Microscale contact and friction behavior are widely present in various important industrial devices and systems. As electromechanical systems become more integrated and miniaturized, the impact of friction on devices becomes increasingly important. At the microscale, friction behavior exhibits a strong dependence on interfacial viscosity and contact size. By developing a series of modifiable potential functions to quantitatively regulate interfacial properties, friction on atomically smooth interfaces with different properties is fully simulated using molecular dynamics methods. The study first examined the influence of various interfacial potential energies on the static friction coefficient, revealing a nonlinear relationship between the static friction coefficient and interfacial potential energy intensity. Furthermore, it was found that this nonlinearity is attributed to the competition between interfacial viscosity and contact stiffness. Additionally, the study investigated the influence of contact size on static friction coefficient. The simulation results showed that as the tangential contact length of the interface increases, the peak static friction force first increases and then stabilizes. By analyzing the contact layer cloud maps obtained through post-processing, interfacial friction is observed as a “nucleation-propagation” process, influenced by different contact sizes which affect the dynamic process and lead to changes in the peak static friction force. This study provides new insights into the effects of interfacial potential energy and contact size on microscale friction through molecular dynamics simulations, it is feasible to regulate friction by changing interfacial potential energy, but attention should be paid to the nonlinear changes in the friction coefficient. Besides, solely increasing the contact size cannot infinitely increase the peak static friction force.

The impact of environmental corrosion on rail service operations poses a direct safety threat. Therefore, the quantitative characterization of rail corrosion damage is of great significance for evaluating rail reliability. Uniform corrosion experiments on U71Mn hot-rolled rail samples in a 3.5 wt. % NaCl solution at room temperature were first carried out. Changes in the diameters of two samples with corrosion time were measured. According to the experimental results, the corrosion mechanism of rail samples in the 3.5 wt. % Nacl solution was analyzed. A corrosion model employing cellular automata was developed to simulate the uniform corrosion behavior of rail samples in the NaCl solution. The corrosion rate was quantified by tracking sample diameter changes through the cellular automaton simulation over varying corrosion time. A unified prediction formula for different sample diameters with corrosion time was established. Results revealed an average relative error of 8.7% between predicted and measured data, indicating the efficacy of cellular automata in accurately simulating the uniform corrosion process of U71Mn hot-rolled rail samples.

Lithium metal is a highly promising anode material due to its high theoretical capacity and low reduction/oxidation potential, and has received extensive attention. However, the formation and growth of lithium dendrites poses the biggest challenge to its commercialization. The use of solid-state electrolyte, instead of liquid electrolyte, has become a potential path to inhibit the growth of lithium dendrites. However, issues such as poor metal-lithium interface contact and low ionic conductivity in solid-state electrolytes persist. Composite solid-state electrolytes, prepared by combining polymers with inorganic ceramic electrolytes, have shown effectiveness in inhibiting the growth of lithium dendrites. Although these composite solid electrolytes typically have high ionic conductivity, their elastic moduli are low. Currently, the mechanism of dendrite suppression by low-modulus composite solid-state electrolytes, especially low-modulus multiphase composite solid-state electrolytes, remains incompletely clarified. Therefore, this paper considers the mechanical effects of solid electrolytes and builds a mechanical-chemical model using the phase field method. By taking poly (ethylene oxide) (PEO)-based composite-state electrolyte as an example, the study investigates the influence of composite solid electrolyte modulus on dendrite growth. The results show that the higher the electrolyte modulus, the greater the stress on the lithium metal, leading to a more uniform distribution of lithium ions on the interface between the electrolyte and the lithium anode electrode. The higher stress also tends to cause the plastic deformation of lithium dendrites, thus inhibiting their growth. This research deepens the understanding of the mechanism of inhibition of lithium dendrites by low-modulus multiphase composite solid electrolytes, and provides guidance for the design of composite solid electrolytes.

