To address the limitations of traditional ICP (Iterative closest point) algorithms in low-overlap scenarios and under noisy interference, this paper proposes an improved ICP point cloud registration method based on an errorguided threshold adjustment mechanism. The approach aims to enhance the accuracy and robustness of point cloud registration. During the coarse registration stage, fast point feature histograms (FPFH) are combined with the random sample consensus (RANSAC) algorithm. By employing random sampling and introducing a triangle similarity constraint, distinctive corresponding point pairs are selected to estimate the initial pose transformation between point clouds. In the fine registration stage, an error-guided threshold adjustment mechanism dynamically updates the distance threshold based on the matching error in each iteration. This ensures that each point in the source point cloud is matched only to its nearest point within the adaptive threshold in the target point cloud, thereby effectively filtering out invalid correspondences. The proposed method is validated on multiple public point cloud datasets, including models with complex geometric structures and large-scale scenes. Experimental results demonstate that the method significantly improves registration accuracy and maintains robust performance even under challenging conditions such as low overlap and high noise levels.

Reusable launch vehicles are critical assets for achieving round-trip space transportation in the world. As a key subsystem, the thermal protection system serves the main function of protecting vehicle from burning and overheating in the aerodynamic heating environment during atmospheric re-entry. Its health condition is directly related to the reliability and cost-effectiveness of the flight mission. In response to failures that thermal protection systems are prone to during flight, this article first analyzes typical damage modes of thermal protection systems in space shuttles and Starships, such as bolt loosening, structural debonding, and damage caused by impacts from micrometeoroids or space debris. Then, it provides a detailed overview of structural health monitoring technologies related to thermal protection systems, including the thermal protection system temperature monitoring, connection bolt loose monitoring, structural debonding monitoring, structural impact monitoring and implementation methods of other key technologies and their practicality. Finally, the future development trends and prospects of structural health monitoring technology for reusable launch vehicle thermal protection system are  iscussed.

In advanced engineering applications such as aerospace, petrochemical, and rail transportation, bolted joints often operate under complex service conditions involving high temperatures, high pressures, and multi-source coupled loads. Piezoresistive sensors exhibit strong potential for engineering applications owing to their mature fabrication process, low cost, and sensitive response characteristics. In this study, a piezoresistive sensor featuring high-temperature resistance, high integration, and a wide response range was developed. A conductive solution was prepared by mechanically stirring and ultrasonically dispersing carbon black (CB), aluminum oxide (Al₂O₃), and polyamic acid (PAA) solution. The sensor, with a thickness of 100 μm, was fabricated using blade coating followed by thermal imidization, and subsequently integrated with a flexible printed circuit (FPC). Experimental results show that the sensor can operate stably at 300 ℃ for extended periods. In monitoring tests of M20 large-size bolted joints looseness, the sensor demonstrated a torque measurement range of up to 100 N·m and a pressure range of 34.30 MPa. The experimental results verify its stable monitoring performance under high-temperature and high-load conditions, offering a robust technical solution for the identification of loosening states in large-scale bolted joint structures of critical engineering equipment and demonstrating significant potential for practical engineering applications.

To address the challenge of structural shape sensing in aircraft under constraints of weight and power consumption, a sensor placement optimization method for shape sensing based on the effective independence (EFI) is proposed. This method evaluates the information content of all candidate sensor positions using the Fisher information matrix and iteratively removes the measurement points that contribute the least to preserving the independence of the target mode shapes. The goal is to retain the maximum modal information with the minimum number of sensors. The effectiveness of the proposed method under complex loads and noise conditions is verified using a numerical simulation model of a wing box segment. Finally, a test platform is built for a lattice sandwich panel with an airfoil shape, and displacement reconstruction error remains below 10% under the optimized strain sensor placement, demonstrating the method’s effectiveness under practical working conditions.

