses the general structure, foam formation mechanisms, production techniques and basic properties of polymer-based foam composite materials. Prominent polymer-based systems, such as PP, PA6 and PLA, were examined comparatively. Additionally, contemporary methodologies concerning hybrid foam systems formed by PLA with PP and PA6, as presented in the existing literature, were evaluated.
Silicone rubbers are high-performance polymers which, in the case of high consistency silicone rubber (HCR), are processed in the extrusion process, for example. Due to the relatively high material price, the material can be foamed to reduce costs and to adjust certain properties. This study therefore investigated the influence of nucleating agents on extruded high consistency silicone foams where water was used as blowing agent. The types of crosslinker investigated showed clear differences in their resulting foam structure and mechanical properties with the talcum and calcium carbonate agents used.
The goal of this research is to examine how certain 3D printing parameters affect the compression behavior of Polylactic Acid (PLA) sandwich beams with a chiral core topology. The parameters examined include printing speed, infill density, and build orientation. These sandwich beams are lightweight, high-strength structures used in the construction, automotive, and aerospace industries. Their enhanced performance can also benefit the energy sector, as well as sports and medical equipment. The findings of this work will provide a framework for advancing materials science and additive manufacturing, helping to improve 3D printing procedures to achieve better mechanical properties. This research employs the Taguchi approach for experimental design to investigate how build orientation, printing speed, and infill density influence the compression behavior of PLA sandwich beams with chiral core topology. The findings revealed that build orientation of 0 degrees, print speed of 30 mm/s and infill density of 60% provided the most optimal and superior mechanical performance as compared to all other specimens. Build orientation had the greatest influence among all parameters, contributing between 36.0% and 50.5% to the mechanical characteristics. This result aligns with previous studies, which often identify build orientation as a key factor affecting the mechanical properties of 3D-printed structures. Infill density also played an important role, contributing between 16.5% and 26.0% to strength and stiffness. Print speed's contribution varied more widely, ranging from 2.0% to 30.8%.
This study investigates the application of Deep Evidential Regression in shallow feed-forward neural networks to model and quantify the compressive stress response of expanded polystyrene foam. This foam material, widely utilized for impact protection and packaging, exhibits distinct mechanical behavior characterized by elasticity, plateau, and densification stages during compressive loading. This research adopts a data-driven approach, leveraging artificial neural networks enhanced with evidential learning to predict the distribution of stress responses, thereby addressing both aleatoric and epistemic uncertainties. The methodology involves organizing stress-strain data into training, validation, and test sets, adding noise to simulate real-world conditions, and training models with evidential layers. Results demonstrate that the proposed models maintain high predictive accuracy, with coefficients of determination exceeding 0.90 for noisy test data and above 0.99 for noise-free data. The evidential regression models also provide robust uncertainty quantification, essential for applications where data quality varies. This study's findings highlight the efficiency and effectiveness of Deep Evidential Regression in enhancing the reliability of stress-strain predictions for EPS foam, offering significant potential for broader application to similar foam materials.
Radar absorbing materials (RAMs) have become increasingly important in modern stealth technologies due to the growing need to minimize radar signatures. However, most RAMs suffer from high weight and limited structural flexibility, which restrict their practical use in lightweight and conformal applications. Therefore, developing lightweight and structurally adaptable absorbers has become a critical research priority, and this study addresses this need by evaluating the effects of polyurethane foam (PUF) density and morphology on their electromagnetic absorption performance. PUF composites with different matrix densities (30, 80, and 120 kg/m 3 ) were prepared by incorporating nickel-coated carbon fillers at varying loadings. In addition, to examine the effect of filler localization, additional samples with identical composition were fabricated using three preparation strategies, introducing the filler into the polyol phase, the isocyanate phase, or equally dividing it between both. These approaches resulted in distinct filler distributions, allowing the evaluation of how spatial arrangement influences electromagnetic behavior. As expected, increasing foam density progressively enhanced absorption performance, with notable attenuation observed only at densities of 80 kg/m 3 and above. A well-balanced cellular morphology was obtained by equally dividing the filler between both phases, resulting in homogeneous localization across both cell interiors and walls. This strategy facilitated more regular foam formation compared to polymeric methylene diphenyl diisocyanate ( pMDI ) based dispersion and offered comparable absorption performance, while clearly outperforming the polyol-only method in efficiency and thickness. As a key outcome, a PUF with an experimental density of 0.553 g/cm 3 achieved a minimum reflection loss ( RL min ) of −41.80 dB at 4.3 cm thickness, while maintaining Reflection Loss ( RL ) values below −10 dB across the entire X-band. Overall, the results demonstrate that both foam density and filler localization serve as effective design parameters to tune dielectric behavior and optimize absorption in lightweight polyurethane-based RAMs.
