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.

The weld pass classification and the region of interest (ROI) extraction are crucial for enhancing the subsequent feature extraction and image processing speed in robotic automatic multi-layer multi-pass (MLMP) welding. Therefore, a novel model, lightweight weld pass classification and ROI extraction (LCE), is proposed. Initially, LCE is constructed based on object detection by some lightweight modules and structures, achieving a low model scale. Then, an enhanced bounding box regression loss function is presented to improve the precision of LCE. Finally, experiments show that LCE’s mean average precision and processing time are 92.7

Laser-assisted automated fiber placement (LAFP) enables high-speed consolidation of thermoplastic composites, yet unstable energy input often leads to weak interlaminar bonding and porosity. Existing optical-thermal models remain limited in resolving three-dimensional energy temperature coupling and supporting process-oriented optimization. This study develops a highly parameterized Monte Carlo ray tracing (MCRT) framework coupled with a transient thermal model to predict spatially resolved heat flux and temperature evolution during LAFP. The optical model resolves tow-scale anisotropic absorption and incorporates key process parameters, including laser incidence angle, spot geometry, energy distribution ratio, and placement speed. Model predictions are validated using eight-tow T700/PEEK layup experiments with synchronized infrared thermography and embedded thermocouples. The coupled model reproduces measured temperature histories with a relative deviation of 1.8% for surface peak temperatures and interlaminar RMSE values ranging from 5.9 to 10.8 degrees C. Parametric analysis reveals that the energy distribution ratio governs interfacial melting behavior, while spot geometry and placement speed control peak temperature and thermal dwell time. The proposed framework provides a quantitative basis for robust process window design in thermoplastic composite placement.

This study presents a novel 3D interlayer contact model based on fractal theory to quantify the evolution of intimate contact during thermoplastic prepreg processing. The rough surface is modeled as a 3D Cantor set embedded in a 2D plane, and the 3D deformation of asperities under processing conditions is described using a creeping squeeze flow model in polar coordinates. Key parameters, including fractal dimension, scaling factor, and a dimensionless surface roughness ratio, are calibrated and used to predict interlayer contact in three-layer prepregs under various thermoforming conditions. Experimental validation using metallographic and C-scan analysis confirms the model's accuracy, with most prediction errors within 5%. Compared to conventional 2D models, the 3D model provides improved accuracy by accounting for the 3D flow of asperities, particularly under higher temperature and pressure. Finally, the independent impact mechanisms of the processing parameters and surface geometry on intimate contact are analyzed, with temperature and fractal dimension exerting the greatest impact on the rate of contact development. This model serves as an effective tool for predicting intimate contact evolution in thermoplastic composite processing and provides theoretical support for interlaminar strength prediction and manufacturing process optimization.

Accurate identification of the horizontal motion model is essential for Autonomous Underwater Vehicle (AUV) to achieve precise path-following and efficient area-coverage tasks. This paper presents a novel online parameter identification framework that simplifies the traditionally complex process of modeling AUV horizontal dynamics. Specifically, an Online Sparse Bayesian Learning (OSBL) algorithm equipped with a dynamic window mechanism is proposed to enable iterative real-time estimation. Furthermore, an adaptive super-twisting observer is developed to estimate environmental disturbances and model uncertainties, thereby enhancing the robustness of the recursive identification process. Theoretical convergence and robustness guarantees are established through Lyapunov-based stability analysis. Extensive simulations and real-world AUV experiments further validate the effectiveness of the proposed approach, demonstrating faster convergence and strong robustness in dynamic marine environments

The accuracy of manoeuvring model identification for autonomous underwater vehicles (AUV) is often compromised by measurement noise and outliers in speed signal recordings. To address this issue, we propose a robust maneuvering parameter identification framework based on Bayesian quantile regression with horseshoe priors. By incorporating rudder effectiveness coefficients into an improved low-order maneuvering model, both hydrodynamic and actuation-related parameters can be identified in a unified manner. The proposed approach effectively suppresses heavy-tailed noise and abnormal samples without relying on Gaussian noise assumptions. Its effectiveness is validated through simulation and experimental studies, including zigzag maneuvers, demonstrating robust performance against measurement noise and outliers and confirming its practical applicability.

