International Journal of Mechanics and Materials in Design最新文献

Porous orthotropic thin-walled I-beams (TWI-Bs) are commonly used in aerospace, civil, and mechanical applications due to their high stiffness-to-weight ratio. However, accurately predicting their lateral-torsional buckling (LTB) behavior remains challenging, especially under non-uniform loading and in the presence of material porosity and orthotropy. Classical beam theories often fail to capture essential deformation mechanisms, particularly shear deformation and warping effects, which become significant in porous and thin-walled configurations. This study develops a novel analytical model for evaluating the LTB response of porous orthotropic doubly symmetric I-beams subjected to non-uniformly distributed transverse loadings. To the author’s knowledge, this study is among the first to integrate an HSDT-based thin-walled beam formulation with porosity-dependent orthotropic constitutive modeling to the elastic lateral–torsional buckling of thin-walled I-beams under various non-uniform transverse loadings. Three trigonometric-based non-uniform porosity distribution patterns are considered. The solution is obtained using Galerkin’s method, and the model is validated against available benchmark solutions. The results reveal that neglecting higher-order shear deformation leads to a significant overestimation of critical buckling loads, especially for beams with high porosity or under non-uniform loading conditions. Among porosity patterns, NUDP1 yields the highest buckling resistance, whereas NUDP2 results in the lowest. The position of the applied load and its distribution type (e.g., TRL, TGL) substantially influence the LTB behavior, particularly in shear-sensitive configurations. Geometric parameters, such as flange and web thickness ratios and the orthotropy ratio, further interact with porosity and loading to affect buckling performance. These findings underscore the importance of incorporating advanced shear deformation models and realistic porosity distributions to ensure accurate LTB predictions and a robust structural design.

Porous orthotropic laminated plates are increasingly used in lightweight structural applications due to their high stiffness-to-weight ratio and tunable mechanical behavior. However, their vibrational performance is strongly affected by the distribution of porosity, the lamination scheme, and interaction with the underlying support medium, particularly when resting on elastic foundations. This study aims to analyze the fundamental natural frequencies of porous orthotropic laminated plates considering various porosity distribution patterns (PDP, UDP, NUDP1-3), lamination sequences, and two types of elastic foundation models: Winkler and orthotropic Pasternak foundations. The equations of motion are derived using Hamilton’s principle and higher-order shear deformation theory (HSDT) and solved analytically via Galerkin’s method. A comprehensive parametric study is conducted to assess the impact of foundation stiffness, shear layer stiffness ratio, porosity coefficient, orthotropy ratio, aspect ratio, side-to-thickness ratio, and fiber orientation angle on the dynamic response. The results reveal that increasing foundation stiffness significantly enhances natural frequencies and reduces the adverse effects of porosity on structural stiffness. Orthotropic shear interactions further amplify frequency gains, particularly in soft porosity distributions. Lamination sequences with higher in-plane stiffness and lower fiber angles exhibit better vibrational capacity. Additionally, geometric and material orthotropy parameters significantly impact the frequency trends, with elastic foundations enhancing configuration-specific behaviors.

This research explores the nonlinear dynamic behaviors of sigmoid functionally graded (SFG) cylindrical shells (CSs) reinforced with spiral stiffeners (SSs) utilizing a semi-analytical approach. The shell is subjected to a temperature distribution along its thickness, and the material properties of both the shell and stiffeners vary continuously across the thickness in accordance with a power-law distribution and temperature-dependent relations. To model the system, classical shell theory (CST), von Kármán nonlinear kinematics, the smeared stiffener approach, and the Galerkin technique are employed. To examine the system’s vibrational behaviors, the method of multiple scales (MMSs) is applied, targeting internal resonances (1:1/2:1/4) and a superharmonic resonance of order 2/1. This leads to the derivation of a six-degree-of-freedom nonlinear averaged system. This study, for the first time, presents a detailed numerical analysis that uncovers key dynamic behaviors of the system, including waveforms, phase portraits, and Poincaré maps, emphasizing how changes in stiffener angles influence the nonlinear response of the SFG-CSs reinforced with SSs under internal and superharmonic resonances. In addition, the results demonstrate that optimizing the angles of SSs is a viable strategy for enhancing the dynamic stability of the current system.

