In this study, static bending behavior of fiber-reinforced multilayered symmetric composite micro-beams is analytically investigated using modified couple Stress Theory. The Laplace transform method is applied to solve the governing differential equations. The micro-scale stiffness coefficient is extended in terms to fully reflect layer-based anisotropy. Through comprehensive parametric analyses, the effects of size, stacking sequence, fiber orientation angle, and material anisotropy on static displacement are evaluated. The results show that as the size effect parameter increases, the "size-dependent stiffening" mechanism stabilizes the structure and reduces displacements. It is revealed that classical theory underestimates stiffness in microstructures, and that the outer layer fiber angles and the degree of material anisotropy play a decisive role in the severity of this size effect.
This paper examines the static bending behavior of functionally graded (FG) nanobeams using a thorough analysis within the context of trigonometric shear deformation theory. A mathematical model of the FG nanobeam has been developed, in which the power-law distribution of the material characteristics varies continuously through the thickness direction. Size-dependency at the nanoscale has been explained by Eringen's nonlocal elasticity theory. Unlike traditional beam theories, no shear correction factor is required since the fundamental equation of the FG nanobeam is developed using the trigonometric function that reflects the non-parabolic shear stress distribution throughout the thickness. The governing differential equations of the static bending of the FG nanobeam have been solved using the Navier solution method under simply supported boundary conditions. The correctness and efficiency of the presented study are demonstrated by the comparison studies carried out. The obtained results reveal in detail the effects of the nonlocal parameter, power-law index, and length-to-thickness ratio on the static bending of the FG nanobeam.
This research studies the mathematical modelling and buckling behaviour of sandwich beam structures featuring a homogeneous metallic core and porous, functionally graded material (FGM) face sheets. A power-law distribution is used to model the continuous variation in material properties across the thickness of the FGM layers. To evaluate the impact of manufacturing-induced micro-voids, two specific porosity topographies—uniform (even) and cosine-based (uneven) distributions—are embedded into the constitutive relations. The governing equations of equilibrium are established using Timoshenko beam theory, ensuring that transverse shear deformation is rigorously accounted for. The resulting differential equations are solved using the Chebyshev Pseudospectral Method, which converts the formulation into a robust eigenvalue problem. The CPM achieves spectral convergence, with the relative error dropping below 10⁻⁹ using as few as N = 12 collocation points. The critical buckling loads agree with exact analytical benchmarks to four decimal places (e.g., β = 9.8709 vs the exact value 9.8696 for a homogeneous simply-supported beam, a relative error of 0.013%). For the baseline FGM sandwich configuration (L/h = 10, p = 1.0, α = 0.1, λ = 0.4, P-P boundary conditions), the dimensionless buckling load is β = 25.99 under the uniform porosity model (Model I) and β = 27.45 under the cosine porosity model (Model II). A comprehensive parametric study explores the synergistic influence of the power-law exponent, porosity volume fraction, thickness-to-length ratios, and diverse boundary conditions on the critical buckling thresholds. The results indicate that, while porosity generally reduces structural stiffness, the uneven distribution model leads to a greater reduction in stability than its uniform counterpart. Additionally, a comparison with Euler-Bernoulli theory underscores the need to employ shear-deformable models for deep beams. The developed semi-analytical framework serves as a computationally efficient tool for the structural optimization of porous FGM-based sandwich systems in high-performance engineering fields.
This study provides a bibliometric analysis of the scholarly literature on aging methods for wood structural elements and their effects on material performance. Focusing on the concept of "aging," the research examines changes in the structural integrity, chemical composition, and mechanical properties of timber induced by natural and accelerated aging processes. A total of 725 publications from the Web of Science Core Collection were analyzed using VOSviewer software, covering author citation networks, document impact, keyword co-occurrence, and international collaboration patterns. The results indicate a 23% increase in publications over the last three years, reflecting a growing focus on service life prediction and aging mechanisms. The analysis identifies a methodological evolution in the literature, shifting from conventional destructive testing to non-invasive approaches, including Non-Destructive Testing (NDT) and Ultrasonic Velocity. Within the global collaboration network, characterized by significant contributions from the USA, France, and China, the integration of Turkey into European research consortia is observed. This study synthesizes existing literature gaps and offers insights for advancing hybrid testing protocols in sustainable timber engineering.
Soft robots, composed of compliant polymers and hydrogels, can safely interact with humans and fragile environments but remain vulnerable to cuts, punctures, and tears, which limit their service life. Self-healing polymers and hydrogels have therefore been proposed as sustainable solutions for soft robotics because they can recover their mechanical and functional properties after damage either autonomously or under mild external stimuli. In parallel, electronic skin (e-skin) platforms are required to endow robots with environmental perception; graphene- and graphene oxide (GO) based conductive, stretchable and self-healing materials have emerged as promising candidates for such skins. In this work, graphene- and GO-based self-healing e-skin systems for soft robotics are evaluated and compared. Polyurethane–GO networks can achieve tensile strengths in the range of 30–80 MPa and toughness values of 250–500 MJ m⁻³ while maintaining mechanical self-healing efficiencies of 80–90 % at room temperature. Photothermally triggered GO composites can further provide 88–99 % healing within 20–60 s under infrared irradiation. Graphene/GO-reinforced hydrogel and elastomer e-skins operate over wide strain ranges (300–850 % and above) and exhibit competitive gauge factors, response times and electrical healing efficiencies compared with state-of-the-art ionic and polymeric self-healing e-skins. Overall, the results indicate that graphene- and GO-based self-healing e-skins are strong candidates for endowing soft robots with both sensing and self-protection capabilities.
