Bast fibers are promising natural materials known for their biodegradability, affordability, and eco-friendliness, making them an alternative to synthetic options. Extensive research has been conducted to examine the effects of integrating various bast fiber reinforcements into epoxy and polystyrene matrices to boost the properties of the composite materials. However, there is limited research on ensete fiber and its utilization as a reinforcement that needs more in-depth research to be used as an alternative bast fiber. This paper aimed to predict and compare the performance of ensete fiber composites with six other bast fiber-reinforced polystyrene and epoxy composites. In this study, flax, hemp, jute, ramie, banana and kenaf were selected bast fibers for comparison purposes. This article employed various micromechanics models and finite element method (FEM), varying the fiber volume fraction. Our findings revealed that hemp fiber-reinforced composites exhibited the best predicted elastic properties, while banana fiber-reinforced composites showed the weakest performance. Notably, composites made with ensete fibers outperformed those made with jute and banana fibers in both epoxy and polystyrene matrices. Comparisons were made between results from the micromechanics models and FEM for all bast fiber-reinforced epoxy and polystyrene composites and there was an agreement between the effective elastic properties and fiber volume fraction (FVF). Further, bast-fiber reinforced epoxy composites showed higher values than polystyrene for strain analysis while for stress analysis, polystyrene composites showed higher stress loads than epoxy composites.

The prediction of crack initiation and propagation of damage initiation and propagation in composite structures has gained significant attention due to the increasing use of these materials in the aerospace industry. In this context, estimating progressive damage in composite ply is crucial, as it refers to the gradual failure and deformation of the structure, which can lead to a reduction in the useful life and safety of the structure. By examining these damages, it is possible to identify the causes and factors contributing to their occurrence and to propose suitable solutions for preventing and repairing the damages. In the present study, an effort is made to develop numerical, analytical, and experimental approaches for modeling and estimating progressive damage at mechanical joints in composite aircraft structures, considering quasi-static loading, including tensile loading. The study incorporates an investigation of damage mechanisms such as fiber breakage, matrix cracking, and delamination that commonly occur in composite laminates under mechanical stress. Combining modeling and experimental results allows for a comprehensive understanding of damage evolution, enabling the formulation of strategies aimed at improving the durability and safety of composite structures in aerospace applications. Ultimately, based on the results of modeling and experiments, strategies will be proposed to enhance the lifespan of the structure.

This paper explores the nonlinear static response of bio-inspired helicoidal laminated composite (BiHLC) plates supported by a Pasternak medium. A combined analytical framework is established by integrating the mixed interpolation of tensorial components (MITC3i) approach with the first order shear deformation plate theory (FSDT). The Pasternak foundation is characterized by its spring stiffness k1 and shear stiffness k2. Based on the Lagrangian energy principle and von Kármán nonlinear theory, the governing equations are formulated and numerically solved through the Newton–Raphson iterative procedure. The effectiveness of the novel method is verified through comparisons with published documents. Additionally, the effects of helicoidal stacking sequences, geometric configurations, boundary constraints, and foundation rigidity on the large deflection behavior are analyzed. An artificial neural network (ANN) model is also proposed to estimate displacements efficiently, eliminating the dependence on finite element computations.

Conventionally, the deep beam shear strength is analyzed with codes (mechanics and empirical models). The purpose of this investigation is to provide an alternative way of accurately estimating the shear capacity of Reinforced Concrete Deep Beams (RCDBs), including those with and without shear reinforcements (WOR and WWR), by adopting machine learning models. Four machine learning algorithms: k-Nearest Neighbor (kNN), Random Forest, M5Rules, and Sequential Minimal Optimization for Regression (SMOReg), were considered, and the selection was based on their performance in previous related studies. A database of 733 samples for WWR and 378 samples for WOR was compiled, utilizing 14 and 8 input features, respectively, in each case. WEKA, an open-source software suite, was used in preprocessing the data and also tuning the hyperparameters. SMOReg beat other models for WOR with an R² value of 0.9607, while Random Forest did best for WWR with an R² value of 0.9667 in the testing sets. The shear strengths predicted by the machine learning models were compared to four traditional standard codes. The results show that the machine learning models beat conventional methods by a large margin, while also being consistent with earlier models generated using machine learning. This demonstrates the model's prediction accuracy and robustness.

This decade has observed an upsurge in the eco-friendly materials because of the development of composites using natural fibers. These composites are made from renewable resources and are gaining popularity for their high performance in engineering applications. Industries are increasingly interested in using materials that are sustainable and resource-efficient. This research proposes a new innovative hybrid composite developed using coir and kenaf fibers, carbon nanotubes acting as a nanofiller, and a matrix made up of epoxy resin, detailing how they are fabricated, tested, and optimized based on different weight percentages. The weight percentages considered for CNT nanoparticles are 0, 1, 2, and 3 wt.%, coir, and kenaf fibers are considered in weight percentages of 12, 13, 14, and 15, whereas thickness is regarded as 2,3,4 and 5 mm. This research evaluates the mechanical features of this hybrid composite fabricated using a vacuum bag molding process. The different composite samples are tested using mechanical tests and subsequently optimized using the design of experiment (i.e., Taguchi method) and analysis of variance (ANOVA) method to arbitrate the best weight percent combination of the innovative hybrid composite. On the basis of the optimization results, the best composite sample obtained includes, 3 wt% of CNT, 15 wt% of kenaf, 15 wt% of Coir, and 4mm thickness of the sample, as it yields the highest tensile modulus and strength among all the hybrid composite samples. The outcomes from the research indicate that the hybridization of kenaf fibers into coir fibers, along with CNTs as fillers in the hybrid composite has enhanced the overall tensile strength, and flexural strength of the hybrid composite in comparison to the coir composite and kenaf composite alone, depicting the superiority of natural fiber hybrid composite over synthetic fiber hybrid composite.

