Aiming at the problems of traditional impact load identification methods, such as the requirement for a large number of sensors, high sampling frequency, and low identification accuracy, a new impact load identification method based on empirical mode decomposition (EMD) technology was proposed.The EMD technology was used to decompose the complete impact response to obtain the modal acceleration response. The impact location was quickly realized by measuring the collinearity between the uncorrected mode shape vector and the column vector of the mode shape matrix in the modal acceleration response. According to the positioning results, an optimization objective function was constructed. The time history of the impact load was fitted by using the Gaussian basis function, and the optimal fitting parameters were quickly solved by using the two-dimensional gradient descent method.Tests conducted on a cantilever plate with dimensions of 600 mm×200 mm×3 mm show that with only one accelerometer, the success rate of 36 impact positioning tests is 91.67%. The peak relative error and relative error index of the reconstruction results are less than 10% and 40%, respectively.

Aiming at optimizing the gear surface modification process, the influence of ion nitrogen implantation on the bending fatigue strength of carburized and quenched gears was studied.Using low-carbon alloy steel 18CrNiMo7-6 carburized and quenched gears as the matrix, nitrogen ion implantation treatment was carried out through a radio-frequency plasma-assisted ion implantation system. The root metallography, hardness gradient, residual stress distribution, and bending fatigue properties of ion-implanted gears and unimplanted gears were systematically compared. The results show that the ion nitrogen implantation process increases the root hardness from 695 HV0.1 to 780 HV0.1, an increase of 12.2%; the hardened layer depth decreases from 1.50 mm to 1.41 mm, a reduction of 6.0%; and the surface residual stress decreases from -400 MPa to -286 MPa, a reduction of 28.5%. Based on the R-S-N equation fitted by bending fatigue tests, under 99% reliability, the fatigue life of ion-implanted gears is only 12.3%-19.3% of that of the control gears, with the failure mode dominated by brittle fracture and accelerated crack propagation rate. The study indicates that although ion nitrogen implantation can delay crack initiation through surface strengthening, the excessively shallow hardened layer and reduced residual stress lead to insufficient crack propagation resistance, ultimately weakening the bending fatigue life of gears.

To accurately simulate the complicated load transfer pattern of the launch device under cold launch mode, the rigid-flexible coupling dynamic simulation method was adopted to analyze the dynamic response of a launch vehicle, the structural strength was also verified. The finite element flexible body was introduced and a multi-rigid-flexible-body dynamic simulation model of a launch vehicle was constructed. Accuracy of the model was verified by the actual launch test. The influence law between the vibration response and stress state of the launch vehicle and the launch angle was further analyzed. The results show that amplitudes of the transmitted load and stress inside the launcher will reduce when the launch angle is close to 90°. The modeling and analysis approaches proposed in this study can effectively support the optimal design of the launch device.

In order to study the strain softening phenomenon of ultra-fine grain (UFG) metal materials under uniaxial tensile loading, a modified model considering the effect of residual internal stress was proposed based on the classical crystal plasticity constitutive model, and the specific form of residual internal stress and its evolution were programmed into the user subroutine. The uniaxial tensile test data were fitted to verify the validity of the model, and the finite element simulation results of crystal plasticity were compared with and without the residual internal stress. The results show that the simulation results obtained by using the modified crystal plasticity constitutive model are in good agreement with the experimental results, indicating that the modified crystal plasticity constitutive model can effectively capture the strain-softening phenomenon of UFG metal materials, and the simulation results show different properties under the two conditions whether the residual internal stress is taken into account. It is reasonable to explain the strain softening phenomenon of UFG metal materials from the perspective of the formation and action of residual internal stress.

The harsh operational environment of helicopters renders their structures highly susceptible to the initiation and propagation of hole-edge cracks around bolt holes, thereby compromising structural integrity and load-bearing capacity. To monitor hole-edge cracks in helicopter attachment lug structures, piezoelectric guided wave-based structural health monitoring(SHM) techniques are commonly employed. However, due to the difficulty in detecting small cracks at the early stages of propagation and the presence of large through-hole configurations in attachment lug structures, the accuracy of guided wave monitoring remains suboptimal. Therefore, addressing the accuracy issues in crack monitoring of attachment lug structures,this study proposes a piezoelectric guided wave array-based method for hole-edge crack detection. Firstly, damage feature information was extracted from acquired piezoelectric guided wave array signals encompassing the entire sensor network.Subsequently, a damage alarm threshold was established using a mean-value method to facilitate damage detection.Furthermore, an improved delay-and-sum imaging algorithm was developed based on the specific configuration of the attachment lug structure to optimize probability distribution and achieve precise crack localization. Finally, validation was conducted through test monitoring of crack propagation in attachment lug structures. Test outcomes demonstrate that the proposed method enables accurate alarm triggering and localization of hole-edge cracks around bolt holes, with localization errors confined within 2.01 mm, thereby confirming the efficacy and precision of the proposed approach.

