Owing to the limitations of single-scale modeling in predicting the impact response of composite bolted joints, this work proposes a bottom-up multiscale analysis strategy. Equivalent material parameters derived from the microscale model are transferred to the macroscale model of joints. The established model is validated by 50 J low-velocity impact tests, with numerical results showing good agreement with experimental data. Results indicate that the joint's impact response reflects the coupling effect of dynamic load distribution, energy conversion and progressive damage. Specifically, a four-peak decreasing contact force-time curve and staged energy conversion process are observed. Under 50 J impact energy, joint damage exhibits significant asymmetry, mainly concentrating in the single-side bolt area, characterized by interlaminar delamination and transverse fiber fracture bands passing through the hole. The single-bolt dominant load-bearing mode originates from the coupling of joint bending deformation and bolt-hole wall contact state. And the quasi-isotropic layup joint outperforms the cross-ply layup joint in impact resistance. This multiscale method provides a reliable numerical tool for impact resistance optimization design and damage assessment of composite structures.

Micro/nano manipulation methods supplement top-down and bottom-up approaches, enabling complex device fabrication at the microscale. Here we present an electrostatic micromanipulation technique that effectively controls the orientation and acceleration of extreme-shape carbon-based micro/nanomaterials in vacuum, achieving high-speed migration, directional transport, precise alignment, and impact embedding. Firstly, by studying the steady-state and dynamic characteristics of high-voltage electrostatic fields in vacuum, the innovative robust rubber-encased electrode was introduced to achieve the stable construction of high-intensity electric fields under broad vacuum conditions. Based on this, the electrostatic micromanipulation process was explained, capturing dynamic behaviors of micro/nanomaterials through experiments. Results show that micro/nanomaterials experience attraction via electrostatic induction and launching through dielectric polarization. This achieves manipulation goals such as picking up migration, directional transport, and arranged placement in vacuum with one-step electric field driving. In summary, this study offers new methods for utilizing carbon-based materials efficiently in vacuum across multiple scales.

ABSTRACT The bending stiffness of unidirectional (UD) prepregs is non‐constant and temperature‐dependent, playing a critical role in wrinkle formation during preforming. Therefore, establishing a temperature‐dependent constitutive model to capture the bending behavior is essential for accurately predicting wrinkling defects. In this study, the temperature‐dependent evolution of the mechanical properties of UD prepregs was systematically investigated through experiments. Horizontal cantilever bending tests at different temperatures show that UD prepregs exhibit a strongly nonlinear moment–curvature response and temperature‐dependent bending stiffness, with the highest sensitivity below 45°C. The 90° bending stiffness is significantly lower than that in the 0° direction. Based on the exponential decay model, two independent temperature‐dependent Bi‐Linear Bi‐Material (BLBM) constitutive models were established for the 0° and 90° nonlinear moment–curvature responses of UD prepregs. The models were implemented into ABAQUS via a VUMAT subroutine, enabling integrated simulation of temperature effects, anisotropic stiffness evolution, and nonlinear bending behavior. The model‐predicted moment–curvature and deflection curves show excellent agreement with the experimental results, with coefficients of determination exceeding 0.97, confirming the validity of the proposed model in capturing the nonlinear bending behavior of UD prepregs. This study provides a methodological foundation for wrinkling prediction in composite preforming.

The development and application of Automated Fiber Placement (AFP) technology have laid a solid foundation for the large-scale application of composite materials in aircraft manufacturing. However, layup defects are inevitably present during the AFP process, directly affecting the performance of the manufactured composite products. To address this problem, this paper creates an Automated Fiber Placement Defect Detection Network (AFP-Net) for implementation in domestically manufactured gantry-type AFP machines. AFP-Net is an end-to-end object detection framework that integrates three key innovations: a novel multi-scale feature fusion convolutions module, an adaptive feature pyramid network with weighted multi-scale information aggregation, and a lightweight detection head specifically tailored for AFP defect detection. Specifically, this study introduces three innovative designs for AFP defect detection: (1) a Multi-scale Feature Fusion with Convolutions (MFFC) module that employs multi-scale convolution kernels without dilation to extract features at different scales and capture local context; (2) a Gap-foreign Aimed Detection Feature Pyramid Network (GADFPN) that enhances multi-level feature utilization through efficient weighted fusion; and (3) a Gap-Aimed Detection (GAD) Head that uses shared convolutions instead of redundant independent ones, improving gap defect detection accuracy while maintaining computational efficiency. Experimental results demonstrate that the proposed AFP-Net exhibits excellent performance in defect detection in the AFP of composite materials. Moreover, comparison experiments on the publicly available surface defect dataset validate that AFP-Net outperforms YOLO and RT-DETR, demonstrating that AFP-Net has outstanding generalization ability.