Deep-sea oil and gas transportation pipelines face stringent requirements due to extreme operating conditions, demanding high temperature resistance, pressure tolerance, corrosion resistance, and reduced weight. To address these challenges, this study proposes an innovative pipeline design that integrates a composite casing with a steel liner, assembled via a liquid nitrogen-cooled interference-fit. This hybrid design leverages the composite's lightweight and corrosion-resistant properties while retaining the steel liner's structural robustness. Finite element simulations validated the pipeline's superior performance under deep-sea conditions, demonstrating a weight reduction of approximately 54.4% compared to equivalent steel pipelines and a 30.6% reduction in maximum stress in the steel liner compared to conventional composite pipelines. The simulations also elucidated the strengthening mechanisms induced by the interference-fit assembly through improved stress distribution and enhanced load-bearing capacity. Additionally, a novel, efficient, and safe low-temperature interference-fit assembly tool was developed, supported by a comprehensive manufacturing and assembly process. Experimental results from the fabricated pipeline showed strong agreement with finite element predictions, with deviation in assembly deformation below 15%. This work offers a lightweight, corrosion-resistant, and mechanically robust alternative to traditional metal pipelines, with substantial potential for ocean engineering applications.

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.

Accurately predicting the underwater acoustic transmission characteristics of fiber-reinforced polymer (FRP) laminates is essential for optimizing composite structures in sonar and marine applications. However, traditional prediction models often neglect the influence of hydrostatic pressure on wave propagation and energy dissipation, leading to limited accuracy. This study proposes an enhanced stiffness matrix method (E-SMM) that incorporates pre-stress effects into guided wave analysis to address this limitation. The approach first employs nonlinear static analysis to evaluate the initial stress-induced geometric stiffness under various hydrostatic pressures. This updated stiffness is then integrated into the stable stiffness matrix method (SMM) to derive pressure-dependent dispersion curves and wave attenuation coefficients. Using these coefficients, a modified transmission loss model is developed to quantify acoustic transmission across different laminate thicknesses, pressures, and frequencies. The model is validated against experimental data obtained from traveling wave tube measurements. The experimental results show that E-SMM captures key acoustic-structure interaction mechanisms—particularly pressure-dependent guided wave behaviour-leading to improved numerical stability and predictive accuracy. This makes it a valuable tool for designing high-performance underwater acoustic composite structures.

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.

Automated fiber placement (AFP) of complex curved structures is frequently hindered by steering-induced tow wrinkling. Existing wrinkle prediction approaches typically involve a trade-off among geometric applicability, process fidelity, and computational efficiency. To address this issue, this study proposes a multiphysics-coupled framework for wrinkle assessment that integrates material properties, path geometry, and process-parameter effects within a unified analytical formulation. A physics-informed wrinkle criterion is introduced to characterize wrinkle initiation as a function of process variables and interfacial tack evolution. An FE-calibrated analytical contact pressure model applicable to arbitrary curved surfaces is also developed to provide the process-state quantities required for wrinkle assessment. These components are integrated into a unified framework for wrinkle prediction and mitigation and are applied to representative winglet and S-shaped surfaces, followed by AFP experimental validation. The results show that the proposed approach not only identifies geometry-driven wrinkling risks induced by excessive path curvature, but also captures process-induced risks associated with locally insufficient interfacial tack under suboptimal process conditions. The computation time is only 9.5 s for an 8-tow course with an approximate length of 1400 mm. For the winglet surface, reducing the number of tows within a course eliminates the predicted wrinkling risks at the course edges in the local convex region. For the S-shaped surface, process optimization reduces the predicted wrinkling risk from approximately 20% to zero. Experimental results further confirm high prediction accuracy and effective wrinkle suppression, supporting manufacturability assessment and process planning for engineering-scale AFP applications.

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.

Continuous ultrasonic welding (CUW) is a promising technology for assembling large-scale thermoplastic composite structures; however, achieving robust joint quality remains challenging due to the complex dynamic thermal equilibrium involved. Unlike static welding, CUW operates under a moving thermal equilibrium in which energy density and residence time must be precisely balanced to ensure effective polymer melting. In this study, the CUW process of continuous carbon fiber-reinforced PEEK (CCF/PEEK) laminates was systematically investigated using Response Surface Methodology (RSM), integrated with in-situ infrared thermography and instantaneous power-signature analysis. A pronounced non-linear interaction between vibration amplitude and welding speed was identified. An optimized processing regime produced cohesive failure and a maximum lap shear strength of 33.14 MPa. Excessive vibration amplitude applied without sufficient welding pressure was found to induce dynamic acoustic decoupling, leading to intermittent contact and inefficient energy dissipation as localized impact heating. This phenomenon is explained by the hammering effect, which accounts for deterioration in joint quality at high nominal energy inputs. It also demonstrates that joint formation is governed not solely by nominal energy input but by the stability of acoustic coupling at the welding interface. The corresponding instantaneous power waveform serves as a sensitive process fingerprint for distinguishing effective coupling from decoupled welding states. By elucidating the coupled roles of energy density, acoustic coupling stability, and thermal history, this work defines a robust processing window for CUW of CCF/PEEK and highlights the potential of power-signature monitoring for in-situ control.

