International Journal of Mechanics and Materials in Design最新文献

Topology optimization plays a critical role in structural design. However, stress-related problems typically involve computationally intensive sensitivity and finite element analysis, making traditional iterative methods costly and inefficient. In this study, an efficient stress-minimizing topology optimization method is proposed using a conditional generative adversarial network (cGAN) based on the residual U-shaped convolutional neural network (ResUNet) model. The von Mises stress field computed from the first iteration of the Solid Isotropic Material with Penalization (SIMP) method is incorporated into the generator as a physical prior to improve the accuracy and mechanical consistency of the generated topologies. A dataset is constructed using the SIMP method under random boundary conditions, volume fractions, and external loads, with the optimization problem solved using the Method of Moving Asymptotes (MMA). Global stress is evaluated using the p-norm function. The generative performance of convolutional neural network (CNN)-cGAN, U-shaped (U-Net)-cGAN, ResUNet-generative adversarial network (GAN), and ResUNet-cGAN models is systematically compared. The proposed method is validated on cantilever and MBB beam cases. Results show that the topologies generated by ResUNet-cGAN closely resemble those produced by the SIMP method, while significantly reducing computation time. This study demonstrates the feasibility of deep learning for efficient stress-related topology optimization.

Based on the principle of non-equilibrium thermodynamics and the theory of nonlinear dissipative dielectrics, this study develops a physical model to describe the viscoelastic electromechanical behavior of a circular dielectric elastomer membrane-spring actuator. Through theoretical analysis and numerical simulations, this study investigates the influence of spring parameters on the viscoelastic electromechanical behavior of the actuator under both constant and periodic loading conditions. It further proposes a regulation method to achieve the desired electromechanical response under different forces by appropriately tuning the spring parameters. The research results indicate that under constant loading conditions, the electromechanical response of the membrane can be either enhanced or suppressed by adjusting the spring’s initial length and stiffness. Specifically, the initial length primarily determines whether the response is enhanced or suppressed, while the stiffness predominantly influences the response amplitude. Furthermore, a functional relationship among the force, spring parameters, and the steady-state downward displacement of the disk has been established. This relationship allows the system to achieve the same steady-state deformation under varying forces by appropriately tuning the spring parameters, thereby enabling response optimization under non-ideal loading conditions. Under periodic excitation by force and voltage, the actuator exhibits stable oscillatory behavior, with the spring parameters continuing to play a crucial regulatory role. Specifically, these parameters significantly affect both the amplitude of the dynamic response and the time required for the system to reach steady-state oscillations. This study aims to provide theoretical guidance for the structural design and performance optimization of circular dielectric elastomer membrane-spring actuators in applications such as soft robotics, artificial heart pumps, and soft fluidic pumps.

To address the challenge of lightweight and high-performance structural design, a novel bionic sandwich plate (VP) inspired by the vein structure of Victoria cruziana leaves is proposed. The mechanical behavior of VPs under impact and blast loading is systematically investigated using finite element simulations. The study explores the influence of key structural parameters, including core distribution, wall thickness, core height, and skin thickness, on energy absorption, peak force, deformation, and failure modes. Results demonstrate that optimizing the core distribution and increasing the skin thickness can significantly enhance impact resistance, while increasing wall thickness or height provides limited benefits in terms of structural efficiency. In blast scenarios, optimizing the core distribution represents the most cost-effective and efficient strategy for enhancing the structural performance of sandwich plates. A random forest model is further employed to quantify the importance of each parameter, allowing for efficient identification of critical design variables based on different loading conditions. This research significantly enhances the understanding of the structural behavior of bionic sandwich plates and offers valuable insights for their practical application in fields such as aerospace, defense, and energy absorption, where weight minimization and resistance to impact and blast loads are of paramount importance.

Frequency optimization of stiffened piezolaminated composite plates has not been investigated so far. Therefore, this study focuses on optimizing the fundamental frequency of stiffened piezolaminated composite plates using a novel special relativity search based on hill climbing (SRSHC) algorithm. The optimization process maximizes the fundamental frequency by adjusting the fibre orientations within the composite layers. The mathematical models, based on classical laminated plate theory (CLPT) with von Karman nonlinearity, are solved using MATLAB. The impact of various parameters, including boundary conditions, grid shapes, angles of diagonal ribs, and plate aspect ratios, on the optimized frequency results is thoroughly examined. The results demonstrate that the SRSHC algorithm outperforms the Special Relativity Search (SRS) algorithm, confirming its effectiveness in complex optimization problems with large search spaces. This research contributes to advancing the design of smart structures with enhanced dynamic performance.

This work presents a methodology for automatic detection of internal defects in parts manufactured from 3D printing with two of the most common materials for these purposes. Active Thermography has been used, specifically the Lock-in technique. To ensure reliable detection, the varying frequencies of the stimulation source have been compared to determine the optimal configuration. Results have been analysed to identify the parameters that most affect the identification of defects. Results show that the frequency and the type of material used are the most critical parameters that condition the detection though the influence of this latter was less clear, so it was necessary apply a novel analysis based on Machine Learning. Most effective algorithm was an ensemble model, which achieved an accuracy rate of 88.9%. A variation of Maximum Stable External Regions is presented to automatised detection.