Compared to conventional mechanical testing methods, the indentation method offers the advantages of simple manufacturing of samples and in-situ testing. This study proposes an alternative to deriving material mechanical parameters solely from indentation load-depth curves. It introduces an effective method for deducing metal plastic mechanical parameters based on residual indentation morphology and neural network learning. An Instron universal material testing machine was used to conduct spherical indentation tests on Cu, Mg, and Fe, followed by scanning their residual indentation morphology through the contour morphology system. The extracted morphology features served as the basis for further analysis. Data processing techniques such as amplification, rounding, binarization, and high-order digit supplementation were applied to the acquired data. Through Abaqus software and numerical simulations, residual indentation depth data associated with various material parameters were automatically extracted for neural network learning. Selections of activation function, neural network parameter initialization and updating mode, loss function, parameter optimization strategy, and neural network structure were carefully conducted to ensure effective learning. The plastic mechanical parameters of Cu, Mg, and Fe were obtained based on the residual indentation morphology feature data from indentation tests and the neural networks after learning. Additionally, the related plastic mechanical parameters of Cu, Mg, and Fe were also acquired through conventional uniaxial tensile tests and characterization using the Instron machine. By comparing the neural network learning results with tensile test data, relative errors in plastic mechanical parameters were identified. The effectiveness of the proposed method in obtaining metal plastic mechanical parameters based on neural network learning and residual indentation morphology was validated. This method can be expanded for characterizing mechanical properties and acquiring plastic parameters of other metal/alloy materials.

To gain a deeper understanding of the constraint effect from double crack tips and accurately characterize it, this study focuses on non-collinear parallel double cracks in a homogeneous plate. It examines the stress and strain fields associated with these double cracks, employing the ABAQUS finite element analysis software. Particular attention is paid to their behaviors at various horizontal distances (s) and vertical distances (h). Additionally, by leveraging the unified constraint parameter A_p, the constraints of double crack tips are compared with those of coalesced single crack tips. The findings reveal significant differences in the distributions and magnitudes of stress and strain at double crack tips compared to coalesced single crack counterparts. The conventional method of calculating the constraints from double cracks based on the stress or strain field at a crack tip, as done for single cracks, would lead to inaccurate results. Comparison of A_total, A_inside, and A_outside with A_single shows that considering both inside and outside crack tip strain fields aligns the variation trend of A_total more closely with A_single, with a remarkably narrow fluctuation. The constraint magnitude for coalesced single cracks ranges from 0.10 to 0.30 of the total strain field (A_total). This approach demonstrates a degree of universality, unaffected by whether the double cracks coalesce or not. It can be directly applied to quantify the constraints imposed by double cracks, regardless of their coalescing status. This study offers valuable insights into the constraint effect of double crack tips and presents a novel method for characterizing the constraints associated with double cracks. In summary, this novel approach offers a more comprehensive and accurate understanding of the complex constraints from double cracks, providing scientific support for evaluating structural integrity with double and multiple cracks.

Stress intensity factor is a crucial parameter for modeling and predicting structural fracture failure. This study evaluates the dynamic stress intensity factor for solving three-dimensional dynamic fracture problems using the adaptive phantom node method. This technique combines the phantom node method with adaptive mesh refinement, automating the generation of a dense mesh around the crack. In this approach, strong discontinuities at cracks are modeled using phantom nodes without crack tip enrichment functions or extra degrees of freedom. The theoretical framework of this technique is straightforward and easy to implement based on the finite element method, but it requires a relatively dense mesh to ensure computational accuracy. Adaptive mesh refinement technology and criteria suitable for crack problems are introduced into the phantom node method, thus obviating the need for a globally dense mesh with high computational consumption while improving computational accuracy and efficiency. A concise approach, known as constrained approximation, is adopted to deal with hanging nodes presented in the locally refined mesh. It is convenient to implement numerically, does not involve special elements or complex shape functions, and retains the interpolation and numerical integration of the standard finite element method. The stress intensity factors for several three-dimensional crack problems are evaluated using the adaptive phantom node method and compared with the theoretical solutions and numerical results obtained by the standard phantom node method. It is found that the numerical results of this method are in good agreement with the theoretical solutions, and the computational accuracy is effectively improved compared to the standard phantom node method. Additionally, compared to the locally pre-refined mesh with equivalent accuracy, the adaptive refined mesh exhibits higher computational efficiency and reduced computational consumption. This holds considerable potential value for the efficient simulation and prediction of dynamic fracture failure in large-scale complex engineering structures.

