To investigate the impact of surface topography on the mechanical properties of additive manufacturing materials, in this paper, high-strength aluminum alloy specimens were fabricated by the selective laser melting method. The influences of scanning speed, heat treatment, deposition direction, and surface roughness on tensile mechanical properties were examined. The surface topography measured by an optical microscope was reconstructed based on the Fourier series and MATLAB software, and the analytical solution of the stress concentration coefficient of the surface topography was derived using the Airy stress function. Finite element analysis was conducted using ABAQUS software to validate the analytical results. The probability density function of the stress concentration coefficient was obtained through normal fitting, and a method for evaluating the reliability of the material based on yield strength was proposed. The proposed methodology in this paper is of reference significance for the quantification of surface roughness and its effect on yield strength of other additive manufacturing materials and specimens.

Random defects due to differences in raw materials and the complexity of the manufacturing process are inevitable in engineering structures. Based on the inherent characteristics of sensitivity to defects in the film-substrate system, the Monte Carlo method is applied in the study of the stability of structures with random defects, coupled with numerical simulations to investigate the morphological evolution and post-buckling equilibrium path of film-substrate systems with random defects during instability. The numerical results show that the critical load of the structure with random defects is unstable. The defects significantly reduce the critical load of the structure, and the random defects destroy the symmetry of the structure, leading to a transformation from an ordered checkerboard pattern to a disordered fold nuclear pattern, hence affecting the subsequent morphological trend. This analysis assesses the potential risks and effects of random defects in thin-film structures, aiming to improve the reliability and performance of thin-film devices, coatings and surface treatments, and to bridge the gap between theoretical stability research findings and practical design applications.

Conductive polymer composite (CPC) foam exhibits excellent characteristics such as high plasticity, energy absorption, as well as thermal and acoustic insulation, and holds enormous potential for applications in various fields including construction, transportation, electronics, etc. However, the porous structure of CPC foam is usually simple and random, which limits its further application. The complexity of CPC processing makes it challenging to achieve a controlled design of micro-porous structures. Inspired by the idea that biomaterials can enhance their mechanical properties by virtue of their well-aligned anisotropic microstructures, highly aligned anisotropic porous biomimetic microstructures are constructed by a bidirectional freeze-casting process to enhance the compressive mechanical properties of CPC foam. Compared to traditional unidirectional freezing, the compressive elastic modulus and peak stress of aligned anisotropic porous microstructured CPC foam increase by 18.7% and 25.4%, respectively. Buckling and collapsing risks during cyclic compression are significantly reduced, and a peak stress of 91.1% and a strain recovery of 89.6% are still maintained after 2,000 cycles at 50% strain. A finite element model of the porous structure in CPC foam is built with parameters including elastic modulus, hole wall thickness, and Poisson's ratio, obtained from measured data or literature. The quasi-static compressive behaviors of biomimetic and disordered structures are investigated using the finite element method, and the deformation and stress distribution are compared with the corresponding experimental results. Through finite element simulations and experimental tests, it is found that the main mechanisms enhancing the compressive mechanical properties of the materials are as follows: stress distribution optimization effectively prevents plastic deformation caused by local stress concentration; the highly elastic behavior of micrometer pore wall and its 3D structure enhance the bionic structure's resilience; and the highly aligned anisotropic channels provide ample deformation space, improve deformation coordination, and enhance the structure's reversibility during loading and unloading.

To improve blast and impact resistance of sandwich structures, this study introduces a composite sandwich structure comprising a re-entrant (RE) negative Poisson's ratio core, polyethylene (PE) fibers, and silicon carbide (SiC) ceramics. Utilizing the coupled Eulerian-Lagrangian (CEL) algorithm within ABAQUS, the dynamic response of this structure under explosive loading was simulated, assessing the impact of various core layer configurations on protective performance through structural deformation mechanisms, velocity response features, and energy absorption capacities. At equivalent areal densities, the incorporation of ceramic and polyethylene layers led to reductions in upper and lower panel deformations by up to 53% and 5.7%, respectively, relative to an RE-only sandwich layer. Notably, a core configuration of SiC-PE-RE optimized interlaminar load distribution, minimizing lower panel deformation; an increase in panel support strength correspondingly reduced panel velocities. Positioning the SiC and PE layers at the upper and middle core layers, respectively, achieved peak reductions in upper and lower panel deformations by 18.84% and 16%, compared to the RE sandwich layer, exhibiting the most rapid rate of decay. Conversely, positioning the RE layer at the upper core resulted in augmented local deformations, leading to localized crushing failures in the PE and SiC layers, thereby maximizing the energy-absorption incrementby up to 14%.