In the present work, modeling of hoop stress in the defect-free structural steel pipe was done under the combined effect of internal hydrostatic pressure and temperature difference using finite element analysis (FEA) and response surface methodology (RSM). The FEA simulation was done on the quarter-ellipsoidal sections of the structural steel pipe specimen using ANSYS 2022 R1 software. The calculated hoop stress using FEA was in agreement with the analytical solution of hoop stress. The thermodynamically induced hoop stress due to the temperature difference (without internal hydrostatic pressure) exhibited a compressive state at the inner wall and a tensile state at the outer wall of the specimen. This compression-tension state also provided a thermodynamic situation at which the total hoop stress becomes null at the neutral axis. However, when the specimen was subjected to internal hydrostatic pressure (in addition to the temperature difference), the initial neutral axis received a thermomechanical hoop stress of a tensile nature. A drop in the burst pressure from 11.5 MPa to 9.9 MPa was observed when the steel pipe was subjected to a maximum temperature difference of 40 °C. A new analytical equation for thermomechanical hoop stress was developed using RSM modeling by considering independent variables as the normalized position in the wall (ṝ), internal hydrostatic pressure (P), and temperature difference (ΔT). The developed analytical equation envisages that the interacting effect of independent variables ṝ(ΔT) was maximum, followed by the interacting effect of ṝ(P). An optimum internal hydrostatic pressure of 7.43 MPa was calculated considering the flow stress of the material for all possible combinations of the other two independent variables (ṝ and ΔT). Furthermore, at the optimum ΔT of 39.65 °C, the interacting effect of the ṝ(P) provided contour-curvature plots, both below the yield strength and endurance limit, considering different combinations of normalized position in the wall and internal hydrostatic pressure.

The automotive industry has experienced rapid development in recent years, with a significant increase in the number of vehicles in China. To effectively reduce the energy consumption and carbon emissions of automobiles, implementing lightweight design for the steering knuckle structure is essential. In this paper, in order to prepare steering knuckle parts with less weight, the material of steering knuckle was changed from 40Cr to polyether ether ketone (PEEK), which has less density, while maintaining the performance of the part. Fused deposition molding (FDM) technology, as one of the main methods of thermoplastic material manufacturing, has the ability to machine any complex geometrical structure, which greatly enhances the degree of design freedom. However, in the case of FDM technology, the print parameters determine the performance of the printed sample. Based on this, this paper will explore the influence of process parameters on the mechanical properties of PEEK materials through orthogonal experiments to screen out the optimal parameter combinations for printing. For the printing layer height of 0.1mm, the temperature of the holding chamber is 90 ℃, the filling method for the spiral tetrahedron. The PEEK materials molded under the optimal parameters of FDM were insulated to investigate the effect of the heat treatment process on the mechanical properties of PEEK materials. The loaded condition of the steering knuckle in each working condition is calculated by the basic parameters of the car, the dangerous cross section of the part is determined by simulation, and the structural optimal design of the part is carried out by topology optimization. Under the premise of ensuring its mechanical properties, minimize the amount of material to achieve the goal of lightweight.

This study investigates the fracture behavior of bimaterials, specifically focusing on the Alumina/Silver interface under mechanical, thermal, and thermo-mechanical loading conditions. Through the analysis of Stress Intensity Factors (SIFs) across various crack lengths, temperatures, and along multiple regions and fronts of the crack, we provide valuable insights into the distribution and the nature of stresses, shedding light on how different loading conditions influence crack propagation. Our findings show that, under mechanical loading, the tensile mode SIF (KI) exhibits a straightforward relationship with applied stress, increasing with crack length. In contrast, under thermal loading, KI generally decreases on the surface as the temperature rises, while it increases within the interface, highlighting the complex interplay of thermal expansion and the mismatch of material properties. The thermo-mechanical case combines these effects, further amplifying the role of residual stresses from manufacturing processes and thermal stresses, significantly affecting SIFs and crack growth, especially in bimaterial interfaces. These results contribute to a deeper understanding of fracture mechanisms in hybrid materials.

The present study follows the identification of crack orientations in industry-driven carbon/epoxy laminated composite beams (LCB) using buckling data through fuzzy logic application. The study proposed a novel buckling-based crack detection with fuzzy logic aid towards the assessment of health and functionality of LCBs. The critical buckling loads are numerically computed on ABAQUS finite element (FE) simulation software. Towards crack detection, an efficient hybrid Mamdani fuzzy inference system (FIS) is developed in the MATLAB platform. The hybrid FIS is formed by fusing three standalone membership functions (Gaussian, Trapezoidal and Triangular). The numerically computed first four modes of buckling loads are provided as input values to the hybrid Mamdani FIS and after defuzzification, the output is read as crack orientations. The output results are validated with the critical buckling loads experimentally arrived through INSTRON 8862 Universal Testing Machine (UTM). Furthermore, the present developed FIS is compared with other standalone methods (triangular, trapezoidal and Gaussian) and the analysis results highlight the superiority of the developed FIS in crack detection as it bears the closest resemblance to the experimental results. Besides, the approach reveals the process of feature learning and the crack detection in the buckling domain.