Resin matrix composites are widely utilized in a variety of typical load-bearing and non-load-bearing constructions in fields such as aeronautics and aerospace. With structural and functional integration, traditional off-line monitoring approaches can no longer match composites’ sophisticated expectations. With the ongoing development of structural health monitoring (SHM) of composite materials, flexible sensors primarily composed of conductive particles such as carbon nanotubes, graphene, and MXene have opened up new possibilities for early damage detection, early warning of service status, and intelligent diagnosis of composite materials. This paper primarily undertakes a systematic evaluation of the structural health monitoring technology of flexible sensors, focusing on introducing the design ideas, essential technologies, mechanism mechanisms, and application advancements of flexible sensors, and looking ahead to their future applications.

Aiming at the critical issue of interlayer debonding damage susceptibility in reusable launch vehicle thermal protection structures under complex multi-physics coupling environments, a non-destructive testing method integrating ultrasonic guided waves with domain-adaptive transfer learning was proposed. Four typical bonding types were designed in thermal protection tile specimens, enabling efficient full-coverage inspection of bonded areas through a bidirectional orthogonal scanning strategy coupled with an ultrasonic excitation-reception mechanism. To solve the problem of signal drift caused by individual differences of specimens, an adaptive phase alignment method based on peak proportion threshold is proposed, and an appropriate window length is selected to realize the retention of key features of debonding damage while suppressing the interference of redundant data. A Domain-Adaptive Transfer Learning (DATL) was further proposed to align cross-specimen damage feature distributions. Experimental results demonstrate that in cross-specimen testing scenarios, the DATL model exhibits only a 3.9% accuracy decline, with inter-domain distribution discrepancy reduced from 0.31 to 0.10. With target domain data below 40%, DATL achieves 85% accuracy, outperforming CNN by 19.4%. The methodology mitigates reliance on damage patterns and specimen consistency, effectively reducing false alarms and missed detections in debonding testing for in-service thermal protection systems, which provides a practical solution for rapid non-destructive evaluation and structural health monitoring of reusable launch vehicle.

Direct ink writing (DIW) 3D-printed sensors exhibit significant potential in aerospace structural health monitoring due to their design flexibility and high manufacturing precision. This paper reviews recent advances in the design, fabrication, and application of DIW sensors. First, we systematically summarize key breakthroughs in sensor design, including theoretical approaches for multilayer composite structures, optimized layouts for sensor arrays, integrated designs for embedded sensors, and decoupling strategies for multifunctional sensors. Regarding fabrication, we highlight the importance of process parameter optimization for controllable high-precision manufacturing, as well as the development of multi-axis printing systems for conformal deposition on complex curved surfaces. In terms of applications, successful implementations are demonstrated in aerospace structural monitoring, building vibration detection, human motion capture, and closed-loop control of soft robotics. Finally, we discuss current challenges and propose future research directions. Owing to its exceptional material compatibility and conformal printing capabilities, DIW technology is poised to become a critical tool for enhancing flight safety and extending equipment service life.

Accurate crack diagnosis in multi-fastener metallic structures is critical for instructing aircraft structural ground tests and ensuring in-service safety. However, heteroscedastic uncertainties in the relationship between crack length and guided-wave damage indices severely compromise diagnostic accuracy and reliability assessment. To address this, a multi-fastener-joint crack diagnosis method based on Quantile Regression Neural Network (QRNN) is proposed. The QRNN establishes a mapping model between guided-wave damage index and the crack length, where crack diagnosis result is determined through the median quantile point. Furthermore, by comprehensively leveraging the quantile outputs, the diagnostic reliability across different crack lengths is quantitatively characterized. A complex multi-layer stringer structure with multiple fastener joints was adopted as the testbed to validate the diagnostic capability and reliability assessment. Experimental results indicate that the proposed approach enables precise crack diagnosis in characteristic longeron fastenerjoint areas, exhibiting Root Mean Squared Error (RMSE) 1.2 mm in the skin and RMSE of 2.2 mm in the stringer, with concurrent quantification of diagnostic reliability.