Composite bolted joints are widely used in engineering applications, but their mechanical performance and failure mechanisms still pose challenges. Typically, macroscopic-scale analysis is used to predict structural performance. In this work, an innovative top-down multi-scale numerical approach is proposed, which combines macro-scale damage models with micro-scale crack analysis. The effect of interference percentage on the bearing behavior of joints is systematically investigated. At the macroscopic scale, the mechanical response and progressive failure of the joint is revealed. Furthermore, by transferring macroscopic strain information to the microscopic model, the initiation and propagation of microcracks are accurately captured. Additionally, tensile tests and scanning electron microscopy (SEM) were conducted to analyze the failure characteristics of the joints under different interference percentages. This approach not only overcomes the limitations of traditional macro models but also provides a new theoretical framework and experimental basis for optimizing the design of composite joints.

The thickness transition in composite rotor blades, achieved through multiple ply drop-offs, promotes stress concentrations and increases the risk of delamination initiation. To simulate the stress state, tension-torsion tests of tapered laminates were conducted using a custom fixture on a universal testing machine. The experimental results validate the proposed numerical model. The results indicate that the pre-torsion load induces additional stresses, which are intensified at the thin end. A 40 degrees pre-torsion reduced the specimen's ultimate tensile load by approximately 22.3% relative to its performance under pure tension. For specimens subjected to a 40 degrees pre-torsion, delamination initiates at the thin end and extends toward the thick end. Conversely, at pre-torsion angles below 30 degrees, the delamination initiates at the thick end and propagates toward the thin end. Furthermore, layup schemes influence the failure load of tapered laminates, resulting in a variation of up to 27.2%. The 0 degrees dropped plies typically act as a stress concentration zone. In contrast, f45 degrees dropped plies can effectively mitigate this stress concentration and transfer the pre-torsion load from the thin end to the thick end, thereby altering the delamination initiation site and propagation. Although f45 degrees plies can improve the bearing capacity, they introduce a stiffness penalty.

Mechanical property analysis in defect-containing composites is essential to composite design, yet existing methods suffer from an inability to simulate randomness, limitations in scale, or high computational resource consumption. Therefore, a Transformer model integrated with principal component analysis (PCA) is proposed for the first time to accurately predict stress-strain (S-S) curves in mesoscale carbon fiber reinforced polymer (CFRP) laminates with random gaps. Based on automated fiber placement (AFP), a laminate with random gaps finite element model is created. The model's reliability is verified experimentally, and a Python script for parameterized modeling is developed to build the dataset. Subsequently, PCA is applied to reduce the dimensionality of the S-S curves, lowering model complexity and computational demands. A Transformer model is then constructed, with its sample size sensitivity and stability analyzed through different dataset sizes and 5-fold cross-validation. The model's performance is comprehensively evaluated by comparing it with four other deep learning models designed to predict S-S curves. The results indicate that S-S curves predicted by the Transformer model correlate well with results from finite element simulations, with a correlation coefficient above 0.9999. The relative percentage errors in tensile strength and elastic modulus derived from the predicted curves remain within 0.28%, and the Transformer model demonstrates significant advantages in both accuracy and computational resource consumption. The proposed model combines high accuracy with computational cost-effectiveness, which characterizes mesoscale defects and predicts their impact on mechanical properties, promising a new and highly potential tool for composite material design optimization and uncertainty quantification.

