International Journal of Mechanics and Materials in Design最新文献

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.

The percutaneous drivelines serve as a biomaterial interface between the exterior component (controller) and the blood pump, transmitting signals and power for wired ventricular assist devices (VADs). For long-term support, the mechanical design of drivelines plays a key role in preventing driveline infections and VAD system malfunctions. However, the mechanical design of VAD drivelines remains understudied. In this study, we introduce a framework that combines experimental data with mathematical modeling to analyze the mechanical response of VAD drivelines. We perform characterization tests on two distinct drivelines (HeartWare and HM3) and conducted further bending experiments to investigate the properties of the multi-layered HM3 design. Using these experimental data, we develop and validate a mathematical model of bending behavior that explicitly captures the stick–slip mechanics and frictional interactions at the interfaces between material layers. A sensitivity analysis was then conducted to quantify the significance of both material and interfacial properties on the overall bending response. Among the parameters, the thickness of the outer insulating layer is most sensitive to the bending stiffness, highlighting a primary target for design optimization. These experimental and mathematical findings show how mechanical and material properties of drivelines can be further modified to improve the overall performance of VAD applications for heart failure patients.

Non-local continuum theory is key to understanding material point interactions, emphasizing size-dependent effects in heat conduction to enhance microscopic-macroscopic interactions. This study develops a generalized thermoelasticity model integrating a two-temperature framework with nonlocal heat conduction and dual-phase-lag effects. A nonlocal thermal length-scale parameter captures size-dependent thermal interactions. The model investigates planar wave propagation in a homogeneous micropolar linear thermoelastic medium rotating at constant angular velocity, with a stationary coordinate system. Using specific boundary conditions and the normal mode method, we analyze variations in temperature, displacement, micro-rotation, coupling, and thermal stresses due to heating. Modified governing equations, solved via the normal mode approach, reveal how nonlocal thermal parameters, rotation, and two-temperature factors affect these physical quantities. The findings underscore the significant influence of polymer microstructure thermal properties on small-scale dynamics and memory-dependent behaviors, offering valuable parametric insights.

Classical thermoelastic-diffusion theories are inadequate at micro- and nanoscales because they assume instantaneous local response of heat and mass fluxes, predict infinite propagation speeds, and completely neglect long-range microstructural interactions. This paper introduces the first thermodynamically consistent theoretical framework that simultaneously overcomes all three limitations: it extends the Lord–Shulman generalized theory by incorporating nonlocal heat conduction of Guyer–Krumhansl type with its own thermal length scale, a newly proposed nonlocal mass-diffusion law governed by an independent diffusive length scale, and separate phase-lag relaxation times for thermal and chemical-potential gradients. The model is analytically solved for an infinite isotropic solid containing a traction-free spherical cavity subjected to a pulsed thermal shock and an exponentially decaying chemical potential at the inner surface. Numerical results for copper reveal three striking physical effects that are entirely absent in all previous local and single-nonlocality models: temperature and concentration disturbances penetrate far deeper into the material while their spatial gradients become remarkably smoother; peak displacements and thermoelastic stresses are reduced by more than half; and the coupled thermo-elasto-diffusive waves experience significantly stronger attenuation throughout the medium. These distinctive size-dependent phenomena originate from long-range interactions among energy carriers and diffusing species. The proposed framework therefore enables accurate performance prediction and deliberate microstructural tailoring in modern nanoscale devices, offering substantially improved reliability for MEMS thermal actuators, faster hydrogen charging in metallic microspheres, safer laser-triggered drug-release nanocapsules, and reduced thermoelastic losses in high-frequency nanoresonators.

Micro machining technology has rapidly developed with the widespread application of miniature components. Chip morphology and burr size are critical indicators for assessing the surface topography in micro machining. However, the evaluation criteria established for macroscopic cutting conditions are no longer applicable in this context. To investigate the effects of cutting parameters on chip morphology and burr size under micro-machining conditions, a typical dual-phase titanium alloy is selected as the workpiece for the micro milling study. First, a physical micro milling experimental platform is established. Next, based on a single-factor experimental approach, the influence of cutting parameters on chip morphology and burr size is analyzed. Finally, top burrs height prediction model of the down and up milling is developed using the SSA-LSSVM algorithm. The results indicate that the cutting depth has a minimal effect on chip morphology. However, as the feed per tooth increases, fine crack-like features become more prominent along the edges of the chips. The cutting depth primarily affects the top burr size in down milling, while the feed per tooth has the least. The prediction accuracy of the SSA-LSSVM models for both the down and up milling reaches 90%, with the maximum prediction errors being 14.2 and 15.1%, respectively. This prediction model can effectively guide the complex nonlinear mapping relationship between cutting parameters and burr size. The research results provide theoretical and experimental basis for the analysis of chip morphology and burr size prediction in micro-milling of dual-phase alloys.

The engine power is transmitted to the tail rotor via the tail drive shaft system without redundancy. Characterized by complex structure, long shafts, and intricate vibration behaviors, excessive vibration-induced failure may lead to uncontrolled drivetrain dynamics, severely compromising flight safety. This study incorporates internal excitations of spiral bevel gears in intermediate and tail reducers. A 162-degree-of-freedom (DOF) dynamic model is established using hybrid finite element and lumped-mass modeling techniques, discretizing the system into shaft segment unit, gear unit, and bearing-casing unit. The nonlinear dynamics are solved through combined Newmark-β and conjugate gradient methods, yielding nodal shaft trajectories and vibration accelerations. Experimental validation on reducer test benches demonstrates a maximum 14.01% deviation between simulations and measurements, confirming model validity.