The electrothermal properties of meshed carbon nanotube film (CNT–film) were investigated by means of simulation and experimental verification. Firstly, the influence of different mesh shape, orientation, mesh spacing and fillet radius on the temperature distribution and uniformity of electric heating element was studied by finite element analysis. The results demonstrate that the temperature distribution uniformity of CNT–film heating element is the best when the parameters of square grid, vertical current direction, mesh spacing d of 5 mm and fillet radius r of 1 mm were adopted. Then, on the basis of simulation optimization, experiments are carried out to verify the reliability of simulation results. The experimental results are consistent with the simulation results, indicating that the meshing scheme can significantly improve temperature uniformity and resistance value of CNT–film electric heating elements, avoiding the problems of uneven temperature distribution and high local temperature caused by uneven surface electrical properties of traditional CNT–film electric heating elements. The optimization scheme not only expands the potential use of CNT–film in surface heating area, but also provides a new idea and method for designing heating elements with local opened pore.

In view of the difficulty of rapid and accurate test of bonding interface strength in the diffusion bonding process of sheet components, a test scheme for the normal bonding strength of diffusion bonding interface for sheet is proposed in this study. Specimens with different diffusion-bonding qualities are obtained through single-factor experiment of diffusion bonding of TC4 alloy. The test results show that interface bonding strength of TC4 alloy is positively correlated
with the bonding rate. The fracture analysis demonstrates that with the increase of bonding rate, the fracture surface transforms from flat to conical and from axial dimple to shear dimple, which proves the equivalence between the proposed method and metallographic evaluation. The proposed method complements the evaluation system of diffusion bonding quality and can be used to directly characterize the mechanical properties of the wide-range diffusion bonding interfaces.

To investigate the effect of shot peening on surface or near-surface porosity defects of additively manufactured components, additively manufactured TC4 specimens were prepared by selective laser melting, and surfaces of the specimen were strengthened by shot peening, internal defects of the specimens were characterized before and after shot peening using computed tomography technology, three-dimensional visual reconstruction and analysis of the porosity defects were carried out, along with testing and analyzing microstructures of the specimens before and after shot peening. Microstructure, microhardness, and tensile properties of the specimens were tested and analyzed before and after strengthening. The results demonstrate that the porosity of the strengthened specimen was reduced by 0.32% compared with that of before, and the volume and spatial geometry of large pores were effectively altered. Post shot peening, the crosssectional hardness was increased by 23.38% and tensile strength was enhanced by 60.15%, indicating that shot peening can refine the grain structure and improve surface integrity and overall mechanical properties of the specimens.

Riveting is an important connection method for carbon fiber reinforced polymer (CFRP) composites, however, traditional riveting is highly susceptible to causing excessive interference and severe damage to CFRP joints, leading to potential quality hazards. This study experimentally investigated the damage behavior of CFRP specimens subjected to various riveting methods, including straight riveting, washer riveting, chuck bushing riveting, and washer-chuck bushing riveting. The results indicate that the average interference of straight riveted joints is the highest, substantially greater than 2%, with joints exhibiting the most severe cross-sectional damage. In contrast, joints riveted with washers show an average interference of 0.757% with virtually no damage to the joints’ cross-section. Joints riveted with chuck bushings are of an average interference of 0.956%, but the tilt of rivet head causes compressive deformation and other damages around the hole. The combination of washer and chuck bushing riveting results in an average interference of –0.915%, without forming effective interference and observing no tangible damage on the joints’ cross-section.

TC4 titanium alloy is the main material of aircraft landing frame and other structural parts. The study of its ultra-low cycle fatigue properties is essential for evaluating the reusability of aircraft structures that experience substantial loads. In this paper, the ultra-low cycle fatigue tests on TC4 titanium alloy at room temperature are conducted utilizing the axial strain control method and the fatigue fracture mechanism is analyzed. The alloy exhibits continuous cyclic softening behavior under significant heavy fatigue loading. The ultra-low cycle fatigue properties of TC4 titanium alloy are characterized respectively by the Coffin–Manson formula, the strain energy density-based model and the powerexponent function model, with the latter presenting better properties and precisions for fatigue life prediction under different strain ratios. Under different strain amplitudes, secondary cracks and holes appear in the materials, and as the strain amplitude increases, the fracture mode transitions from the normal fracture to the shear fracture.

