Synergistically double-sided friction stir welding (SDS–FSW) is a significant variant of friction stir welding (FSW). The problems of large deformation and time-consuming of traditional FSW are greatly suppressed by SDS–FSW. SDS–FSW has prompted considerable scientific and technological interests due to its advantages in welding largesized and thick plates or profiles. At present, the research and application of SDS–FSW are still in the initial development stage, and relevant research results are mainly limited to 6061 aluminum alloy. This paper provides an overview of this new technology, including its development history, technical characteristics, typical research results and application prospects. The aim is to provide a reference for the further development and engineering application of SDS–FSW.

Structural health monitoring is a crucial approach for ensuring the safety and integrity of composite material structures in aircraft. Distributed fiber optic sensors based on backscattered Rayleigh scattering provide data support for composite material damage monitoring by measuring high-density strain distributions. However, the mapping relationship between structural strain distribution characteristics and damage is complex, making it challenging to accurately determine the quantitative information of damage based solely on strain distribution. Additionally, the large volume of data from distributed fiber optic sensors makes manual analysis of strain data time-consuming and less accurate. To address this challenge, an intelligent damage identification method based on distributed fiber optic sensing data and the U-Net neural network is proposed. It aims to automate the precise identification of common delamination damage in composite materials. Initially, training and validation sets for the U-Net neural network are constructed through finite element simulations. Subsequently, cantilever loading tests of composite material plates with delamination damage are conducted, and structural strain distribution data are collected as a test set using distributed fiber optic sensors. The damage identification results demonstrate that the U-Net neural network can accurately quantify the position, size, and shape of delamination damage.

The use of fiber reinforced plastics (FRPs) is on the rise in aerospace and other high-tech fields, owing to their high specific strength/stiffness, design flexibility, and corrosion resistance. Structural health monitoring (SHM) is therefore crucial to ensure structural safety. Recently, machine learning has emerged as a promising approach for SHM of FRPs. Data-driven methods that employ machine learning algorithms have shown higher accuracy, robustness, and efficiency in SHM. After the brief introduction of the machine learning algorithms commonly used in SHM of FRPs, the applications of machine learning in SHM are reviewed mainly from the following aspects: Damage pattern recognition, damage localization, and damage degree recognition of FRPs. Finally, the future development trend of SHM based on machine learning in FRPs are discussed.

Structural health monitoring (SHM) technology is poised to play a pivotal role in the design, service, and maintenance of aircraft, enhancing structural efficiency and ensuring the safety and reliability of aircraft structures. This article begins by outlining the fundamental concepts and applicability of SHM, emphasizing its significance in the aerospace industry. Subsequently, it delves into the research landscape of SHM technology in typical aircraft structures, focusing on advanced techniques such as impact monitoring, ultrasonic guided wave damage detection, and strain monitoring using fiber optic sensors. The review encompasses an overview of international and domestic research progress, technological capabilities, and typical applications. Lastly, it highlights the challenging issues facing structural health monitoring in aircraft structures and provides insights into its promising future applications in the aerospace field.

Smart skin seamlessly integrates a heterogeneous multifunctional circuit system on the surface of the load-bearing structure of an aircraft, serving as an enabling technology for achieving the“ Fly-by-Feel” capability in future variant aircraft. The smart skin of the aircraft involves various functional modules, including multifunctional  distributed sensing systems, conformal antennas, frequency-selective surfaces, and anti/de-icing. This integration enhances the aircraft’s situational awareness and promotes the development of lightweight structures, and the technical key is the fabrication of heterogeneous multilayer circuits on the intricate 3D surfaces. In addressing the manufacturing challenges associated with the multifunctional circuits of smart skin, this paper provides a detailed analysis of the difficulties encountered in the manufacturing of different types of curved functional units. Additionally, it compares and elucidates manufacturing technologies applicable to diverse structural types, encompassing conformal printing, via-hole interconnection, curved surface attach, and thin-film fabrication. Finally, the paper summarizes and forecasts the challenges and potential solutions confronting the manufacturing technology for heterogeneous multilayer circuits in smart skin, offering valuable insights for the breakthrough of next-generation aircraft smart skin manufacturing processes.

Aiming at the situation of honeycomb sandwich structure impacted by external objects, this study proposes a method to detect and identify the impact damage with electrical tomography and deep learning, and provide precise information for structural integrity evaluation and decision-making. The sensing layer and corresponding circuits are first printed on the surface of honeycomb sandwich structure with carbon ink and silver ink through silk-screen printing technique. Then numerical simulation is performed by considering impact damage with different quantities, positions and sizes to obtain training data of conductivity change and boundary voltage change of the corresponding sensing layer. Deep learning is carried out by a residual neural network to establish the mapping relationship between conductivity change and boundary voltage change of the sensing layer. Finally, the boundary voltage data of the sensing layer is measured before and after impact, and tomographic image of the conductivity change caused by impact damage is reconstructed by a trained residual neural network, identifying the locations and sizes of the damage. Low velocity impact test for a honeycomb sandwich structure is conducted to demonstrate the feasibility and effectiveness of the proposed method.