As a representative tapered composite laminate, composite blades are widely employed for lightweight purposes, but ply drop-offs in blades can induce premature delamination. To compensates for the weakened interlaminar capacity resulting from ply drop-offs, a pseudo-woven tapered laminate was tested under tension–torsion loading, which approximating the service conditions of composite blades. A high-fidelity finite element model of the pseudo-woven reinforced configuration was established based on the actual morphology, which was validated against experimental results. Due to the mechanical interlocking effect of pseudo-woven configuration, an increase in the delamination onset load was achieved compared with the baseline specimens. The pseudo-woven tapered laminate exhibits a tensile failure mode similar to that of a laminate without ply drop-offs, while the baseline fails by delamination alone. In addition, the influence of the Translaminar-envelope ply’s (TEP) orientation and the geometric parameter ‘n’ on the reinforcement effect of the laminate was analyzed. The angular mismatch between the TEP and the segmented plies has two competing consequences. As these effects cancel each other out, the overall influence of the TEP’s fiber orientation remains small under the present geometry. A strong dependence of the delamination onset load on the TEP’s geometry is evident. A comparison of the three TEP geometries reveals a positive yet nonlinear correlation between the geometric parameter ‘n’ and the delamination onset load.

During composite preforming, unidirectional (UD) thermoset prepregs are prone to out-of-plane wrinkling. To accurately predict such defects, this study investigates the mechanical properties of USN25000/7901 UD prepreg at 25 degrees C, focusing on bending stiffness. Free-cantilever bending tests of single-layer UD prepreg reveal strongly nonlinear bending moment-curvature (M-kappa) responses in both the 0 degrees and 90 degrees directions, reflecting the material's inherently non-constant bending stiffness, with the 90 degrees bending stiffness much lower than that of 0 degrees. The nonlinear M-kappa responses are well captured by two independent Bi-Linear Bi-Material (BLBM) models. Simulations based on the BLBM model present that the predicted bending-moment accuracy improves by 42.39 % and 62.37 % in the 0 degrees and 90 degrees directions, respectively. The mean relative errors of the predicted free-cantilever deflection are 4.23 % and 3.39 % in the 0 degrees and 90 degrees directions, both superior to conventional models. The out-of-plane bending deformations from axial compression and 10 degrees off-axis tensile simulations using the BLBM model also show better agreement with experiments. The normalized RMSE of the predicted 0 degrees axial compression bending deformation remains within 5.5 %. These results demonstrate that the BLBM model offers a practical approach for predicting out-of-plane bending and wrinkling defects of UD prepregs during preforming.

Aero-engines typically operate under time-varying conditions, which obscure degradation patterns in sensor data and significantly increase the difficulty of Remaining Useful Life (RUL) prediction. Approaches based on Graph Neural Networks (GNNs) extract spatial-temporal features by constructing homogeneous graphs and refining node feature update mechanisms. However, these studies do not adequately model how operating conditions influence sensor data, thereby weakening the methods' robustness. Therefore, we propose an Operating Condition-Aware Heterogeneous Graph Neural Network (OCA-HGNN) for RUL prediction of aero-engines under time-varying operating conditions. First, we introduce an expert knowledge-driven method for constructing a condition-sensor heterogeneous graph that explicitly accounts for the engine's mechanical structure and operating principles. Then, a Spatial-Temporal and Operating Condition-Aware Message Passing (STOCA-MP) mechanism is developed to update node features and select edges. STOCA-MP computes and fuses the central node's historical message, spatial messages from neighboring nodes at the same time step, and influence messages from operating-condition nodes, while identifying the edges that provide more information to the central node. Finally, STOCA-MP is embedded into neural network layers to form OCA-HGNN for RUL prediction. Experiments on the C-MAPSS and N-CMAPSS datasets demonstrate that the proposed method is effective for spatial-temporal feature extraction, modeling the influence of operating conditions, and aggregating multisource messages, and it achieves high prediction accuracy under time-varying operating conditions.

