International Journal of Mechanics and Materials in Design最新文献

Zirconium (Zr)-based alloys have great potential for orthopedic implants due to their excellent mechanical properties, corrosion resistance, and biocompatibility. However, untreated Zr-based alloys exhibit inadequate wear resistance, which limits their service life as joint prostheses. This study employed a combined surface texturing and thermal oxidation approach to enhance wear resistance. Biomimetic micro-textures were fabricated on the alloy surface via laser processing, followed by high-temperature oxidation to produce a textured ceramic coating. The influence of micro-texture diameter on anti-friction performance was systematically investigated. Surface modification treatment has significantly enhanced the hardness and roughness of the samples by several times and greatly improved the wetting performance. The contact angle decreased by approximately 56% (± 1.8%) after texturing treatment and further reduced by 16% (± 5.7%) after high-temperature oxidation. Friction test revealed that ceramic-textured specimens outperformed smooth surfaces in terms of friction reduction and wear resistance. Specifically, the friction coefficient was reduced by 25.29% (± 2.4%), with a maximum wear reduction rate of 27.7%. This study provides a novel strategy for improving the surface properties of Zr-based alloys.

In bone tissue engineering, achieving a balance between mechanical properties and mass transport capabilities is essential for designing porous scaffolds. This study proposes a dual-objective optimization design method based on triply periodic minimal surface (TPMS) structures, aiming to simultaneously satisfy the requirements for elastic modulus and permeability. Three types of TPMS structures—Diamond (D), Gyroid (G), and IWP—were constructed in both sheet and rod forms. The effects of structural parameters, including porosity and unit cell size, on the elastic modulus and permeability of the scaffolds were systematically investigated. Finite element analysis and computational fluid dynamics simulations were conducted to establish empirical formulas relating structural parameters to mechanical and transport performance, which were subsequently validated experimentally with high predictive accuracy. On this basis, orthogonal experiments and entropy weight analysis were employed to quantitatively evaluate the influence of structural parameters on the two performance indicators, and a comprehensive performance optimization strategy was proposed. The results show that porosity is the most significant factor affecting elastic modulus, while unit cell size is the dominant factor influencing permeability. Among the structures, the IWP type demonstrates superior performance in both mechanical and transport characteristics. This study provides a theoretical foundation and quantitative tools for the personalized design of TPMS bone scaffolds, offering promising potential for clinical applications.

This paper presents a novel investigation into the transient dynamic behavior of curved zigzag nanobeams using the Finite Element Doublet Mechanics (FEDM) theory. A sinusoidal shear deformation theory is employed to capture shear effects, while size-dependent behavior is introduced through a length scale parameter within the Doublet Mechanics framework. The model accounts for a range of boundary conditions, open angles, aspect ratios, and time-dependent loading profiles, including step, sinusoidal, blast, and triangular functions. Validation is performed through comparisons with molecular dynamics simulations and classical continuum beam theories. Results reveal that curvature, boundary conditions, and scale effects significantly influence the transient response. Overall, the study demonstrates the effectiveness of the FEDM theory in accurately predicting nanoscale beam dynamics and offers a robust tool for micro- and nanoscale structural analysis.

Active health monitoring and fault diagnosis methods are essential to improve the safety and reliability of rotating machineries and to prevent from catastrophic failure. The conventional fault diagnosis methods require battery-support sensors. This paper presents a novel piezoelectric smart bearing to fulfill fault detection without using battery-support sensors. An electromechanical coupling model of the unbalanced flexible rotor with piezoelectric smart bearings is established using the prominent principle of piezoelectric transducers and Lagrange equation. This model also takes in to account the nonlinearity due to the breathing transverse crack. Numerical exploration for the voltage response when the crack grows deeper is performed using the frequency response, orbit diagram, power spectrum and bifurcation diagram. Then, a test rig has been designed and built for experimental validation. The obtained results show that the voltage responses of the system contain the fault characteristic frequencies. So, the proposed smart bearing is capable of detecting the unbalance and crack faults and can be used for self-powered condition monitoring of rotating machines.