Composite laminates are vulnerable to impact damage during service, which has adverse effects on the structure. The internal structural damage of composite materials caused by impact can shorten the service life of composite materials. Therefore, it is necessary for impact localization and damage monitoring of composite laminates. New carbon nanomaterials represented by carbon nanotubes (CNT) and MXene films have unique nanoscale structures and excellent physical properties. The MXene/CNT thin film sensor array is arranged in the monitoring range, and a localization algorithm suitable for the sensor array is proposed. The algorithm can accurately calculate and orientate the impact position of the monitoring area. At the same time, the damage of the workpiece can be judged according to the relative resistance change rate of the sensor. Finally, the results were compared and verified by ultrasonic C-scan equipment. The experimental results show that the sensor array response calculation matches the actual impact position, and the resistance change rate of the sensor is related to the damage severity.

ZrB₂–SiC ceramics were prepared using hot oscillatory pressing (HOP) and hot pressing (HP) processes, and the effect of oscillatory pressure on the densification and mechanical properties of ZrB₂–SiC ceramics was investigated. The results have shown that compared to using the HP process to fabricate ZrB₂–SiC ceramics, the densification rate of HOP process has been improved significantly, and the interface between ZrB₂ and SiC is well bonded; The hardness and fracture toughness of the ZS30 sample prepared by the HOP process reached 21.1 GPa and 7.3 MPa·m^1/2, respectively, which were significantly improved compared to the samples prepared by the HP process under the same sintering parameters. HOP has shown good development prospects in the preparation of high-performance ZrB₂–SiC ceramics.

Peening coverage has essential effects on the service performance of parts. In this paper, aluminum alloy 2024–T351 was subjected to pneumatic shot peening with different coverage of 88.3%, 100%, 200% and 400% by adjusting feeding speed. The surface topography and microstructure, roughness, residual stress and microhardness were investigated using a scanning electron microscope, roughness tester, residual stress instrument and Vickers hardness tester. The surface roughness increases first and then decreases with the increase of peening coverage. The average surface roughness of peened sample with 100% coverage is 4.601 μm, which is the highest value. Moreover, the peened surface shows significant folds and microcracks owing to the high coverage of 400%. The increase of peening coverage leads to the enlargement of the maximum compressive residual stress and depth of the compressive residual stress layer. Similarly, the maximum hardness and hardened layer depth are enhanced with peening coverage. The maximum hardness corresponding to peening coverage of 88.3%, 100%, 200% and 400% is increased by 18.4%, 22.8%, 25.1% and 27.2%, respectively.

To improve the surface forming quality and machining efficiency of aluminum matrix composites, a twodimensional ultrasonic combined electrolysis/discharge generating machining technology (2UE/DM) with low voltage and low current density was proposed. Using the side of a tool coated with diamond grains, three different processing contrast tests and performance tests under three sets of parameters were carried out on the composite. The current, material removal rate (MRR) and surface roughness of SiC_pAl composites processed in 0.5% NaNO₃ (mass fraction) solution were measured, and the effects of tool rotation speed, voltage and amplitude on processing efficiency and quality were explored. The results showed that the vibration of the workpiece periodically changed the machining gap, and the discharge frequency increased by 2 times per vibration period. At 5000 r/min, the discharge frequency decreased, the surface roughness decreased by 13.2% compared with 1000 r/min. At the voltage of 6 V, the material removal rate reached 0.89 mm³/min, which was 45.9% higher than 3 V, but more reinforcing particles were exposed at the higher voltage, and the surface roughness was 1.4 μm higher than 3 V. When the amplitude increases to 5 μm, the surface roughness was 17.5% lower than 2 μm, and the technology had higher machining quality.

This paper studies a laser melting deposition path generation strategy based on industrial robots to addressthe repair problem of aero-engine TC17 high-pressure compressor blades after service damage. This method can analyze the blade damage area with model Boolean operations and embeds a secondary development plug-in in the RobotStudio offline programming software to generate Zigzag-axial, centerline-offset, and edge-helical scanning paths. The path simulation and deposition topography analysis show that when the scanning speed is 5 mm/s, the deposition layer thickness is 0.3 mm, and the overlap ratio is 50%, the centerline-offset scanning path can maintain the speed stable for 95.97% of time, and the speed decline of turning point is tinier. Simultaneously, the formed height after deposition is close to 0.3 mm, and the height fluctuation variance is relatively small with excellent flatness. Furthermore, simulations and experiments on rectangular flat-plate regions show that a continuous path, such as Zigzag scanning path, can dramatically reduce residual stress on the formed surface.