To address the insufficient interlayer mechanical properties of glass fiber-reinforced polyetheretherketone （GF/PEEK） composites fabricated by fused deposition modeling （FDM）， resulting from the impracticality of post-process heat treatment for large-scale components and structural circuit-integrated components， an in situ thermal radiation-assisted strengthening method was proposed to enhance the interlayer mechanical properties along the build direction. The mechanical properties and interlayer strengthening mechanism of GF/PEEK composites under different thermal treatment strategies were systematically investigated by regulating the chamber temperature and in situ thermal radiation power， combined with tensile tests， interlayer tensile tests， and fracture surface morphology analysis. The results showed that increasing the chamber temperature improved the tensile properties in the horizontal direction. At a chamber temperature of 200 ℃， the tensile strength and Young’s modulus reached 62.72 MPa and 3.45 GPa， respectively. However， the interlayer tensile strength decreased from 20.61 MPa to 6.03 MPa， while the interlayer Young’s modulus increased from 1.90 GPa to 2.24 GPa. By contrast， in situ thermal radiation significantly enhanced the interlayer mechanical properties of specimens fabricated along the build direction. At a thermal radiation power of 255 W， the vertical tensile strength and fracture elongation increased to 2.34 and 7.91 times those of conventional FDM specimens， respectively， achieving mechanical properties comparable to those of post-process heat-treated specimens. Fracture surface analysis further revealed that in situ thermal radiation promoted interlayer polymer chain diffusion and molecular entanglement while reducing interfacial defects， thereby substantially improving the interlayer bonding performance. The proposed method effectively enhances the interlayer mechanical properties of FDM-fabricated GF/PEEK composites without requiring an additional post-process heat treatment， providing an effective processing strategy for the additive manufacturing of high-performance thermoplastic composite structures.
Accurate excitation current prediction is crucial for the high-performance control of synchronous machines （SMs）， which are widely employed in industrial drives such as electro-spindles. However， achieving accurate and generalizable prediction across multiple operating points is challenging due to coupled nonlinearities like thermal drift and magnetic saturation. This study proposes a novel prediction model based on the extended long short-term memory （xLSTM） network. The model integrates scalar LSTM （sLSTM） and matrix LSTM （mLSTM） units and leverages an exponential gating mechanism to enhance the capability for learning complex nonlinear mappings and long-term dependencies. Specifically， the vectorized parallel memory structure of sLSTM is suited to capturing slow parameter variations caused by thermal drift， while the matrix associative memory mechanism of mLSTM excels at learning multi-variable nonlinear coupling effects such as magnetic saturation. These two modules form a complementary hybrid architecture. Comparative analyses against traditional LSTM and gate recurrent unit （GRU） benchmarks were conducted using SM monitoring data covering various load and excitation conditions. In addition， an ablation study was performed using xLSTM with varying blending ratios of scalar and matrix LSTM components. Evaluation based on multiple error metrics and computational time demonstrates that the proposed xLSTM achieves superior accuracy， stronger generalization， lower computational overhead， and higher prediction stability. The underlying mechanisms are analyzed from architectural and algorithmic perspectives. These findings offer a novel data-driven modeling approach for SM excitation current， with potential value for applications requiring high-fidelity motor state estimation.
As a critical component of the turbine rotor， the baffle plays an essential role in ensuring safe and reliable operation of the aero-engine. This study addresses the issue of excessive local stress in the baffle of a high-pressure turbine disc. The structural design optimization is performed using the self-developed Zhizhou software integrated with the slime mould algorithm （SMA）. Leveraging advanced algorithms and comprehensive simulation interfaces， the Zhizhou software effectively exploits the potential of structural design， leading to significant improvements in structural performance. The SMA algorithm employs a positive feedback mechanism and adaptive strategies through the incorporation of fitness weights and oscillation factors. These parameters simulate the oscillatory contraction behavior of slime moulds， allowing the algorithm to dynamically adjust search direction and speed， thereby achieving an effective balance between local exploration and global optimization. During the optimization process， a sector sub-model is established， and the contact model is simplified using a force load equivalence approach to improve computational efficiency. Subsequently， a parametric model of the baffle is developed based on geometric characteristics， stress responses， and boundary constraints， with the variation ranges of key parameters being determined. A mathematical model is then formulated with the objective of minimizing the maximum equivalent stress， under the constraint of the axial support reaction force at the contact surface. Finally， an integrated design optimization workflow is constructed using Zhizhou in combination with Unigraphics （UG） and Workbenchs， as well as incorporating the SMA algorithm to optimize the baffle structure. After optimization， the maximum equivalent stress is decreased from 1 382.4 to 1 235.4 MPa， a reduction of 10.6%. Meanwhile， the axial support reaction force is increased from 4 158.9 to 4 330.6 N， a variation of 4.0%， which satisfies the requirement for being within 12%. These results validate the effectiveness of the SMA algorithm in the structural design optimization of the baffle and demonstrate the practical value of the Zhizhou software in engineering applications.