Operating conditions of aero-engines often change over time, complicating the prediction of their remaining useful life (RUL). Most RUL prediction methods overlook the impact of time-varying operating conditions on feature extraction and fail to account for the potential interference of operating condition information in sensor data features. Additionally, deep-learning-based methods lack sufficient interpretability. To address these issues, we model sensor data as a coupling of operating condition and equipment degradation information, and propose an interpretable model named variational attention-weighted feature decoupling network (VAFD-Net) for RUL prediction of engines under both discrete and continuous time-varying operating conditions. VAFD-Net separately extracts operating condition features and sensor data features, then uses variational attention weights representing operating condition information to weight sensor data features, improving the model’s robustness. VAFD-Net also introduces three constraints to decouple operating condition and degradation information in latent space, mitigating the impact of signal non-stationarity. Experimental results on the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) and N-CMAPSS datasets indicate that VAFD-Net not only achieves high prediction accuracy under time-varying operating conditions but also reveals the contribution of physical quantities to the engine’s health states through weighted feature maps. Furthermore, latent variable distribution plots enable users to directly infer the RUL based on the position of the latent variables.

Automated fiber placement (AFP) is a key technology in aerospace manufacturing, in which the maintenance of rated load is a necessary condition for high-quality forming of composite parts. In order to realize the continuous and stable contact between the flexible roller and the complex mold in the process of robotic AFP, a constant force stabilization method based on off-line trajectory correction is proposed in this paper. In this method, the robot stiffness model and the contact mechanics model proposed in the previous work are used to accurately predict the robot deformation and roller deformation. The two kinds of deformation are compensated to the initial trajectory to keep the distance between the robot end and the roller constant, so as to reduce the burden of the pneumatic system and achieve the stability of the contact force. Static loading experiments demonstrate the accuracy of the proposed method for trajectory correction, and dynamic loading experiments verify the effectiveness of the proposed method for suppressing contact force fluctuations. Experimental results show that this method reduces the maximum error of contact force control from 18.3 % to 7.5 %. The experiment of fiber placement further elucidated the engineering value of this method for laying quality improvement.

This study for the first time develops a novel stochastic multiscale method to elucidate the impact of void defects on the dynamic progressive failure of multidirectional CFRP laminates subjected to low-velocity impact (LVI). Initially, void defects are characterized using optical microscopy, and a high-fidelity representative defect volume (RDV) is constructed. Following this, the impact of voids on microscopic failure and macroscopic properties is assessed by micromechanical models. Finally, the dynamic progressive failure behavior of CFRP laminates is predicted using the stochastic multiscale model and validated through LVI experiments. This model is executed in the nonlinear finite element analysis software ABAQUS via a user-defined material subroutine (VUMAT). The findings indicate that the presence of void reduces the composite’s resistance to LVI, decreases peak force, increases maximum displacement and absorbed energy, significantly affecting damage and failure mechanisms. The proposed multiscale numerical model shows excellent agreement with experimental results.

The low stiffness of series robots limits their application in high-load precision manufacturing, such as automated fiber placement (AFP). This paper presents a stiffness optimization method to enhance the stiffness of plane-mobile robots in continuous fiber placement by simultaneously adjusting the robot's posture and the base position. A stiffness performance index suitable for evaluating the comprehensive stiffness of the robot during the AFP process is proposed, which is based on the fluctuation characteristics of the contact force in fiber placement. To maximize this index and the normal stiffness, the multi-objective particle swarm optimization algorithm (MOPSO) is used to solve the two-objective optimization model under multiple constraints. The constrained area of the mobile robot base corresponding to a given path point is determined by the fixed-height slice of the robot's reachable point cloud. A novel method combining global discrete solution and local continuous solution (GD-LC) is proposed to solve the model efficiently, which reduces the search dimension of the MOPSO algorithm. Experimental results from fiber placement on an aircraft mold show that the proposed method can significantly improve the stiffness performance of the AFP robot, and the force-induced deformation after continuous stiffness optimization is reduced by 70.01 % on average. The optimized laying quality further validates the engineering value of the proposed method.