This study presents a hybrid modelling approach for the damped vibration analysis of functionally graded nanoplates supported by generalized viscoelastic foundations. The plates, composed of ceramic–metal mixtures, are described by higher-order shear deformation theory and a newly formulated modified nonlocal strain gradient theory, accounting simultaneously for nonlocal and strain gradient effects. The foundation model extends the visco-Pasternak type to include two stiffness parameters and two damping coefficients. Closed-form Navier solutions are derived and used both for a comprehensive parametric study and as training data for a neural network surrogate model. The surrogate enables rapid vibration predictions without repeating the full analytical process. The results demonstrate strong consistency between the analytical and ANN-based predictions, confirming the reliability of the proposed hybrid approach. Parametric results highlight the coupled influence of size-dependent effects, gradation profiles, and foundation damping on dynamic characteristics. The proposed framework effectively balances theoretical rigor and computational efficiency, providing a practical reference for vibration prediction and preliminary design of micro- and nano-scale structural components.

Maraging steel, known for its high tensile strength and fracture toughness, is crucial in high-performance aerospace applications like airplane landing gear, helicopter undercarriages, and rocket engine casings. FSW, a solid-state joining procedure, is frequently utilized for softer materials, including aluminum, magnesium, and zinc. Its efficacy has generated interest in using FSW in steel and titanium alloys. High strength and heat generation make welding maraging steel problematic and affect deformation, residual stresses, and joint stability. This study employs Comsol Multiphysics to conduct a transient heat transfer analysis of FSW in maraging steel, focusing on understanding heat distribution and predicting peak temperature obtained in the workpiece under different rotational speeds and welding speeds. The simulation analyzes the peak temperature obtained during FSW of maraging steel at various tool rotational speeds (TRS: 50–250 rpm) and welding speeds (WS: 10–50 mm/min). The findings indicate that when TRS increases, the peak temperature increases, whereas increasing WS causes the peak temperature to decrease. TRS is recognized as the most significant factor determining thermal input. The numerical model has been evaluated using Response Surface Methodology (RSM), resulting in outstanding statistical agreement ((R^{2} = 99.99%), adjusted (R^{2} = 99.97%), projected (R^{2} = 99.86%)). Tool rotational speed (TRS) exhibited the greatest impact, as confirmed by ANOVA ((F = 27{,}504.36), (p < 0.001)). The low standard deviation ((text {SD} = 5.51)) implies a minimal difference between predicted and simulated peak temperatures. Precise regulation of heat transfer behavior is vital to ensure proper tool design and parameter selection during the FSW of maraging steel. The study directly deals with transient heat transfer behavior during FSW, which leads to its wider application in advanced structural alloys.

This study investigates the nonlinear dynamic behavior of perforated functionally graded porous (PFGP) sandwich nanobeams to enhance the design of nano-electromechanical resonators that require nonlinear stability. The proposed structure comprises a perforated core with a regular square-hole network, sandwiched between two functionally graded porous face layers. Axial compression, thermal loading, adatom adsorption, shear deformation, and size-dependent effects are modeled through Eringen’s nonlocal elasticity theory combined with von Kármán geometric nonlinearity. Hamilton’s principle is employed to derive the governing equations, while van der Waals (vdW) interactions between adatoms and the substrate are described using the Lennard–Jones (6–12) potential. The equations are reduced via the Galerkin method and analytically solved using the method of multiple scales to determine the nonlinear resonance frequency. The results reveal that the resonance frequency and stiffness of the nanobeam are significantly affected by the distribution of porosity, perforation geometry, adsorbed adatoms, nonlocal parameters, and temperature variation. Adsorption-induced atomic interactions cause a softening behavior that reduces structural rigidity. A comparative analysis between the Timoshenko and Euler–Bernoulli beam models highlights the crucial role of shear deformation in accurately capturing nanoscale dynamics. Overall, this research establishes a robust and adaptable analytical framework for modeling complex PFGP nanostructures. The findings offer valuable insights for designing and optimizing high-sensitivity MEMS/NEMS-based biosensors, resonators, and nanoscale actuators that operate under coupled thermo-mechanical conditions.