To address the issue of accuracy degradation caused by aerodynamic damping when measuring the moment of inertia of irregular specimens with large airfoil surfaces using the torsional pendulum method, a compensation approach based on drag simulation results was proposed. Initially, the mechanism of aerodynamic damping in torsional oscillations was analyzed, and a measurement model incorporating compensation through calculation of aerodynamic damping torque was established. Subsequently, the reduced frequency parameter was introduced to characterize the unsteady aerodynamic nature of the aerodynamic damping torque. By employing a quasi-steady assumption combined with equivalent linearization techniques,the unsteady time-varying aerodynamic damping torque was equivalently represented as viscous damping. Furthermore,computational fluid dynamics (CFD) simulations were conducted to obtain drag coefficients during specimen motion, from which a compensation formula based on drag coefficients was derived. Finally, validation test were designed and performed to verify the proposed method. The findings indicate that under low reduced frequency conditions (reduced frequency less than 0.01), the relative error between the equivalent aerodynamic damping ratio calculated via quasi-steady assumption and test separation values is approximately 7%. After compensating using the proposed equivalent aerodynamic damping ratio, the error between measured and theoretical moments of inertia is approximately 0.2%, demonstrating that the proposed method effectively enhances measurement accuracy for moments of inertia of irregular specimens with large airfoil surfaces.

In order to ensure the industrial robot realizes the reliability goal, reliability allocation is a task to be accomplished in its manufacturing design stage. According to the characteristics of industrial robots, such as complex structure, high uncertainty, few samples, and failure correlation between component parts, a reliability allocation method for industrial robot systems based on BP neural network and Pythagorean fuzzy numbers was proposed. Using Copula function to establish a system reliability model, the failures of industrial robots were classified into three levels, the system level, the subsystem level, and the component level. By using the back propagation (BP) neural network, the system reliability,subsystem structure importance and subsystem complexity were taken as the input variables to complete the system-to-subsystem reliability allocation. The Pythagoras fuzzy number was introduced to score the influence factors of importance,environmental condition, technical level, maintainability, cost sensitivity and complexity, complete the reliability allocation from the subsystem level to the component level.The results show that the methodology achieves reliability goals and ensures reliability growth.

Aiming at the problem that loop-end braided stents are prone to loop-end fracture during crimping, the key design parameters affecting the structural stability of loop-end braided stents were explored, and an improvement scheme was proposed.Numerical simulation of crimping for magnesium alloy loop-end braided stents was conducted using Abaqus analysis software. The results showed that increasing the braiding angle can effectively reduce the risk of loop-end fracture.Based on this finding, a variable pitch stent was designed, which features increasing the braiding angle at both ends to form sparse segments while maintaining the braiding angle in the middle of the stent. The influence of adjusting the length and braiding angle of the sparse segments on the mechanical response of the stent was evaluated.The research results showed that increasing the braiding angle and length of the sparse segments can effectively reduce the axial elongation and loop-end stress of the stent, but it will shorten the length of the middle dense segment, thereby reducing the radial force of the stent. Different from the flared expansion of the constant pitch open-end stent, the variable pitch stent presents a shape with large middle expansion and small end expansion after expansion.This study reveals the potential of variable pitch design in controlling loop-end fracture and provides a reference for the structural optimization of magnesium alloy braided stents.

Aiming at the issues of fracture and weight reduction in the wheel hub motor housing of an off-road vehicle, a structural strength finite element simulation analysis and structural topology optimization design were conducted. Firstly, a multi-body dynamics model of the entire vehicle was established, and a simulation analysis was performed to determine the load boundary conditions of the hub motor housing. Secondly, based on the spatial position relation between the housing and interconnected structures, a finite element model of the motor housing and suspension system was constructed for dynamic simulation analysis. Subsequently,using the OptiStruct software platform,with the objective of minimizing structural compliance and constraints on volume ratio before and after optimization as well as the maximum stress, a mathematical topology optimization model for the motor housing under various typical operating conditions was established and solved to obtain the optimal material distribution scheme. Finally, the optimization results were verified by simulation. The results indicate that compared to the existing design,the optimized hub motor housing structure experiences a stress reduction of over 40% and a weight reduction of 2.6%. It addresses the original fracture issue and eliminates the phenomenon stress concentration, thus providing the valuable reference for the design of similar hub motor housing structures.

The telescopic arm, a pivotal component in the pipeline grabbing vehicle, links the lifting platform and the mechanical claw, shouldering the majority of the load. Conducting a reliability analysis is imperative. Traditional methods for reliability face challenges like high computational costs and low accuracy dealing with multidimensional uncertainties. To overcome these, our study proposed an engineering mechanical reliability analysis method, leveraging Adams dynamic simulation, semi-supervised learning, deep neural networks, and Monte Carlo method. In this study, a virtual prototype model of the pipeline grabbing vehicle was established, identifying hazardous operating conditions. Combining the telescopic arm model’s geometric parameters and overall structure, uncertain factors influencing the maximum von Mises stress were determined, conducting a sensitivity analysis was conducted. Utilizing optimal Latin hypercube sampling based on uncertain parameter distributions, Ansys Workbench was employed to build a finite element model, obtain output results for the sample size. Semi-supervised learning processed the finite element simulation data, enhanced deep neural network training accuracy.Finally, based on the fourth strength theory, a failure criteria for the telescopic arm component was determined. Combining deep neural networks and Monte Carlo method, the reliability and failure probability were predicted. Results show that this method surpasses actual engineering precision requirements，provides a certain guiding significance.