Machine learning provides a fast and accurate tool for the prediction of a physical model. In this paper， a machine learning framework based on the physics-informed neural network （PINN） was established to predict the linear elastic static deformation of plate and shell structures. In contrast to the purely data-driven neural network， PINN incorporates the physical laws into the training process， thus reducing the required amount of data. The loss functions of the PINN are constructed based on the total potential energy functions of the thin-walled structure. Besides， the proposed PINN can be easily extended to shell structures with multiple patches by adding interface compatibility constraints into the loss function. The performance of the PINNs with the energy-based loss functions was evaluated with different shell structures and compared with the finite element results. Numerical examples show that the highly accurate results can be achieved based on the proposed framework which significantly reduces the amount of required training data compared to the data-driven neural network.
A hybrid action deformation control method based on hybrid proximal policy optimization （HPPO） is proposed for titanium alloy structural components. Existing reinforcement learning algorithms are generally confined to either discrete or continuous action spaces， and thus cannot simultaneously optimize machining sequence and allowance. The proposed method unifies both decision variables—machining sequence as discrete actions and machining allowance as continuous parameters—into a single parameterized hybrid action space. Online deformation force monitoring data serve as state feedback to enable adaptive control under dynamic machining conditions. A dual-layer reward mechanism combining process-level deformation force uniformity with terminal deformation convergence is designed to guide the agent toward synchronized suppression of both local and global deformations. Experimental validation on a Ti6Al4V aviation structural component demonstrates that the proposed method reduces average machining deformation from 0.103 mm to 0.054 mm， with RMSE decreasing from 0.119 mm to 0.071 mm， representing a 47.57% reduction relative to the uncontrolled case. These results confirm the accuracy and effectiveness of the proposed method in real manufacturing environments.
The deployment of unmanned aerial vehicle （UAV） hangars is critical to the efficiency of forest inspections， significantly influencing both infrastructure construction costs and operational expenses. Existing research on hangar selection often overlooks the complex constraints posed by forest environments， such as topographical variability， power limitations， and coverage demands. To tackle these challenges， this paper presents a multi-objective optimization approach for UAV hangar selection in forest environments， aiming to reduce construction costs while maximizing coverage under complex topographical constraints. The process begins with the preliminary selection of candidate hangars， utilizing geographic data such as the digital elevation model （DEM）， meteorological data， and power/signal coverage. A multi-criteria decision analysis （MCDA） method evaluates and scores candidates based on rigid and flexible criteria， including topographical suitability， wind speed， and power supply availability. A multi-objective optimization model is then developed to optimize the layout of hangars， incorporating critical constraints such as topographical characteristics， UAV power limits， and coverage redundancy. To solve this optimization problem， the non-dominated sorting genetic algorithm Ⅱ （NSGA-Ⅱ） is applied. Experimental results demonstrate that the proposed method outperforms traditional approaches， such as the greedy algorithm and the single-objective genetic algorithm. Specifically， the NSGA-Ⅱ method reduces the number of hangars by 8.3%， and increases the coverage by 1.6%. It also significantly accelerates the convergence， demonstrating superior performance and efficiency. This methodology provides a comprehensive solution for UAV deployment in forest inspections and can be adapted to other complex topography.
Continuous fiber-reinforced metal matrix composites （CFMMCs） exhibit exceptional specific strength and high-temperature resistance， making them ideal for aerospace applications. However， their anisotropic and heterogeneous structure lead to severe machining challenges， including tool wear， fiber pull-out， and interfacial debonding. This review summarizes the current state of CFMMCs machining， emphasizing the role of energy field-assisted machining and their limitations. Conventional machining （CM） exhibits complex material removal mechanisms involving plastic deformation， brittle fracture， and interface failure. Ultrasonic vibration-assisted machining （UVAM） reduces cutting forces and residual stress through acoustic softening， while laser-assisted machining （LAM） induces fiber ductile transition and matrix softening. Femtosecond laser machining further enables high-precision， low-damage ablation. Despite these advances， research gaps remain regarding anisotropic effects， parameter coordination， and damage-service life relationships. The development of multi-energy field synergy， AI-based closed-loop control， and integrated additive-subtractive platforms is also analyzed. Finally， based on the current development status and the requirements of aerospace manufacturing， future trends in CFMMCs machining are proposed.