Aiming at the problems of uneven distribution of sampling points, redundant sampling points, complex path map construction and overmuch path folds when using traditional probabilistic roadmap (PRM) algorithm for aircraft wing box assembly robots, a path planning method based on improved PRM algorithm is proposed. Firstly, the Halton sequence is adopted to optimize the sampling strategy to ensure the uniform distribution of sampling points in the configuration space, so as to improve the sampling quality. Secondly, an optimization strategy for redundant points in elliptic region based on the control points is designed, and the locality sensitive hashing (LSH) function is introduced to reduce redundant sampling points in the configuration space and to optimize the construction and searching efficiency of the probabilistic roadmap. Finally, a B–spline curve is used for smoothing the planning path to meet the actual motion constraints of the wing box assembly robot. The simulation experimental results in 2D and 3D demonstrate that the improved PRM algorithm reduces the planning time by 41.1% on average in 2D space and by 68.43% on average in the high-dimensional configuration space of the robotic arm, compared with the traditional PRM algorithm. Meanwhile, the generated paths are more optimized, which significantly improves working efficiency of the wing box assembly robot.

The tool wear condition in micro-milling significantly affects the geometry and surface quality of critical parts in miniature components, which are key factors for product quality and performance stability. However, due to the small size of the tools, real-time monitoring of tool wear is challenging in actual machining processes and severely impacts machining efficiency. This paper proposes a tool wear prediction method based on fractal features of machined surface images. The method primarily utilizes multifractal analysis to extract various texture features from machined surface images and constructs a dataset with the actual tool wear values obtained from an image acquisition system. Then, the tool wear is predicted using a tool wear evaluation technique that combines feature selection algorithms and support vector regression (ReliefF–SVR). The results show that the proposed method exhibits strong robustness under various cutting conditions and can accurately predict tool flank wear in the micro-milling process, with an average prediction accuracy of over 93.6%. This study presents a feasible and reliable solution for the actual quality control of micro-precision parts.

The bolt-connected structure of lug, widely used in aviation industry, is prone to fracture under frequent loading, making the analysis of its tensile and fatigue performance crucial. In this paper, the tensile test parameters of lugconnected structure were designed based on theoretical calculations, and the low-cycle fatigue test was designed by the tensile displacement–load curve. The static-load failure and low-cycle fatigue failure of 7050–T7451 aluminum alloy lugconnected structure were investigated using experiments and finite element simulations. The results demonstrated that the double lug structure completely fractured after 7686 cycles, and the crack propagation a–N curve was close to that of the fatigue life simulation results. The results provide a reliable theoretical basis for evaluating the safety of lug-connected structure and selection of lug-connected part.

In order to accurately reflect the compression characteristics of the integral pierced carbon fiber felt, a compaction model for the pierced carbon fiber felt was constructed, employing finite element methods to predict the nonlinear mapping relationship between the compression load and compaction height. Initially, considering the randomness of the internal fiber distribution in the carbon fiber felt, a parametric modeling method for the microstructure of the carbon fiber felt was proposed, defining parameters such as fiber orientation, azimuth angle, and length. On the ABAQUS platform, a microscale geometric model of the carbon fiber felt was constructed using the Python programming language. Subsequently, the compaction process of the pierced carbon fiber felt was simulated using the Abaqus/Explicit algorithm, analyzing the structural changes of the preform at different stages of the compaction process. Finally, the relationship between the compaction height and the compression load of the preform was obtained through compaction experiments. The experimental results indicate that the structural morphology variations of the preform and the compression load–displacement curve relationship predicted by the numerical simulation are in good agreement with the experimental results, with the maximum error between the two being less than 5.5%, thereby validating the correctness of the compaction model of the pierced carbon fiber felt.