Nomex honeycomb materials are widely used in marine，high-speed rail，aerospace and other fields because of the material’s low density，high specific strength and high specific stiffness. However，the non-homogeneous，thin-walled and brittle characteristics of honeycomb structures make them prone to tearing，crushing and other defects during processing. Therefore，scholars both in China and abroad have used ultrasonic machining technology to solve the problem of Nomex honeycomb material processing with the advantages of small cutting force and high surface quality，which have carried out extensive and in-depth research. However，compared with ordinary machining，high-frequency vibration and periodic impact generated in high-quality and efficient ultrasonic machining process put forward higher requirements for tool performance and life. Therefore， focused on tool material selection， tool design， kinematic analysis， cutting force and cutting heat modeling， and surface topography analysis during machining of Nomex honeycomb materials. The research status of machining mechanism，cutting characteristics and ultrasonic tool design for ultrasonic cutting of Nomex honeycomb materials were summarized. The future research and development trend of ultrasonic cutting Nomex honeycomb materials were put forward， which provided a reference for further research on the service performance and processing quality of ultrasonic cutting tools in cutting.
To accurately capture the aerodynamic characteristics of Z-shaped folding wing at different folding angles， while considering the influence of the structural elastic effect of different wing segments， a parametric aerodynamic-structural coupling model was constructed based on a quasi-steady state environment.Using ANSYS Workbench software with the RNG turbulence model and the Coupled algorithm，numerical simulation was conducted to analyze the aerodynamic characteristics of the folding wing under the action of elastic effect and the results were compared with those of a rigid wing. Additionally， the deformation laws of the elastic folding wing in different flight environments were systematically explored. The results show that under the influence of elastic effect， the absolute values of lift and pitching moment of the folding wing are slightly lower than those of the rigid wing. As the folding angle increases， the impact of elastic deformation on the overall aerodynamic performance of the wing stabilizes. When the folding angle is 90°， the differences in lift and pitching moment between the elastic and rigid wings are only 0.93% and 0.70% respectively. Furthermore， under the influence of elastic effect， the maximum deformation of the wing occurs at the wingtip. As the folding angle increases， the magnitude of wingtip deformation gradually decreases.
In order to solve the thermo-mechanical fatgue damage problem of trubine blade under the complex multifield coupling service conditions，combining the improved Morrow low-cycle fatigue damage and creep damage models based on the load spectrum of the engine’s service conditions，the prediction of the thermo-mechanical fatgue life of engine turbine blade was achived.Firstly， considering the rotor-stator interference effect between cascades， the three-dimensional flow field modeling and multi-field coupling simulation of the turbine blade were completed. Further， based on the engine load spectrum and multi-field coupling response characteristics under service operating condition， the key assessment positions of fatigue damage of the turbine blade were determined. then， an improved Morrow low-cycle fatigue damage model was developed and compared with traditional models and experimental results for verification. Finally， using the linear cumulative damage criterion， as well as the Morrow low-cycle fatigue damage and L-M creep damage， thermo-mechanical fatigue life of the turbine blade was predicted under service conditions. The results show that the numerical simulation results are in good agreement with the experimental data， verifying the accuracy of the three-dimensional unsteady flow field simulation of the turbine blade and the improved Morrow low-cycle fatigue damage model. Under the engine load spectrum and multi-field coupling effect of service conditions， considering the Morrow low-cycle fatigue damage and L-M creep damage， the thermo-mechanical fatigue life of the turbine blade is 6.028×10³ h. The area with the minimum life is located at the leading edge of the blade root at the air inlet， the assessment position A， which is the key maintenance part of the turbine blade. This study can provide a theoretical reference and basis for the thermo-mechanical fatigue life assessment of turbine blade under complex service operating condition.
In order to improve the accuracy of component performance evaluation under a whole-engine test environment for aero-engine，a performance optimization and evaluation method was proposed for aero-engine components based on the artificial bee colony algorithm. This method took the performance parameters of the components to be evaluated as input variables. It utilized an overall aero-engine performance simulation model combined with the ABC algorithm to optimize and obtain the optimal set of performance parameters for the components to be evaluated， ensuring compliance with the accuracy requirements of the test parameters. This method was applied to evaluate the component performance under the test environment of a core engine. The results show that the deviation between the evaluated parameters and the measured values remains within 0.95%， demonstrating the high effectiveness and engineering practicability of the evaluation method. During the multi-operating condition optimization process， to balance the accuracy of the entire speed range， the maximum deviation increases by approximately 0.1%. But the introduction of solution constraints enhances the authenticity of the performance evaluation and reduces the impact of test uncertainties on the performance evaluations. Meanwhile， the correction characteristics of component performance under different inlet temperature environments of the core engine are obtained in the optimization process.
To ensure the dynamic performance of the electric helicopter equipment platform under complex vibration and shock environments， a combined test and simulation approach was adopted to conduct an in-depth investigation of a specific electric helicopter equipment platform. The simulation part utilized HYPERMESH and ANSYS software to perform modal analysis， frequency sweep analysis， random vibration analysis， and transient response analysis， obtaining the platform’s dynamic characteristics and key data. The test part included random vibration and shock tests. The random vibration testing employed an acceleration power spectral density （PSD） scaled to 1g RMS as the excitation condition， and the shock testing was conducted with a peak acceleration of 4 g and a half-sine wave excitation lasting 6 ms. The results show that the first two modal frequencies of the platform are 33.735 Hz and 39.751 Hz， and the low-frequency modes may couple with low-frequency excitations during operating condition. Random vibration analysis shows that the primary response is concentrated within the 70~300 Hz range. The maximum deformation of the platform under random vibration conditions is 1.572 9 mm， and the stress distribution is uniform， meeting the material yield strength requirements. Transient shock analysis indicates that the platform’s maximum stress under a 4 g shock is 11.464 MPa， which is significantly lower than the material yield strength. The correlation between test and simulation results verifies the reliability of the platform design. This study provides a theoretical basis and reference for the optimized design of electric helicopter equipment platforms.