Contact resonance atomic force microscopy (CR-AFM) is a powerful technique that enables the measurement of topography and the mechanical properties of various materials at the micro/nanoscale. It can be used in both air and liquid environments. However, when CR-AFM is operated in a liquid environment, the dynamic behaviors of the microcantilever can be significantly different from those in air or vacuum due to the complex fluid-solid coupling of the microcantilever-liquid-sample system and the tip-sample interaction. In this study, we explore the effects of liquid density and viscosity, as well as tip-sample normalized contact stiffness and contact damping, on the dynamics of the AFM microcantilever in liquid environments. We treat the influence of the liquid on the dynamics of the AFM microcantilever as added mass and added damping. Our results show that in free vibration, the natural frequencies of the AFM microcantilever are primarily dominated by the liquid density, while the liquid viscosity plays a dominant role in the quality factor compared to the liquid density. Higher modes exhibit higher sensitivity to changes in liquid viscosity and liquid density. As the normalized tip-sample contact stiffness increases, a higher mode shows increased sensitivity to changes in normalized contact stiffness in a liquid environment. On the other hand, a lower mode is more sensitive to changes in normalized contact damping in a liquid environment. In addition, the dynamic responses of the AFM microcantilever under three different excitation approaches are compared and discussed. Variations in boundary conditions and hydrodynamic loads applied to the microcantilever under these approaches lead to diverse dynamic responses. The findings in this study are essential for the development of micro/nanoscale mechanical property imaging techniques using CR-AFM in liquid environments, as well as the improvement of measurement accuracy and sensitivity.

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.

The mechanical properties of materials are affected by inevitable defects such as inclusions and cracks. Accurate knowledge of their elastic fields is required to prevent stress concentration, which can lead to fracture and plastic damage. To study mutual interactions in an isotropic plane with cracks and inclusions, heterogeneous inclusions are approximated as homogeneous inclusions with the same elastic modulus as the matrix plus unknown eigenstrain based on the equivalent inclusion method, while mixed-mode Ⅰ/Ⅱ cracks are approximated as climb/glide dislocations with unknown densities according to the distributed dislocation technology. Interactions in the plane are fully considered in the governing equation system, and a solvable matrix is established with all unknowns in a unified framework. The conjugate gradient method is used to iteratively solve the unknowns, and the fast Fourier transform is introduced to improve computational efficiency. The stress field of cracks in any direction is settled by the stress transformation law, and the stress intensity factors at crack tips are determined by the converged dislocation densities with the assumption of crack-induced displacements in parabolic shapes. The influence of the heterogeneous properties of inclusions on stress intensity factors at crack tips is then properly captured. The situations of cracks/inclusions are discussed in detail, providing a description of the elastic fields and stress intensity factors. The complexity does not necessarily increase with the number of inclusions and cracks, and the calculation cost depends only on the mesh density. The effectiveness of the model developed in this study is verified using the finite element method. This model has potential application prospects in the fracture failure of heterogeneous materials and the plastic zone problems near crack tips. The conclusions may offer insight into the modeling scheme of various defective structures and the fracture behavior of materials.

The interaction between a penetrating-type circular-arc crack and a screw dislocation in magnetoelectric composites is studied. Firstly, according to the basic equations of magnetoelastic composites and the theory of complex function, the relationship between the anti-plane shear stress, the normal component of the electric displacement, and the normal component of the magnetic induction along the boundary arc c is derived. Then, based on the conformal mapping technique, the complex form of the generalized stress field is obtained by analyzing the stress conditions of the dislocation. In order to discuss the dislocation, dislocation shielding effect, and crack shielding effect at the crack tip, the force-electric-magnetic field intensity factors and the image forces acting on the dislocation are further deduced. By analyzing the analytical solutions and numerical examples, the results show that the shielding effect of the field strength factor decreases with increasing the distance between the circular-arc crack tip and the dislocation point, and the angle formed by their connecting line and the positive half of the x-axis, indicating that the dislocation has a shielding effect on the crack. Additionally, the effect of dislocation on a circular-arc crack is more prominent than on a straight crack. Besides, the image force on the dislocation is affected by the surface properties of the circular-arc crack. Finally, the screw dislocation can reduce the stress intensity factor of the circular-arc crack tip, and the shielding effect rapidly weakens as the angle increases. The shielding effect of the screw dislocation on the crack tip is strengthened as the ratio between the distance from the dislocation point to the crack tip and the half-chord length of the circular-arc crack increases. These conclusions carry meaningful significance for fracture mechanics research and provide a theoretical basis for improving and evaluating the performance of electromagnetic devices.

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.

In this paper, an asymmetric bristle model based on the combination of bristle model and LuGre model is proposed to explain the direction-dependent friction (i.e., asymmetric friction) phenomenon exhibited by steel brushes. In this model, friction is generated through the horizontal frictionless contact between asymmetric bristles and the contacted substrate. Numerical simulation and expreimental results demonstrate that the asymmetric bristle model can effectively illustrate the direction-dependent friction phenomenon. Furthermore, in a simulation study of a planar biped robot, results show that applying this model can improve the maximum motion speed of the planar biped robot compared to using a symmetric friction model. Additionally, exmprimental resutls indicate that compared to rubber materials, steel brushes possess the advantages of high friction and abrasion resistance. Therefore, due to these propterties, steel brush structures may have great application prospects and potential benefits in the field of legged robotics.