In this present, the finite element approach is employed to analyze the free oscillation, static and dynamic buckling of skew-nanoplate made of piezoelectric materials with variable thickness resting on variable Pasternak medium in a hygro-temperature environment. This study is a wonderful combination of Kirchhoff plate theory, nonlocal strain gradient hypothesis, and surface effect and Hamilton’s principle to derive the general equilibrium equation of the plate. A four-node quadrilateral plate element with six degrees of freedom per node is developed using a Hermit C²-level non-conforming shape function. This element offers high accuracy and fast convergence for a variety of shapes and boundary conditions, outperforming lower-order elements. Bolotin’s method is applied to determine the dynamic instability region of the non-uniform piezoelectric skew nanoplate. The accuracy of the present approach is validated through numerical comparisons with established data. Furthermore, the effects of parameters such as residual surface stress, applied voltage, temperature gradient, moisture, elastic foundation stiffness, thickness variation, skew angle, geometric factors, and boundary conditions on free oscillation and stability of skew nanoplate are thoroughly assessed. The present study will offer the physical insights required to model size-dependent multifunctional systems for active control of mechanical characteristics and electromechanical energy harvesting, given the recent developments in nanoscale manufacturing.

This study develops a comprehensive mathematical model for vibration-based energy harvesting in laminated bimorph cylindrical microshells with hexagonal honeycomb cores, subjected to nonlinear thermal gradients and moisture environments, and supported by a viscoelastic foundation. It is assumed that the sandwich microshell is subjected to various boundary conditions. The external layers use piezoelectric materials to improve energy conversion. First-order shear deformation theory (FSDT) and modified strain gradient theory (MSGT) are utilized to formulate size-dependent dynamic equations that incorporate microscale effects. The modified Gibson’s equation is used to estimate the material characteristics of the honeycomb core, which is considered a homogeneous orthotropic medium. Employing Hamilton’s principle and Gauss’s law, coupled electromechanical equations are derived to characterize the system’s dynamic behavior. Frequency response functions are found using analytical methods that correlate electrical power production with resistance to external loads. An extensive parametric study examines how energy harvesting efficiency is affected by geometric dimensions, length scale parameters, fluctuation of the temperature, variation of moisture, viscoelastic medium, parallel and series piezoelectric setups, boundary conditions, and honeycomb characteristics. The results provide important information for improving the design of nanoscale energy harvesters and their performance in viscoelastic and thermally dynamic environments.

This study utilizes the nonlocal strain gradient elasticity theory to investigate the dimensionless frequency shift caused by adsorption in a dynamic resonator system. The system consists of double functionally graded porous sandwich microbeams with a two-dimensional periodic square holes network, connected through an elastic medium and influenced by a magnetic field, compressive loading, and distributed water molecules. The double functional microbeams follow a power-law distribution for thickness-dependent properties, considering two porosity patterns. The impact of the applied magnetic field is analyzed using Maxwell’s equations. Nonlocal strain gradient elasticity theory is employed to capture small-scale effects. Both Euler–Bernoulli and Rayleigh beam theories are utilized to account for bending and rotary inertia effects. Interatomic interaction energies are modeled using Lennard–Jones (6–12), Morse, and Buckingham potentials, while perforation effects are incorporated into the equivalent bending stiffness. Analytical solutions are derived using the Navier-type method, and numerical solutions via the differential quadrature method. Results indicate that the dimensionless frequency shift is strongly influenced by porosity volume fraction and hole configuration. Water molecule adsorption reduces the frequency shift, while increases in magnetic field intensity and length-to-width ratio improve structural sensitivity. Additionally, both dimensionless compressive force and spring parameter introduce a softening effect, lowering system stiffness and amplifying the drop in dimensionless nonlocal frequency. A clear discrepancy between Euler–Bernoulli and Rayleigh model predictions highlights the role of rotary inertia. This model offers a comprehensive framework for designing advanced microresonators for nano/microscale sensing applications, such as mass detection and virus-induced frequency modulation.

This study presents exact analytical solutions for the local and nonlocal transverse vibration of Euler–Bernoulli nanobeams resting on Winkler–Pasternak elastic foundations. Incorporating Eringen’s nonlocal elasticity theory, the model captures small-scale effects, while rotational and translational springs represent general boundary conditions. For the first time, exact and general frequency equations are derived for nanobeams with arbitrary boundary conditions, elastic foundations, and tip masses. These equations are numerically solved to obtain precise natural frequencies, showcasing the accuracy and versatility of the proposed framework. The influence of key parameters such as nonlocal effects, boundary flexibility, foundation stiffness, tip masses, and slenderness ratio on the first three natural frequencies and mode shapes is systematically analyzed. Results reveal the significant role these factors play in shaping vibrational behavior, providing critical insights into the dynamic response of nanostructures. Presented in graphical and tabular formats, the findings offer benchmark solutions for validating future models. This work advances the understanding of nanobeam dynamics and supports the design and optimization of advanced nanoscale systems embedded in elastic media with flexible supports and tip masses.

The growing demand for energy has led to significant attention being given to the energy harvesting process from vibrations using piezoelectric materials. Given the limited energy available for conversion, robust designs that minimize sensitivity to parameter uncertainties or external variations are essential. To ensure project quality, multi-objective optimizations are necessary to maximize the mean and minimize the standard deviation of the response, but the computational cost increases with the number of uncertain parameters, requiring more efficient approaches. In this way, with metamodels, which are computational tools, it is possible to provide a faster and less costly evaluation of such computationally expensive models. This study proposes the use of a Kriging metamodel to design robust cantilever beam energy harvesting devices, combined with Monte Carlo Simulation to estimate the mean and standard deviation of the Frequency Response Function of power output. Multi-objective optimization and sensitivity analysis are applied. Results indicate that using more design variables leads to a metamodel with higher computational cost due to the larger number of experimental samples required. Nevertheless, this cost remains low compared to direct model optimization, with a satisfactory time reduction in the optimization process.