Smart morphing structures represent a key technology for future advanced equipment such as unmanned aerial vehicles, where distributed active deformation structures enable smooth, continuous, and multi-degree-of-freedom shape changes, serving as an effective mean to significantly enhance structural performance and mission adaptability. Addressing this need, this study proposes an innovative design and control scheme for shape memory alloy (SMA)-driven smart lattice structures. Firstly, the proposed pseudo-thermal deformation method (PTDM) provides an efficient approach to evaluate deformation performance of SMA actuators. Simulation and experimental verification demonstrate that this method achieves deformation analysis accuracy within 5% error for smart lattice structures. It successfully realizes multi-degreeof-freedom controllable shape morphing. Furthermore, an optimized distributed actuation design model was developed with energy consumption minimization as the objective function and deformation precision as constraint condition. In a wing structure application, this model achieved high-precision 400 mm deformation at 8 control points with less than 1% error while consuming only 16.67% of the global energy. To address real-time control challenges in large-scale structures, a BP neural network was employed to achieve precise prediction and control of multiple degrees of freedom deformation. The proposed method exhibits remarkable versatility, being extendable to various SMA actuation configurations and smart structural designs like composite wings, providing a new solution for next-generation smart morphing structural systems that integrate both mechanical performance and smart deformation capabilities.

Morphing aircraft, overcoming the limitations of traditional fixed configurations, represents a critical direction in aircraft development. Their core principle lies in adjusting the airframe configuration to achieve optimal aerodynamic profiles for diverse operational environments, thereby significantly enhancing flight performance. After years of development, morphing aircraft exhibits the following characteristics: (1) Evolution from rigid deformation to flexible continuous deformation; (2) Progression from low-speed to high-speed applications; (3) Transition from single-medium to cross-medium flight; (4) Expansion of operational domains from singular airspace to integrated airspace domains. Deformable structures constitute a key enabling technology for morphing aircraft, primarily encompassing skins, supporting structures, and actuation systems. Skin technology has evolved from rigid to flexible skins. Moving beyond the initially employed metal skins, contemporary developments include composite material skins, smart skins based on shape memory polymers and piezoelectric responsive materials, multi-medium adaptive reconfigurable skins, and thermal protectiondeformable integrated skins. This flexible skin system not only grants adaptive surface deformation capabilities but also maintains the continuity of the airframe surface. Structural design is shifting from hinge-based mechanisms towards continuous deformation structures. Hinge-based wing morphing systems enable large-angle adjustments of aerodynamic surfaces. In contrast, continuous deformation structures not only possess substantial deformation capacity but also achieve precise aerodynamic profile matching while preserving airframe continuity. This capability, coupled with space deployment mechanisms, enables multi-medium transition capabilities, such as switching between underwater navigation and aerial flight. Actuation systems for morphing aircraft are transitioning from traditional motor-hydraulic actuators towards smart actuation, realized through the integration of active deformation units like dielectric elastomers, piezoelectric stack actuators, and shape memory alloys. This paper reviews the morphing configurations and research progress on deformable structures, focusing on their applications in unmanned aerial vehicles (UAVs), cross-medium vehicles, civil airliners, and aerospace vehicles. Building on this foundation, it outlines future development directions and primary challenges, aiming to provide a reference for innovative research in morphing aircraft technology.

This paper proposes a novel preparation process utilizing composite materials for the flexible bionic wings of hovering flapping wing micro air vehicles (FWMAVs), addressing issues such as excessive mass and inertia, low flapping efficiency, and limited service life. The process employs lightweight, tear-resistant Cuben fabric as the wing membrane, combined with carbon fiber prepreg curing to form the wing veins. It also incorporates laser cutting, selfbonding technology, and a standardized thermal curing process, significantly enhancing the performance of the bionic wings. Experimental results demonstrate that, compared to traditional processes, the bionic wing prepared using this process reduces mass by 53.6%, increases flapping efficiency by 1–2 gf/W (approximately 40%), and exhibits no significant damage during high-frequency, long-duration flapping, thereby substantially extending service life. Meanwhile, comparative experiments reveal no significant difference in aerodynamic performance between flexible bionic wings with simplified wing veins and those with insect-like veins, offering a new perspective for the optimal design of bionic wings. Furthermore, flight experiments validate the practicality of the proposed bionic wing fabrication process, laying a foundation for performance enhancement and practical application of hovering FWMAVs.