With the rapid evolution of weapon systems towards precision and intelligence， unmanned aerial combat has increasingly transitioned from close-range engagements to beyond-visual-range （BVR） operations. This paper addresses the challenges of learning effective missile launch strategies in BVR air combat， where the long delay between weapon launch and target hit leads to sparse and delayed reward problems. This paper first extends the multi-agent proximal policy optimization （MAPPO） framework to incorporate expert rule-based launch control， resulting in MAPPO with launch constraints （MAPPO-LC）. This method ensures that missile launch decisions satisfy tactical constraints on distance， altitude and timing while providing the learning process with a viable starting policy. Building upon this baseline， this paper introduces MAPPO with reward return （MAPPO-RR）， a reward return mechanism that explicitly identifies missile launch and hit events as key decision nodes， and return the hit reward to the launch step. This reward redistribution method mitigates the delayed reward problem， and significantly accelerates policy convergence in multi-agent BVR scenarios. Experimental evaluation demonstrates that the MAPPO-RR algorithm achieves a win rate exceeding 75% and exhibits a sampling efficiency over 55% higher than that of the baseline method.
Visible light cameras are excellent at capturing subtle features of moving targets in well-wit and stable scenes.However,such cameras may not be able to accurately detect targets when encountering occlusion,fluctuating light intensity,or shadow effects,leading to the occurrence of missed or false alarms.Aiming at the problem of poor anti-interference ability of visible light images in complex scenes,a target detection method based on the combination of visible light and infrared images is proposed.The Canny algorithm is used to preprocess the unmanned aerial vehicle(UAV)infrared image,the seed points are obtained through the Sobel operator,and the image segmentation is performed using the maximum inter-class variance value of the image as the growth criterion in order to locate the UAV region in the infrared image.Then the corresponding region of the visible image is cropped,and UAV target detection is performed in this region.The joint detection method narrows the scope of detection and effectively reduces the interference of factors such as illumination changes and interfering objects on the detection results.Experimental results show that the proposed method achieves 87.6%precision and 75.9%recall,with mAP0.5 and mAP0.5:0.95 values of 83.9%and 52.9%,respectively.
Generative design methods have been widely applied in modern aircraft aerodynamic design. However， the integrated optimization of aerodynamic and stealth performance in aircraft still relies on surrogate models and multi-objective optimization algorithms. To address the complex verification and optimization procedures in current integrated aerodynamic-stealth aircraft design， this paper proposes a rapid generative design method based on a conditional denoising diffusion probability model （CDDPM）. First， the class-shape transformation （CST） method is employed for parametric modeling of airfoils. To build the aerodynamic and stealth performance datasets， the vortex lattice method and the physical optics method for large-sized objects are used to compute the lift-to-drag ratio （L/D） and radar cross-section （RCS）， respectively. Based on the dataset， a generative conditional diffusion model is implemented to achieve the mapping relationship from target performance （L/D and RCS） to CST parameters of wing airfoils. Validation results indicate that the prediction errors for the generative model in aerodynamic-stealth performance are smaller than 6%. Meanwhile， the generated airfoils exhibit notable diversity. Furthermore， optimization design of airfoils considering both aerodynamic and stealth performance is conducted， where the diffusion model is utilized to generate new airfoils to expand the design space. The pareto front is obviously expanded with the minimum RCS decreased by 28.6%， and the maximum L/D increased by 7.5%. This study establishes a generative model-based framework for rapid aerodynamic-stealth optimization of airfoils， laying a foundation for AI-driven multidisciplinary design optimization （MDO） in aircraft design.
With growing urban congestion， urban air mobility （UAM）， which brings urban mobility into a third dimension for more efficient urban travel， has become a global research focus today. To provide a detailed summary of the development status and critical issues for UAM， this paper conducts bibliometric analyses on relevant publications in the Web of Science database from 1993 to 2024 using visualization software， such as VOSviewer and CiteSpace. This study covers publication output， country collaboration networks， core institutions， co-citation analysis， high-frequency keywords， and thematic mapping. Together， these indicators outline the core scholarly development of the UAM field. The findings aim to help readers gain a clearer understanding of the current research landscape and identify promising directions for future exploration.