Additive manufacturing (AM) techniques have attracted widespread attention in aerospace and biomedical fields due to advantages like high material utilization and extensive design flexibility. However, process-induced defects in AM-built components pose significant challenges for evaluating fatigue performance. The AM-built components are subjected to complex alternating loads in service, making it imperative to develop accurate fatigue life prediction models. Currently, two main approaches are widely employed: theoretical analysis and data-driven methods. Traditional life prediction models like continuum damage mechanics (CDM) suffer from limitations such as low accuracy and restricted applicability. Conversely, data-driven models, such as artificial neural networks (ANN), encounter constraints when dealing with limited sample sizes. To address these issues, knowledge-data hybrid models have emerged as a promising approach that combines physical principles with data insights. In view of this, this study has developed a calibrated CDM model and seamlessly integrated it with an ANN-based data-driven model. Employing methods of feature, parameter, and output fusion, three types of hybrid models based on CDM and ANN have been developed. To quantitatively analyze the prediction accuracy and data requirements of these models, calculations using fatigue data obtained from laser powder bed fusion (LPBF)-processed AlSi10Mg alloy have been performed. The results highlight the crucial role played by the corrective function of training data in the parameter fusion-based model, while indicating a relatively minor influence from the CDM model in terms of prediction accuracy. Moreover, this model retains a commendable level of accuracy even with suboptimal fitting outcomes from the CDM model. The hybrid model, which leverages feature fusion, maximizes the utilization of physical information from the CDM model, thus achieving the highest prediction accuracy and stability when ample data are available. The model based on output fusion, primarily guided by results of the CDM model and enhanced by ANN adjustments, demonstrates relatively superior predictive capabilities in domains outside of the training set compared to other models. These findings provide significant reference value for the further development of high-accuracy, knowledge-data hybrid fatigue life prediction models in AM.

Using fused deposition modeling (FDM) 3D printing technology, a lattice structure was created. After adhering composite conductive materials to the surface of its structural elements, the 3D lattice structure with sensing capability (LSS) was fabricated. Based on three-unit configurations, a study was conducted to investigate the mechanical properties and piezoresistive characteristics of different lattice structures in LSS. Utilizing the conductive percolation phenomenon in conductive composites, this study explored the patterns of piezoresistive behavior in LSS with varying structures and composites under both small and large strain conditions. Key factors such as stress caused by structural deformation and self-contact between lattice surfaces were identified, leading to the observed three-stage trend in the change of electrical resistance response. By analyzing the experimental data from compression tests, the optimal lattice structure and composite mass fraction for LSS were determined, providing a reliable basis for deformation monitoring in perceptual structures. The approach of creating a 3D structure and then incorporating conductive composites offers benefits such as structural controllability and good mechanical performance. The sensing structure can detect compressive stress in objects and serve as a high-quality buffering or damping material that effectively absorbs vibration and energy. This research demonstrates promising applications in various fields.

Ultra-high cycle fatigue experiments can be conducted using traditional testing methods such as electromagnetic vibration (30-3000 Hz) and ultrasonic vibration (20 kHz). Differences in fatigue life for the same material may arise when tested under varying loading frequencies. To fully utilize the ultra-high cycle fatigue life data obtained from different testing systems, the impact of loading frequency on the ultra-high cycle fatigue life of materials needs to be studied imperatively. This paper presents novel prediction models for ultra-high cycle fatigue life, taking into account loading frequency. The models incorporate the crack initiation life prediction model based on Tanaka's dislocation theory and the Paris crack growth life prediction model. The influence of loading frequency is integrated into effective stress and fatigue strength. The proposed models are verified using available very high cycle fatigue test data for titanium alloy TC17 and nickel-based superalloy GH4169 under different loading frequencies. The results show that the models proposed in this work can reasonably characterize the ultra-high cycle fatigue test data of materials under varying loading frequencies, establishing the correlation of fatigue life data under different loading frequencies.

For some polymers below or near their glass transition temperature, a particular type of non-Fickian solvent diffusion, known as Case Ⅱ diffusion, is typically observed. To describe the coupling effect of Case Ⅱ diffusion and swelling deformation in polymers, theoretical models are established based on continuum mechanics. Here, governing equations for solvent penetration into polymer are derived and specialized in the reference configuration, including the mechanical-chemical equilibrium state equation, the concentration-dependent diffusion equation, and the molecular number conservation equation. Additionally, a visco-hyperelastic constitutive equation taking into account the time-dependent deformation characteristics of the material is integrated to reflect the competition mechanism between relaxation rate of the polymeric network and migration of solvent in Case Ⅱ diffusion. This modeling approach is used to analyze the transient free swelling process for two material systems, so as to investigate the behavior of unidirectional Case Ⅱ diffusion in columnar and tabular polymer specimens without constraint. By applying appropriate boundary and initial conditions, the concentration, stress, and deformation field variables during the unidirectional diffusion are directly obtained. The distribution and evolution of these calculation results are compared with experimental observations, moderately validating the effectiveness and adaptability of the proposed coupling analysis method regarding polymer swelling. This developed theory may provide important guidance for practical applications such as membrane designing or drug delivery systems, where Case Ⅱ diffusion commonly occurs. It also aids in enhancing understanding of the combination of different polymer-solvent diffusion scenarios, from Fickian to non-Fickian circumstances.