To improve the aerodynamic performance of civil fixed-wing carrier-based UAV, this study presents a variable trailing-edge camber wing design scheme based on a multi-segment drive mechanism. Using the k – ω turbulence model for viscous fluids, comparative aerodynamic analyses across angles of attack are conducted for traditional singlesegment wing structures and 2–5-segment deformable wing structures. Through quantitative evaluation of key parameters such as lift coefficient and lift-to-drag ratio, the 3-segment deformable wing is identified as the optimal aerodynamic configuration. Structural optimization and simulation verification of the wing are carried out based on this scheme. The results show that the kinematic model established for the 3-segment structure achieves precise control of the target deformation angle of 30° through simulation verification, with excellent collaborative motion of each rotating rib and a continuously smooth wing profile without jamming. Finite element strength analysis indicates that the maximum Von-Mises stress in the structure is 287.12 MPa, with a safety margin of 3.81 (based on the yield strength of 1093 MPa for 15–5PH stainless steel), meeting the strength requirements under designed loads. The proposed multi-segment variable trailingedge camber wing design method provides a complete parameter matching and performance verification system for the engineering implementation of high-lift wings in carrier-based UAVs.

Lattice materials are a class of engineered materials with artificially designable microstructures, with excellent multi-physics field manipulation capabilities in mechanics, acoustics, and thermodynamics, and exhibit broad application prospects in the field of smart structures. Although the development of additive manufacturing technology has significantly enhanced the shape complexity of lattice materials, its limitations in build size and manufacturing efficiency still impede the integrated fabrication of large-scale complex structures, which has become a key technical bottleneck for their engineering application. To address this, this paper proposes a prefabricated assembly construction method using standard unit cells, which enables configuration flexibility and manufacturing feasibility for large-scale lattice materials in complex structures via modular construction and spatial assembly. In terms of structural configuration, octahedral lattice unit cells serve as the basic components, with two connection strategies developed: Bolt joints for flexible material systems and bionic plug-in joints for rigid ones, enabling modular assembly for diverse application requirements. For flexible structures, linear actuators are integrated to construct an actively deformable wing structure, which achieves local deformation control with a maximum thickness adjustment range of 25 mm and a maximum surface inclination angle of 12°. For rigid structures, a lattice sandwich cockpit prototype is constructed, and a homogenized finite element model is established for static mechanical analysis, resulting in a bending stiffness of 2564.1 N/mm and a torsional stiffness of 1409 N·m/deg. The research results indicate that this assembled lattice structural system, while maintaining lightweight properties and high performance, possesses good assembly flexibility and cross-scale adaptability, thereby offering an effective configuration and manufacturing solution for the engineering application of smart structures and lightweight aircraft components.

To address the urgent demand for high-reliability, high-elasticity flexible components in aerospace applications under extreme temperatures and complex load coupling, this study developed Ni₅₁Ti₄₇Nb₂ alloy wires using a vacuum melting-cold drawing-annealing synergistic control process. Further, high-elastic flexible hinges tailored for aerospace applications were successfully fabricated via mold-based thermal shaping. Experimental results demonstrate that alloy wires annealed at 400 ℃ for 10 min exhibit exceptional superelastic stress of 1616.5 MPa and a tensile superelastic strain of 6% across a wide temperature range from –120 ℃ to 120 ℃. Notably, the temperature dependence of superelastic stress (dσ/dT = 3.3 MPa/℃) is reduced by approximately 50% compared to traditional NiTi alloys. The flexible hinges, subjected to 70% tensile strain cyclic testing for 10 cycles at –120 ℃, retained a shape recovery rate of 96% without degradation in mechanical performance. These hinges have been successfully deployed in the elastic deployment mechanisms of deep space exploration probes, overcoming the limitations of conventional metallic materials under extreme temperature and complex load coupling conditions. This achievement marks a significant advancement in superelastic performance, offering an innovative solution for lightweight design and enhanced reliability in aerospace systems.