To minimize the damage caused by Z-pin insertion to the in-plane properties of the laminate, researchers have initiated investigations into the utilization of fine Z-pins and reduced insertion densities. However, the manufacturing process of fine Z-pins introduces voids and fiber fracture defects, leading to a decline in Z-pin quality and consequently affecting the efficacy of interlaminar toughening. In this research, defects in the Z-pin preparation process were identified through extensive experiments, and pre-stressing is applied during the curing of the Z-pin, leading to the successful development of the high-quality 80 mu m and 90 mu m diameter Z-pins. Furthermore, this research extensively examines the impact of these two defects on the quality of Z-pins and provides a model to determine the optimal Z-pin size corresponding to various insertion depths under the existing processing conditions. For example, according to the model, the optimal Z-pin size for laminates with a thickness of 4 mm is 90 mu m. This ensures that the Z-pin's failure mode remains at the critical transition between pull-out and fracture, thereby maximizing its tensile strength utilization. The research findings are validated through experimental verification, thus providing insights for the application of ultrafine Z-pins.

Reliable sonar target tracking and image processing face significant challenges due to multiplicative speckle noise and the complexity of maneuvering target motion. Conventional approaches, such as median and Gaussian filtering for image processing and multihypothesis tracking or probability hypothesis density for target tracking, often struggle to address these issues effectively. Inspired by radar-based tracking techniques, we propose an enhanced track-oriented multihypothesis tracking algorithm incorporating track temporary storage to improve multitarget tracking performance. To suppress sonar image noise, we employ an advanced Wiener filter optimized with Kalman filtering. For target detection, a novel threshold segmentation method leveraging polygon fitting enhances the identification of salient sonar targets. The extracted target position data are then fed into an improved multitarget tracking framework based on a rectilinear-curvilinear hybrid multimodel interaction, which includes a mechanism for temporary storage of missed tracks. Simulation results demonstrate that this approach effectively mitigates multiplicative speckle noise, enhances the peak signal-to-noise ratio, and enables robust tracking of complex underwater target motion while reducing the optimal subpattern assignment error. Furthermore, experiments conducted using multibeam forward-looking sonar images in a laboratory-scale pool with a high-reverberation environment validate the proposed method's effectiveness. The present algorithms are expected to be integrated with the underwater vehicles' operating system for high-efficiency obstacle avoidance and maneuvering target tracking.

This study investigates the fatigue behavior of aircraft countersunk riveted joints. Riveting tests and their numerical counterparts were conducted to explore the effects of hole diameter and squeeze force on the driven head dimensions and the distribution of interference magnitude. Fatigue tests were performed to analyze the fatigue behavior, including fatigue life, failure mode, etc. The microstructural characteristics of the fractures on the rivets and plates were analyzed to observe and measure the distribution and chemical composition of fretting debris, which effectively revealed the mechanisms of fatigue failure and fretting of the riveted joints. The results demonstrate that appropriately increasing the squeeze force and reducing the hole diameter can significantly improve the fatigue life of the riveted structure. There are two failure modes during fatigue: rivet fracture and outer plate fracture. Cracks were observed on both the rivets and outer plates during the fatigue failure, indicating a competitive relationship between their crack propagation. Fretting wear on the contact surfaces of the inner and outer plates initiates fatigue cracks. The fretting debris contributes to the initiation and propagation of the cracks, which expand along the thickness and width directions of the riveted joints.

Ply drop-offs represent the regions of structural weakness in variable-thickness composite laminates, such as those found in pump-jet rotor blades. However, the structural experiment of the rotor blade entails considerable expense at the preliminary design stage. This paper proposed a combined tension-torsion loading method, which used tapered laminates to simulate the stress state of a pump-jet rotor blade under critical failure conditions. By matching the geometric and stress-state similarity, this approach offers a cost-effective means of verifying preliminary designs. The fixture system was developed to impose the pre-torsion load, whereas the tensile load was applied using a uniaxial testing machine. The design of tapered laminate specimens was further investigated to more accurately approximate the stress conditions experienced by pump-jet rotor blades. The results show that the specimen's width has a significant effect on the stress field. The stress increases with decreasing width. A too short interval between the clamping area and the tapered part of the specimen leads to a severe stress concentration effect on Interface 1. A converse trend is observed for Interface 2. With the increase in the interval, the influence of the clamping end is weakened. Therefore, it is necessary to determine the width and interval appropriately to achieve the stress-state similarity between the tapered laminate and the rotor blade.

