International Journal of Mechanics and Materials in Design最新文献

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.

Study on the multi-physical field coupling characteristics of nanoplate structures is an important theoretical basis for designing nanoelectromechanical systems with high sensitivity, small size and wide application range. This paper presents a novel model of circular nano-laminated plates containing piezoelectric layers and piezomagnetic layers with flexoelectric effect and flexomagnetic effect. By using the plate theory considering surface effect, the control equations for the bending and free vibration of circular nano-laminated plates under transverse mechanical and electromagnetic loads were established, and the equivalent axial force and equivalent bending moment of the circular nano-laminated plates were given. The analytical solutions of the bending deflection and natural frequency under different displacement boundary conditions were obtained. The influences of surface effect, flexoelectric effect, flexomagnetic effect and geometric parameters on bending deflection and natural frequency were discussed. It was found that the flexoelectric effect, flexomagnetic effect and surface effect have completely different influences on the bending of circular nano-laminated plates. The model, analytical solution and conclusion presented in this paper have significant theoretical significance and serve as a benchmark for the structural modeling, functional design and multi-field coupling performance analysis of such nanoscale laminated plate structures.

This paper presents an isogeometric analysis (IGA) approach based on the modified first-order shear deformation theory (m-FSDT) for static bending, free vibration and transient response of a bio-inspired helicoid laminated composite (B-iHLC) shallow shell integrated with piezoelectric surface layers (hereafter referred to as B-iHLC-Piezo shell), resting on a Pasternak foundation and accounting for initial geometrical imperfections. The shell’s core layer is constructed using helicoidal schemes inspired by biological composite structures, which enable high-impact energy absorption with remarkable efficiency and exceptional damage resistance. The surface layers consist of isotropic piezoelectric smart materials capable of actively controlling structural vibrations. The mechanical displacement field is approximated via the m-FSDT framework using Non-Uniform Rational B-Spline basis functions. Smart B-iHLC shell structures' static and dynamic responses are actively controlled using a closed-loop control process that considers the structural damping effect and is based on displacement and velocity feedback gains. The reliability and effectiveness of the proposed method are validated through numerical comparisons with existing literature. The findings from this study serve as valuable references for the design and vibration control of advanced structures in military, aerospace, marine, and related engineering fields.

Cylindrical roller bearings (CRBs) are essential components of mechanical equipment, and their dynamic and thermal performance has a significant impact on the overall operation of the machine. Therefore, an accurate analysis of the operating performance of CRBs is crucial. This study aims to develop an improved dynamic model for CRBs by integrating key factors such as oil film stiffness, damping, and the thermal-mechanical coupling mechanisms. Specifically, the comprehensive dynamic and transient thermal network models are first constructed, taking into account component interactions and oil film characteristics. A detailed thermal-mechanical coupling analysis is then performed to accurately capture the transient dynamic and thermal behavior of CRBs. Furthermore, a specialized test rig is developed to verify the accuracy of the thermal-mechanical coupling model established in this study. The test results validate the theoretical findings, including slip rate, cage centroid trajectory, and temperature rise, confirming the accuracy and reliability of the proposed analytical framework: The dynamic model of cylindrical roller bearings (CRBs), incorporating the effects of oil film stiffness and damping, enables a more accurate and realistic analysis of their motion behavior. This model proposed in this paper demonstrates closer alignment with experimental results by 13.3% in calculating parameters such as temperature rise and slip ratio, compared to conventional dynamic models. In addition, the thermal-mechanical coupling analysis shows that the temperature rise of the bearing, after taking into account the thermal deformation coupling and the lubricant viscosity-temperature effect, is lower than that predicted by the steady-state model.

This study presents a comprehensive experimental and AI-based investigation into the three-point bending behavior of aramid and glass fiber-reinforced hybrid composites, considering different EKabor-II nanoparticle reinforcement levels (0 wt%, 0.5 wt%, and 1 wt%). Artificial Neural Networks (ANN), Classification Learner, and Regression Learner algorithms are comparatively applied for the first time to predict the mechanical responses of composite panels with millimeter-level precision. The ANN model, trained using Levenberg–Marquardt with 5 neurons and a learning rate of 0.013, achieved a validation MSE of 0.678 MPa and R² of 0.9997. Meanwhile, the Gaussian Process Regression (GPR) method produced outstanding results (RMSE = 0.089 MPa, R² = 0.9999). Hyperparameter optimization using the CMA-ES algorithm eliminated the need for manual trial-and-error, objectively identifying the optimal ANN configuration and enhancing global search capability and generalization reliability. Five-fold cross-validation and 95% confidence intervals (RMSE = 0.75 ± 0.83 MPa; R² = 0.9869 ± 0.0039) demonstrate consistent performance beyond randomness. SHAP-based explainability analysis revealed that compressive load (55% contribution) and test duration (20%) dominantly influence flexural stress, enabling causal interpretation of the model. Edge-case analysis under extreme configurations (Wt = 0, Wt = 1, and maximum compressive load) confirmed prediction deviations within ± 5 MPa, ensuring safety margins. This holistic approach significantly contributes to accelerating computational materials design and establishing reliable infrastructures for Industry 4.0, digital twin, and sustainable (eco-composite) applications in materials science and engineering.

Graphene, a two-dimensional material known for its exceptional stiffness and low weight, exhibits resonant frequencies that facilitate the detection of mass changes on the order of zeptograms. However, extensive research has been conducted on carbon-based nanomaterials, particularly graphene sheets, the majority of studies have predominantly focused on single mass detection. As is well-known, real systems often involve the identification of materials containing a large number of particles per sample. This paper investigates the application of graphene in the development of mechanical nano-sensors capable of detecting minuscule entities, such as viruses and gas molecules, through measurable alterations in their resonant frequency. This study analytically develops a non-local continuum model to explore the effects of multiple masses attached to a graphene sheet on its frequency response. Additionally, the finite element method is employed to analyze this system, allowing for a comparative assessment of the results obtained from both analytical modeling and finite element analysis. The research focuses on graphene sheets with randomly distributed masses on their surfaces, examining how variations in aspect ratio, length, and mass quantity influence frequency changes. The findings contribute to the understanding of graphene-based nanosensors and their potential applications in biosensing and diagnostics, particularly in the context of rapid detection methods for viral pathogens such as SARS-CoV-2.