Lattice structures, characterized by lightweight properties, high specific strength and specific stiffness, and excellent vibration-damping and energy-absorbing efficiency, have been widely applied in the design of critical loadbearing components for high-end equipment in aerospace and deep-sea fields. However, for traditional lattice structures, the unit cells can only be described by a single parameter at the microscopic scale, and their distribution is confined to the assumption of uniformity at the macroscopic scale. This leads to underutilization of the design space, which restricts the enhancement of mechanical properties and fails to meet the stringent requirements for the extreme lightweight design of critical load-bearing components. In this paper, a multi-scale topology optimization design method for multi-parameter lattice structures is proposed. At the microscopic scale, the topological configuration design of multi-parameter lattice unit cells is achieved using an approximate model-assisted particle swarm optimization (PSO) algorithm. At the macroscopic scale, the topological distribution of lattice unit cells is optimized via a parametric level set-based topology optimization method. The proposed method realizes the coupling of microscopic lattice material design and macroscopic structural optimization, maximizing material potential and enhancing the mechanical properties of lattice structures. Numerical examples show that compared with traditional single-parameter lattice structures, the mechanical properties of the structures optimized using the proposed method are enhanced by 53.42%. Compared with lattice structures optimized via single-scale optimization (either microscopic or macroscopic), the performance is improved by 48.07% and 12.69%, respectively. This indicates that lattice structures designed with multi-scale optimization exhibit significantly superior load-bearing capacity. The proposed method significantly expands the design space of lattice structures and effectively enhances their mechanical properties, thus holding significant application potential for the lightweight design of structural components in key fields like aerospace.

The development of electron beam welding technology is advancing toward higher voltages, greater power, extended lifespan, improved stability, and intelligent control, positioning it as an ideal welding technique for thick titanium alloy structures. In this study, a high-voltage electron beam was employed to weld 20 mm thick TA15 titanium alloy, systematically analyzing the effects of 100 kV, 120 kV, and 150 kV accelerating voltages on the microstructure and mechanical properties of the weld joints under consistent heat input. Additionally, the thermal action mechanisms of accelerating voltages on the welding process were also investigated. The results show that accelerating voltage significantly impacts local undercooling within the weld joint, and as the accelerating voltage increases, the undercooling degree decreases, thereby promoting the diffusion and transformation of alloying elements in TA15 titanium alloy, resulting in the formation of fine needle-like α′ phase and a small amount of lamellar α phase. At 150 kV, the room-temperature ductility of the TA15 titanium alloy was notably improved due to the increased presence of the β-phase and high-angle grain boundaries in the weld, which in turn enhances the alloy’s plastic deformation capacity. These findings offer crucial theoretical insights for the engineering application of high-voltage, high-power electron beam welding in the fabrication of thick and tough titanium alloys.

Laser weapons are essential means for anti-missile and anti-drone operations in modern warfare. To fairly assess the system contribution rates of various equipment in a laser weapon coalition, a fuzzy coefficient matrix is established based on damage impact factors, with full consideration of the damage effects of laser weapons during target engagement. Furthermore, by treating laser weapons as participants in cooperative games, the fuzzy Shapley value model is employed, and corresponding calculation formulas are derived to quantify the contribution share of each participant in combat missions. Finally, the system contribution rates of various laser weapons in typical laser weapon combat scenarios are calculated and obtained, which provides a reference basis for laser weapon employment strategies.

Accurate contact force perception is key to achieving active compliance assembly of aviation interference fit shaft-hole components using the temperature difference method. However, non-contact forces (e.g., gravity and inertial forces) induced by the load can interfere with force perception, thereby affecting the accuracy and stability of the assembly process. To address this issue, a contact force perception method based on the joint force sensors of a parallel posture adjustment mechanism is proposed. First, a self-calibration strategy is introduced to calibrate the mounting tilt angle of the posture adjustment mechanism base. Then, the method for calculating the contact force between components during active compliance assembly is described, and a load dynamics parameter identification model is established based on the principle of force translation and rigid body rotation. Next, the reliability of the parameter identification and contact force perception algorithms is verified through cosimulations using Adams and Simulink. Finally, experiments to verify the contact force perception accuracy are conducted on an active compliance assembly platform built in the laboratory. The results show that the proposed method can accurately identify the load dynamics parameters, achieve precise non-contact force compensation, and significantly reduce force perception errors.