To meet the requirements for open-loop trajectory motion of a solar-powered car， a design and fabrication method was proposed for a solar-powered car. Firstly， by using B-spline curves， the characteristic points were fitted to generate an ideal open-loop trajectory curve. MATLAB software was then utilized to analyze and optimize the planned trajectory to produce the cam profile. Next， based on the overall functional requirements， the overall structure， transmission mechanism， and steering fine-tuning mechanism of the solar-powered car were designed. Subsequently， simulation software was employed to perform simulation analysis and optimization of the cam trajectory， and the car’s circuit modules were assembled. Finally， actual driving tests of the car were conducted. To improve the trajectory accuracy of the car， the front wheel offset angle was modified by adjusting the fine-tuning mechanism. Experimental results demonstrate that the car fabricated using this method operates stably and successfully passes through all predetermined characteristic points.
In the context of cooperative operation between UAV and ground vehicle， rotor UAV often needs to pass through necessary points or avoid obstacles when executing related tasks， which increases the trajectory fluctuation and brings more challenges to path planning. To solve this problem， an improved discrete mechanics and optimal control （DMOC） trajectory optimization algorithm was proposed， which transformed the optimal control problem into a nonlinear programming problem. Considering the endurance time of vehicle-mounted rotor UAV， the shortest time was set as the optimization objective under guaranteed traversal conditions. In the process of the algorithm implementation， an approach was proposed to adjust the distance walk length according to the size of the error factor， and then approximate correlation integral， which effectively solved the problem of trajectory fluctuation. The experimental results show that this method can improve the smoothness of the optimal trajectory while ensuring that all the necessary points are traversed，and also provides a reference for the trajectory optimization of other agents.
In the highly complex and intensely adversarial air combat environment， current unmanned aerial vehicle intention assessment methods generally face challenges such as strong subjectivity of fusion rules， ignoring the time correlation of relevant attributes， and insufficient defect information processing means. To address these issues， this paper proposes a dynamic evidence network method that integrates defect information correction and subjective and objective rules. Firstly， focusing on the strong correlation of continuous variables in the time dimension， a spatiotemporal fusion evidence network model was modularly constructed. Subsequently， by introducing the LSTM trajectory prediction technology， a defect evidence correction mechanism was established， which significantly improved the accuracy and integrity of information. Finally， objective rules were designed based on statistical calculation methods， and combined with subjective experience， a library of subjective and objective rules was constructed. Based on the above improvements， simulation experiments were conducted. The results confirm that the proposed mechanism of defect information correction， subjective and objective rules， and dynamic fusion have significant advantages in improving the accuracy and credibility of intention recognition results.
Automatic drilling technology plays an important role in modern aircraft manufacturing. To improve the efficiency and reduce the cost of aircraft assembly and manufacturing，a hole path planning algorithm of aircraft curved surface components was studied in depth. Based on the situation that the traditional ant colony algorithm was easy to fall into local optimum and converge slowly，the new school-based optimization algorithm combined with the traditional ant colony optimization algorithm was proposed to achieve the optimization of the drilling path of aircraft curved surface components. The simulation results show that the improved school-based optimization ant colony algorithm is superior to the traditional ant colony algorithms in terms of drilling path length and iteration speed.
To address the issue of poor system adaptability inherent in traditional event-triggered mechanisms，a problem of dynamic event-triggered consensus of uncertain multi-agent systems under switching topology was studied. The event-triggered mechanism could not effectively guarantee the system adaptability in practice. It was proposed to use the dynamic event-triggered mechanism to dynamically adjust the current state of the system， which could reduce the frequency of the system update. In designing the consensus controller， factors such as switching topology， time delays， and external disturbances were considered. Algebraic graph theory and related theoretical methods proved that the dynamic event-triggered mechanism set the consensus controller to achieve the consensus of the uncertain multi-agent system. Finally， the simulation results confirmed that the method is reasonable.
The key to achieving high level of scientific and technological self-reliance is to strengthen the incentives for the innovative behaviors of scientific and technological personnel. Based on the AMO theory， combined with NCA and QCA methods， a driving path of scientific and technological personnel’s innovative behaviors from the perspective of complex interactive configuration between individuals and organizations was explored. The results show that the emergence of a single factor is not a necessary condition for the outcome variable. There are three paths that can drive the high innovative behavior of scientific and technological personnel. It enriches the relevant theoretical research on the scientific and technological personnel’s innovative behavior， providing reliable basis and concrete measures for managers to effectively drive the innovative behavior of scientific and technological personnel.
To enhance the resilience of drone enterprise supply chains and their ability to respond to risks， the impact and mechanism of artificial intelligence technology on supply chain resilience were empirically examined which based on data from A-share listed drone companies from 2016 to approximately 2022. It was found that strengthening artificial intelligence technology could significantly improve supply chain resilience. This conclusion remained valid after a series of robustness tests and was particularly pronounced for small and medium-sized drone companies with lower market share. Mechanism analysis indicated that artificial intelligence mainly operates through two pathways： enhancing supply chain concentration and reducing internal management costs. By optimizing resource allocation， improving operational efficiency， and reducing management expenses， it strengthened the enterprise's ability to cope with market fluctuations and external shocks. This study provided policy and strategic recommendations for governments and companied to use artificial intelligence to optimize supply chain management， as well as the oretical support and practical guidance for enhancing enterprise risk resilience.