This work investigates the interplay between chemical reaction and radiative heat transfer in magnetohydrodynamic (MHD) stagnation-point nanofluid flow characterized by velocity and thermal slip on a stretched surface inside a porous medium, a subject that has not been previously explored. The impacts of the magnetic field, diffusion, radiation, Brownian motion, thermophoresis, and chemical reactions are considered in the nonlinear partial differential equations that regulate the momentum, energy, and concentration profiles. The similarity variables convert these equations into ordinary differential equations. The Keller Box Method (KBM) is used in MATLAB to numerically solve the resultant equations. This method is stable, converges quickly, and gives accurate results for tightly coupled nonlinear situations. The findings demonstrate that radiation, viscous dissipation, and the inertial coefficient substantially affect the flow structure. The Biot number makes the thermal boundary layer thicker, while heating the temperature profiles makes the Brownian motion parameter bigger. The KellerBox Method is a good way to explain the difficult physics of MHD nanofluid flow, which might help with heat control applications.

Spine finned surfaces are commonly utilized when an enhanced heat transmission is needed from a small surface area and also in applications where lightweight designs are desired. These spine fins are specifically used in air ducts, automobile engines, microchannel heat sinks, and electronic cooling to improve the effectiveness of thermal controls. Thus, this article focuses on analyzing the thermal behavior of conical spine exposed to convective–radiative and moist environment. The straight conical fin is analogously analyzed with the inclined one, with radiative and convective heat transfer coefficients treated as function of temperature. The occurrence of heat distribution is outlined by non-linear differential equation with relevant boundary conditions, which is then converted into nondimensional form using suitable dimensionless quantities. Further, the Fibonacci wavelet collocation method is applied to tackle the specified model. The implications of various factors on the energy field and efficiency have been visually demonstrated. The findings indicate that when the emissivity parameter is increased, the spine temperature drops by roughly 3%. Conversely, a rise in the Peclet number causes its temperature to increase by about 9%. Additionally, inclined spine demonstrates higher efficiency and better thermal dispersion compared to straight conical spine.

This research investigates the use of Stir-squeeze-cast can be effectively machined using high-speed wire electric Hybrid Aluminium Matrix Composites (HAMCs), specifically AA 024, using nanoparticles of ceramic ((Al_{2} O_{3}), (SiC), (Si_{3} N_{4}), (BN)). Because of the reinforcements’ natural hardness and abrasiveness, HAMCs are difficult to mill conventionally, despite their significant value in the industrial sector. By using a variety of machining variables, the study aims to create complicated surfaces with superior degradation properties and evaluate the erosion performance in terms of WWR (wire wear ratio) and MRR (material removal rate) for various profiles (curve, angular, and plane). A model for the WEDM process applied to HAMCs is presented, utilizing a Neural Network with Temporal Inductive Paths (TIPNN) optimized with the Starfish Optimization Algorithm (SOA). The process begins with the collection of a comprehensive dataset consisting of key machining variables such as drum speed ((D_{S} )), wire feed rate ((W_{FR} )), pulse voltage ((P_{V} )), pulse ((P)), and pulse current ((P_{I} )) angular, and plane machining profiles. After pre-processing the data by normalizing inputs and outputs, handling missing values, and removing outliers, the TIPNN to capture the dynamic interactions between the input data, and a model is built and machining outcomes. The model’s performance is enhanced using SOA, a nature-inspired optimization technique that fine-tunes the network’s weights and adjusts machining variables to achieve optimal MRR and WWR. The proposed TIPNN-SOA model is assessed and contrasted with current techniques like genetic-integrated neural networks (HAMC), DS-EDM optimization strategies, and hybrid Grey-ANFIS techniques, demonstrating its superior performance in improving machining outcomes.