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Metal matrix composites (MMCs) combine metal with ceramic reinforcement, offering high strength, stiffness, corrosion resistance, and low weight for diverse applications. Al-SiC, a common MMC, consists of an aluminum matrix reinforced with silicon carbide, making it ideal for the aerospace and automotive industries. In this work, molecular dynamics simulations are performed to investigate the mechanical properties of the complex-shaped models of Al-SiC. Three different volume fractions of SiC particles, precisely 10%, 15%, and 25%, are investigated in a composite under uniaxial tensile loading. The tensile behavior of Al-SiC composites is evaluated under two loading directions, considering both cases with and without diffusion effects. The results show that diffusion increases the ultimate tensile strength of the Al-SiC composite, particularly for the 15% SiC volume fraction. Regarding the shape of the SiC particles considered in this research, the strength of the composite varies in different directions. Specifically, the ultimate strength of the Al-SiC composite with 25% SiC reached 11.29 GPa in one direction, and 6.63 GPa in another, demonstrating the material's anisotropic mechanical behavior when diffusion effects are considered. Young's modulus shows negligible change in the presence of diffusion. Furthermore, diffusion improves toughness in Al-SiC composites, resulting in higher values compared to those without diffusion, as evidenced by the 25% SiC volume fraction composite (2.086 GPa) versus 15% (0.863 GPa) and 10% (1.296 GPa) SiC volume fractions. [ABSTRACT FROM AUTHOR]

This study comprehensively investigates Al2O3′s mechanical properties, focusing on fracture toughness, surface energy, Young's modulus, and crack propagation. The density functional theory (DFT) is employed to model the vacancies in Al2O3, providing essential insights into this material's structural stability and defect formation. The DFT simulations reveal a deep understanding of vacancy-related properties and their impact on mechanical behavior. In conjunction with molecular dynamics (MD) simulations, the fracture toughness and crack propagation in Al2O3 are explored, offering valuable information on material strength and durability. The surface energy of Al2O3 is also assessed using DFT, shedding light on its interactions with the surrounding environment. The results of this investigation highlight the significant impact of oxygen vacancies on mechanical characteristics such as ultimate strength and fracture toughness, drawing comparisons with the effects observed in the presence of aluminum vacancies. Additionally, the research underscores the validation of fracture toughness outcomes derived from both DFT and MD simulations, which align well with findings from established experimental studies. Additionally, the research underscores the validation of fracture toughness outcomes derived from DFT and MD simulations, aligning well with findings from established experimental studies. The combination of DFT and MD simulations provides a robust framework for a comprehensive understanding of Al2O3′s mechanical properties, with implications for material science and engineering applications. [ABSTRACT FROM AUTHOR]

The effect of molybdenum additions on the phases, microstructures, and mechanical properties of pre-alloyed Ti6Al4V was studied through the spark plasma sintering technique. Ti6Al4V-xMo (where x = 0, 2, 4, 6 wt.% of Mo) alloys were developed, and the sintered compacts were characterized in terms of their phase composition, microstructure, and mechanical properties. The results show that the equiaxed primary alpha and Widmänstatten (alpha + beta) microstructure in pre-alloyed Ti6Al4V is transformed into a duplex and globular model with the increasing content of Mo from 0 to 6%. The changing pattern of the microstructure of the sample strongly influences the properties of the material. The solid solution hardening element such as Mo enhances mechanical properties such as yield strength, ultimate tensile strength, ductility, and hardness compared with the pre-alloyed Ti6Al4V alloy. [ABSTRACT FROM AUTHOR]

As Nickel (Ni) is the base of important Ni-based superalloys for high-temperature applications, it is important to determine the creep behavior of its nano-polycrystals. The nano-tensile properties and creep behavior of nickel polycrystalline nanopillars are investigated employing molecular dynamics simulations under different temperatures, stresses, and grain sizes. The mechanisms behind the creep behavior are analyzed in detail by calculating the stress exponents, grain boundary exponents, and activation energies. The novel results in this work are summarized in a deformation mechanism map and are in good agreement with Ashby's experimental results for pure Ni. Through the deformation diagram, dislocation creep dominates the creep process when applying a high stress, while grain boundary sliding prevails at lower stress levels. These two mechanisms could also be coupled together for a low-stress but a high-temperature creep simulation. In this work, the dislocation creep is clearly observed and discussed in detail. Through analyzing the activation energies, vacancy diffusion begins to play an important role in enhancing the grain boundary creep in the creep process when the temperature is above 1000 K. [ABSTRACT FROM AUTHOR]

In this study, a novel hybrid surrogate machine learning model based on a feedforward neural network (FNN) and one step secant algorithm (OSS) was developed to predict the load-bearing capacity of concrete-filled steel tube columns (CFST), whereas the OSS was used to optimize the weights and bias of the FNN for developing a hybrid model (FNN-OSS). For achieving this goal, an experimental database containing 422 instances was firstly gathered from the literature and used to develop the FNN-OSS algorithm. The input variables in the database contained the geometrical characteristics of CFST columns, and the mechanical properties of two CFST constituent materials, i.e., steel and concrete. Thereafter, the selection of the appropriate parameters of FNN-OSS was performed and evaluated by common statistical measurements, for instance, the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). In the next step, the prediction capability of the best FNN-OSS structure was evaluated in both global and local analyses, showing an excellent agreement between actual and predicted values of the load-bearing capacity. Finally, an in-depth investigation of the performance and limitations of FNN-OSS was conducted from a structural engineering point of view. The results confirmed the effectiveness of the FNN-OSS as a robust algorithm for the prediction of the CFST load-bearing capacity. [ABSTRACT FROM AUTHOR]