A novel decentralized control approach for unmanned aerial vehicles （UAVs） based on emergent collective behavior identified in starling flocks is proposed in this paper. With the self-organizing principle of starling flocking separation， alignment and cohesion behaviors， a fully distributed leaderless control is designed based on local interaction only. It is augmented by an adaptive switching event-triggered communication protocol， a novelty which greatly reduces the communication load， yet maintains swarming cohesion and formation stability rigorously. Via the Lyapunov stability theory， the formation error can converge to a boundary range， robustness to disturbances and partial communication burden. Ultimately， the numerical simulations are presented to verify the effectiveness of the theoretical results.
Modular design of complex engineering systems is a universal technology for rapid system design in the modern industrial field. Generating assembly schemes for modular engineering systems in industrial production is the core of achieving automation in system design. Traditional system design methods based on human experience suffer from low efficiency and poor adaptability. To enable the automatic assembly of modules for engineering systems， the performance matching and correlation between modules need to be accurately identified. Currently， general large language models， with their strong language analysis capabilities， have been applied to multiple industries. However， limited by data barriers in specific industrial field， their application in assembly of system modules is rare. Therefore， constructing high-quality domain data， fine-tuning professional large language models， and developing domain frameworks for automatic design of modular system constitute an important research direction to be explored at present. Based on the Qwen2.5 open-source large model， we utilize industrial system knowledge to construct a component library and propose a large language model for design of complex industrial system （DCI-LLM）. Through the proposed low-rank adaptation （LoRA）-freeze-based “local-collaborative” fine-tuning method which combines local module and global knowledge of the system， DCI-LLM automatically generates system composition schemes and produces the related engineering drawings given system design requirements. We use the scheme design of heating systems as an example to verify the effectiveness of the proposed framework. Experimental results show that the fine-tuned DCI-LLM model achieves accuracy rates of 93.4% and 89.3% in answering questions about module knowledge and global knowledge， respectively. Moreover， scores from professional engineers indicate that DCI-LLM has practical application potential in scheme design of complex modular systems. Our work demonstrates that LLMs have significant application prospects in the field of automatic scheme design for complex industrial systems.
This study investigates a high-loaded axial compressor in which flow instabilities in the rotor and the stator occur almost concurrently.Under these conditions,conventional stability enhancement methods prove to be ineffective.The paper proposes a combined rotor-stator flow control technique.This study reveals that the flow field deterioration stems from combined flow blockage at the rotor tip region and the near-hub region of the stator.Research on flow control methods finds that self-recirculating casing treatment can effectively improve flow capacity in the rotor tip region,but simultaneously reduce flow capacity in the near-hub zone.This makes the hub flow field more susceptible to breakdown and ultimately triggers compressor instability.Thus,the self-recirculating casing treatment fails to enhance stall margin.By contrast,hub suction significantly improves the hub-region flow field.Yet without suppressing the rotor-tip flow blockage,it achieves limited stability enhancement.The integrated solution combining self-recirculating casing treatment with hub suction simultaneously addresses flow blockage at both the rotor tip and the stator near-hub regions.This combined flow control method delivers effective stability enhancement,achieving 6.78%increase in compressor stall margin.
Precise detection of flight path deviations during the approach phase is critical for identifying operational risks and enhancing aviation safety.However,existing monitoring methods often face significant challenges in distinguishing between environmental signal distortions and operational handling errors due to the lack of high-fidelity physical reference models.To address these limitations,this study establishes an integrated simulation framework for required navigation performance(RNP)and instrument landing system(ILS)approaches by synthesizing quick access recorder(QAR)data with rigorous navigation modeling.A site-specific"digital twin"of Linfen Yaodu Airport is constructed,incorporating 3D trajectory reconstruction based on Mercator projection and Baro-VNAV logic,alongside an electromagnetic simulation of ILS signals utilizing antenna array and image theory to model multipath effects.Empirical validation demonstrates that the model accurately reproduces critical signal characteristics,including interference fringes and secondary glide slope lobes.Furthermore,quantitative regression analysis establishes a definitive linear correlation(R2>0.98)between the theoretical difference in depth of modulation(DDM)and the pilot-observed distance off track(DOT),providing a verified calibration equation for interpreting cockpit indications.The practical utility of the framework is substantiated through the investigation of a specific vertical deviation anomaly,where the model successfully traces the causal chain from signal distortion to unsafe landing parameters such as threshold height exceedance and flare compression.This research offers a robust,data-driven methodology for continuous flight quality monitoring and provides a scientific foundation for identifying the root causes of navigation incidents.