To address the issue of shape deformation of aircraft skin during the forming, clamping, and machining processes, which compromises CNC milling accuracy, a method for in-process measurement of aircraft skin surfaces based on machining features is proposed. This method ensures the machining accuracy of the skin by acquiring the contour of the surface to be machined. First, a surface fitting method suitable for machining features is introduced, based on the curvature distribution characteristics of aircraft skin. A curvature-driven surface fitting error calculation method is proposed to quantify the fitting errors. Second, for the measurement point planning of machining features, a mesh division method based on boundary equal division is presented. Then, an optimization strategy for adaptive measurement point density adjustment is proposed based on surface fitting errors. Finally, the proposed method is applied to measurement planning and machining verification for the machining features of a large aircraft cabin skin. Experimental results show that the proposed method can reasonably distribute the measurement points on the machining feature surface, accurately acquire the feature surface contour, and ensure that the machining accuracy of the skin meets the tolerance design requirements.

High-precision and high-efficiency on-machine measurement is the premise for evaluating machining accuracy and ensuring machining quality of aerospace large thin-wall parts with large size, thin wall and weak rigidity. Multi-sensor data fusion is an important means to achieve high-precision and high-efficiency measurement of large thin-walled parts, however, the existing multi-sensor data fusion measurement methods rely on the explicit function reconstruction of curved surface, which is susceptible to the uncertainty of the measurement data and make it difficult to ensure the stability of the fusion results. A weighted residual fuzzy learning (WRFL)-based multi-sensor fusion measurement method for large thin-walled parts is proposed in this paper, in which, the residuals between different sensor measurement data are characterized by partition to obtain fuzzy-weighted fusion. Firstly, the low-precision point cloud is clustered and partitioned based on the high-precision data by probe measurement. Then the discrete residuals of lowprecision point cloud data in each partition are solved, and the residual sets are obtained by weighting the residuals in the cluster boundary region. The fuzzy set is finally established based on the discrete residuals to construct the high-precision fusion point cloud and realize the surface reconstruction. The experimental results demonstrate that the proposed method can significantly improve the surface measurement accuracy compared with the existing fusion measurement, and provides technical support for high-precision and high-efficiency measurement of large thin-walled parts.

Industrial CT linear array scanning is an important method for acquiring the internal characteristic structures of aero-engine turbine blades, and extracting the contours of reconstructed tomographic grayscale images is a key step for measuring dimensions such as blade wall thickness. Since the commonly used pixel-level unsupervised evaluation methods suffer from blurred extracted edges and low dimensional measurement accuracy, this paper proposes a subpixellevel contour extraction algorithm based on intelligent parameter optimization for CAD model matching. Firstly, the local binary fitting (LBF) geometric active contour model is employed to extract edges; Secondly, the corresponding crosssectional point cloud is acquired from the CAD model; Thirdly, the coordinates of the two are unified using the oriented bounding box (OBB) algorithm; And finally, the evaluation function is constructed based on the Hausdorff distance. Ultimately, four parameters in the LBF model are optimized via the dung beetle optimizer (DBO), thereby achieving optimal contour extraction. The results of CT tomography images of turbine blades show that the relative error is less than 1.6%, compared with traditional edge detection algorithms such as Canny, Ostu, and Zernike, the method proposed in this paper can significantly improve the measurement accuracy.

This study establishes an assembly model for composite/aluminum alloy multi-bolt joint structures and investigates the preload distribution patterns of the bolts under four tightening sequences: Sequential tightening, staggered tightening, tightening from the middle to the ends, and tightening from the ends to the middle. It focuses on three assembly conditions: No assembly gap, forced assembly with a gap, and gap compensation with shims. By combining experimental and finite element methods, the preload distribution patterns under the aforementioned tightening sequences were analyzed. The results show that in cases involving an assembly gap, the bolt preload variation increases as the assembly gap expands. Among the four tightening sequences, when adopting the sequence of tightening from the ends to the middle, the preload distribution exhibits better consistency, and the final preload of each bolt is closer to the target preload.