Aero-engine nacelle acoustic liners are complex curved surface subassemblies with tens of thousands of dense acoustic holes for noise reduction. Traditional robotic drilling systems with a single spindle and conventional teaching programming cannot meet the high-quality and high-efficiency drilling requirements for nacelle acoustic liners. This paper developed a novel robotic multi-spindle drilling system, which integrates standard industrial robots with a multi-spindle drilling end-effector to drill acoustic holes on an acoustic liner. The task planning strategy and offline programming method are investigated for the robotic multi-spindle drilling of the acoustic liner. A dedicated offline programming software capable of quickly generating robotic machining programs has been designed and developed. A precise acoustic hole arrangement applicable to curved drilling zones is demonstrated. The virtual surrogate hole is introduced to target the array layout of the developed end-effector's multiple spindles. A virtual surrogate hole generation method is proposed based on the minimum mutually exclusive set cover and the greedy algorithm. After that, a virtual surrogate hole layout optimization method is developed using an adaptive genetic annealing algorithm. Then, the frame chain is constructed as the kinematic foundation in robotic multi-spindle drilling of a nacelle acoustic liner. The drilling pose planning of the multi-spindle end-effector based on normal vectors and coordinates of acoustic hole points covered by virtual surrogate holes is explored. And a multi-spindle drilling path planning algorithm is developed using the genetic algorithm. To drill the aero-engine nacelle of a large aircraft, an offline programming software for robotic multi-spindle drilling of the nacelle acoustic liner is developed by integrating the methods above. A case study of multi-spindle drilling task planning of a nacelle acoustic liner is carried out. The case study results indicate that, compared to traditional single-spindle robotic drilling systems, the developed multi-spindle drilling system increases drilling efficiency by approximately 347 %. Using the proposed multi-spindle drilling path optimization algorithm, the drilling path has been shortened by 84.8 %. These results validate the effectiveness of the proposed series of drilling task planning methods and the developed offline programming software, which significantly enhance drilling efficiency while meeting the technical requirements for drilling acoustic holes on the acoustic liner.

Laying acoustic liners is one of the most effective ways to reduce aero-engine noise. Traditional single-spindle drilling systems are inadequate for high-quality and efficient drilling for large-scale sound-absorbing holes on acoustic liners. Therefore, this paper develops a robotic multi-spindle drilling cell (RMDCell) that integrates a multi-spindle drilling end-effector, a standard industrial robot, and a rotatable clamping fixture. A robot performance-oriented layout optimization method is studied for the RMDCell to improve the drilling quality of acoustic liners in aero-engine nacelles. Firstly, a weighted comprehensive performance model of the multispindle drilling robot is built, integrating joint limit avoidance, singularity avoidance, and robot stiffness performances. Thereinto, a new robot stiffness performance index is introduced, and a variable robot stiffness weight is derived considering the end-effector's spindle configuration. Secondly, a dimensionality reduction method of layout optimization parameters and a drilling zone division method are proposed for the RMDCell of nacelle acoustic liners. A bi-level optimization algorithm for RMDCell layout parameters is developed. The algorithm fuses particle swarm optimization (PSO) and deep hierarchical feature learning on point sets in a metric space (PointNet++). Finally, drilling experiments are conducted based on the developed RMDCell. The results show that the comprehensive robot performance index is reduced by 39.8 % after optimization compared to the comprehensive robot performance before layout optimization. The layout optimization method effectively reduces defects such as burrs, delamination, and tearing in composite sound-absorbing holes. The average delamination factor decreases by 16.5 %. The robot performance-oriented layout optimization significantly improves the drilling quality, which meets the manufacturing quality requirements of the acoustic liner of an aero-engine nacelle.