Composite materials have been widely applied to aircraft structures due to their significant mechanical performance and lightweight. However， the shape difference， which induced by curing deformation， between the actual and theoretical composite panels leads to poor assembly accuracy. This paper proposes a shape control method with flexible tooling based on the finite element analysis and the adaptive genetic algorithm. The optimized displacement of every distributed shape adjusting end can be calculated to meet the requirements for shape conformity and structural healthy of the composite panel. The feasibility of the method has been verified via the shape optimization of aircraft wing-body fairing composite panel. Experimental results indicate that the shape conformity （within 0.3 mm） increases from 66.46% to 86.36% with the proposed optimized method. Moreover， the accuracy and efficiency for the composite panel assembly have been significantly improved. This study provides a new efficient and accuracy approach for the shape adjustment of composite panels during the assembly process.
To enhance the overall performance of multiple aerial manipulators under complex lumped disturbances， a nonsingular terminal sliding mode （NTSM） controller based on time-delay estimation （TDE） and deviation coupling control （DCC） is proposed. The stability of the controller is proven using the Lyapunov stability theory. Comparative experiments are conducted using a system of multiple aerial manipulators. The results demonstrate that， compared with a PID controller based on TDE， the proposed controller reduces the integral of absolute error （IAE）， integral of time-weighted absolute error （ITAE）， and integral of squared error （ISE） by at least 45.8%， 44.1%， and 66.5% respectively， thereby achieving superior overall control performance.
Vertical landing and recovery of a launch vehicle at sea can further reduce the launch cost of commercial space-flight， so this paper investigates the issue of reusable launch vehicle landing at sea. A vehicle landing dynamics model considering mechanism flexibility is established， and vehicle drop tests under various operating conditions are conducted. By comparing the vehicle drop test， the simulations of the dynamics model are consistent with test results， and the vehicle landing dynamics model considering the landing mechanism flexibility can simulate the vertical landing process of the vehicle more effectively than the rigid body dynamics model， which is more meaningful to guide the design of the reusable vehicle landing mechanism. Based on the vehicle landing dynamics model， a deck motion model and wind disturbance model are added to simulate the vehicle vertical landing dynamics at sea. The simulation results show that the sea landing causes the peak acceleration response of the vehicle， the main strut load and the buffer compression stroke to become larger， and there is a significant decrease in the landing stability of the vehicle.
Ice accretion on transmission lines poses a severe threat to the safety of power grids. To achieve early icing warning and online ice thickness measurement， it is necessary to consider the effect of the rough ice layer on ultrasonic echo signals during the early icing stage. This study applied a sinusoidal equivalent roughness model to simplify the ice layer profile， and used PZFlex to numerically analyze the effects of the rough structure scale on the ultrasonic pulse-echo signals. The reflection coefficient of the ice-air layer interface was introduced to characterize small-scale roughness. It is found that the attenuation of pulse-echo signals is dominant by the height of rough structure. As the roughness upper ice layer increases， the interface reflection coefficient gradually decreases. When the roughness exceeds the critical value， the reflection coefficient shows independent with roughness. As the threshold of reflection coefficient is set as 0.25， the effective measurement range of roughness under different center frequencies of excitation signal sources is determined. The relationship between the reflection coefficient and roughness upper ice layer is established， enabling the quantitative identification of rough ice layer using the ultrasonic pulse-echo technique. The research can provide a technical basis for the accurate measurement of ice thickness above transmission lines.
A tripartite evolutionary game model of enterprise,air traffic control(ATC)and passengers in an air-rail intermodal transport(ARIT)system was developed and investigated.The optimal interaction among enterprise,ATC and passengers was explored based on the congestion charging mechanism,as presented in terms of the payoffs and decision-making behaviors of three participants.Payoff matrices were established for three game players,wherein fare,mileage cost,en-route charge and generalized travel cost were taken into consideration.After that,the replicated dynamic equations were derived and employed to analyze the reliability of the proposed model and the dynamic behaviors of each game player under initial conditions.Eventually,the Beijing-Shanghai,Beijing-Guangzhou and Beijing-Kunming corridors were used as practical cases to clarify the impact of key factors(e.g.,distance,en-route charge and passenger sharing ratio)on the evolutionary trend and final strategy.The results showed that three players tend to choose the strategy which is always profitable.The enterprises would choose to introduce the ARIT strategy in medium-distance route,but not in short-and long-distance route,ATC chose to implement the congestion charging strategy,and passengers preferred the ARIT strategy.In addition,the final strategies were affected by any changes in key factors,and enterprises were more sensitive and likely to introduce the ARIT strategy out of individual interest.