Additive manufacturing, particularly fused filament fabrication (FFF), has become an important technique for producing lightweight and geometrically complex polymer structures, yet the reliable integration of these parts into structural assemblies requires effective joining strategies. Adhesive bonding offers significant advantages over mechanical fastening by enabling uniform stress transfer and compatibility with dissimilar materials, but the mechanical integrity of bonded 3D-printed joints is strongly influenced by both adherend material and joint geometry. This study explores the effects of adherend material (PLA and PETG) and joint geometry, single lap joint (SLJ), notched SLJ (NSLJ), and curved joint (CJ) on failure loads and fracture behavior. Specimens were manufactured via fused filament fabrication (FFF) and bonded using a methacrylate-based structural adhesive. A cohesive zone model (CZM) was developed to simulate load–displacement responses and predict failure initiation and progression. The novelty of this work lies in the combined experimental and numerical investigation of how joint geometry and material selection affect the mechanical integrity of bonded 3D-printed components. Among the tested configurations, curved joints showed the highest failure loads, while notched joints performed the weakest. CZM simulations accurately predicted experimental behavior, with deviations ranging from 1.03% to 9.77%. Failure modes varied with both material and geometry, including adhesive cohesive failure and adherend fracture. These findings offer a framework for enhancing the reliability of polymer bonded joints in additive manufacturing, supporting failure prevention through informed design.

High pressure common rail pipe is mainly used for storing high pressure fuel, maintaining stable pressure in the common rail pipe, and distributing the fuel to each injector. Therefore, the stability characteristics of common rail pressure determine the stability of fuel injection and engine. This paper first analyzes the composition and working principles of the high pressure common rail injection system for diesel engines, and establishes mathematical models for its key components. Secondly, based on numerical simulation and using AMESim, the simulation model of the diesel engine high-pressure common rail system was built, and the accuracy of the established simulation model was verified based on the high-pressure common rail system test bench built. Then, a detailed study was conducted on the rail pressure fluctuation characteristics of the common rail pipe, mainly analyzing the effects of injection target rail pressure, injection pulse width, common rail pipe length, common rail pipe inner diameter, common rail pipe wall thickness, and common rail pipe material on the rail pressure characteristics. Research has shown that the maximum peak value of rail pressure fluctuation follows a linear function relationship with the length and inner diameter of the common rail pipe, while the average rail pressure fluctuation follows a polynomial function relationship with the length and inner diameter of the common rail tube. Within the range of the studied parameters, the target rail pressure has the greatest impact on the peak rail pressure fluctuation, and the common rail pipe diameter has the most significant suppression on the average rail pressure fluctuation. Finally, based on the AMESim rail pressure fluctuation curve mentioned above, Fluent were used to preliminarily study the internal flow field characteristics of the common rail pipe at different outlets during the stable pressure stage with a target rail pressure of 150 MPa. Through the research in this paper, certain references can be provided for the design and optimization of common rail tubes in high-pressure common rail injection system.

Perforated fins have been regarded as one of the most effective ways in fin optimization and are used in film cooling turbine blades. The convective heat transfer from a rectangular fin with circular perforations by considering the effect of internal heat generation, is studied in this analysis. The solution to the modelled heat transfer-perforation fin problem is explored using the Chelyshkov polynomial collocation method. The operational-matrix form of the heat transfer-perforation fin problem is established using this collocation method. The governing heat equation is developed and is transformed into a dimensionless ordinary differential equation (ODE) using appropriate dimensionless variables. This equation is solved via matrix based Chelyshkov polynomial collocation approximations (MBCPCA). The comparison with alternative approaches to solving the perforated fin equation is executed, and an error analysis is also presented. The effect of parameters, including the number of perforations and their geometrical dimensions, is considered in this analysis. The variation in thermal profiles for various parameters is illustrated through tabulated and graphical statistics. The outcomes demonstrated that the perforated fin with circular holes exhibited a lower fin temperature than the non-perforated one. The rate of heat transmission is increased by 39.83% when the Biot number is increased from 1 to 2.

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.