Continuous ultrasonic welding (CUW) is a promising technology for assembling large-scale thermoplastic composite structures; however, achieving robust joint quality remains challenging due to the complex dynamic thermal equilibrium involved. In this study, the CUW process of continuous carbon fiber-reinforced PEEK (CCF/ PEEK) laminates was investigated using Response Surface Methodology (RSM), in-situ thermal monitoring, ultrasonic C-scan inspection, and instantaneous power-signal characterization. A strong interaction between vibration amplitude and welding speed was identified, showing that joint quality depends on the balance between effective energy input and thermal exposure during the moving welding process. The optimized processing condition produced cohesive failure and achieved a maximum lap shear strength of 33.14MPa. Quantitative analysis of temperature and power curves further revealed that high nominal energy input does not necessarily lead to high joint strength. Under high amplitude and insufficient pressure conditions, intermittent acoustic contact caused hammering-related instability, in which part of the input energy was dissipated through impactdominated mechanism rather than interfacial heating. The coefficient of variation of power, CVP, was introduced to evaluate power-signal stability: a low CVP corresponded to stable acoustic coupling, whereas pronounced power drops and higher CVP values indicated intermittent decoupling. These findings demonstrate that CUW quality is governed by effective energy transmission, thermal history, and acoustic coupling stability rather than nominal energy input alone. The identified process window provides guidance for robust CCF/PEEK continuous ultrasonic welding and supports the use of power-signal monitoring for in-situ process assessment.

Path planning is crucial for autonomous underwater vehicles (AUVs), ensuring their safety and demonstrating their intelligence. However, the underwater environment is unstructured, with unknown static and dynamic obstacles. Moreover, the kinematic constraints of the AUV's movement add to the complexity of planning. To address these challenges, this paper proposes a hybrid approach that combines crested porcupine optimization (CPO) with an improved dynamic window approach (DWA), called DFDWA. First, CPO is used for global path planning to find an optimal solution. Second, to better avoid suddenly appearing dynamic obstacles, we enhance the traditional DWA in four ways: extending it to three dimensions to better model the AUV's motion; incorporating a three-dimensional distance field to improve dynamic obstacle avoidance; calculating the Distance at Closest Point of Approach (DCPA) for collision risk assessment; and using fuzzy logic to adaptively tune DWA parameters. Finally, simulation results demonstrate that the proposed algorithm effectively avoids both static and dynamic obstacles, reduces detours while maintaining safety, thereby making a valuable contribution to future AUV path planning.

The precise assembly of aircraft structures remains a critical challenge in aerospace manufacturing, as assembly gaps are a primary factor undermining precision. This study proposes a surrogate model for the rapid and accurate prediction of assembly gaps in the presence of structural deformation. The methodology begins with creating a comprehensive assembly gap dataset that integrates both geometric deviations and structural deformations through assembly modeling and automated workflow. Building upon this dataset, an enhanced PointNet+ + (PNP) network is employed to extract and fuse multi-source assembly features from part shape point clouds and tooling movements. These fused features are then integrated with a generation network based on the Conditional Generative Adversarial Network (CGAN) architecture, reformulating gap prediction as a conditional generation task. The innovative integration of the two networks realizes an end-to-end pipeline, from initial assembly feature extraction to final assembly gap prediction. A representative wing-box structure was employed as a case study to validate the approach. The trained model efficiently predicts gap fields directly from multi-source assembly information. Experimental results demonstrate that the proposed model achieves prediction accuracy comparable to virtual assembly methods while significantly enhancing computational efficiency. These findings underscore the model's efficacy, positioning it as a valuable tool for rapidly predicting gaps in aircraft assembly.