Thin-walled aircraft components are highly prone to deformation during positioning, which can lead to forced assembly and stress concentration. Once the structure is removed from the tooling, stress release may cause significant shape distortion. Conventional positioning layout design relies heavily on deformation simulations, which are often limited in accuracy, efficiency, and applicability. To address these challenges, this study introduces a stiffness distribution guided positioning layout design method. Specifically, the concept of a stiffness field along the minimum-stiffness direction is proposed, and a positioning layout feature map is used to construct paired image datasets. A conditional generative adversarial network featuring a multi-scale generator, an enhanced convolutional block attention module, and a multi-scale discriminator is developed to enable rapid prediction of stiffness fields from given positioning layouts. Moreover, three stiffness-distribution evaluation indices are defined based on the predicted stiffness field. Together with the pre-trained deep learning model, these indices form a solver integrated into NSGA-III, with additional engineering constraints incorporated. Finally, a case study on the rear fuselage of a fighter aircraft demonstrates the implementation of the proposed stiffness-field prediction and positioning layout algorithms. A flexible tooling system with reconfigurable positioning points is designed for experimental validation. Results show that the proposed method limits deformation in the minimum-stiffness direction to 0.790 mm. Compared with the baseline positioning layout, average deformation is reduced by 20.3 %, stress distribution becomes more uniform, and both maximum and average stress decrease by 24.8 % and a noticeable margin, respectively.

Cryogenic drilling has been explored to mitigate thermal damage, yet existing studies predominantly focus on apparent quality metrics such as drilling forces and delamination, while the relationship between cryogenic processes and the hole bearing capacity has remained overlooked. To address this gap, this study establishes a temperature-dependent constitutive model for thermoplastic composites and performs finite element simulations of drilling using the VUMAT subroutine. The combined effects of cooling temperature, spindle speed, and feed rate on drilling forces, hole quality, and static bearing strength are investigated under dry and cryogenic conditions. On this basis, an RSM-based multi-objective optimization model is developed to balance damage suppression, joint reliability, and machining efficiency. The results show that, compared with dry drilling, cryogenic machining increases the drilling forces by 35–50%, improves overall hole accuracy, and reduces burr formation; meanwhile, the bearing strength exhibits a non-monotonic trend, first increasing by ~10% and then decreasing by ~17%, while the delamination factor shows an inverse trend, with an initial decrease followed by an increase. The average relative errors between the optimized model predictions and experimental values for the drilling force and static bearing strength are 9.97% and 13.93%, respectively.

The alteration in crystallinity and interfacial properties around holes in Carbon Fiber Reinforced Polyetheretherketone (CF/PEEK), induced by drilling heat, severely affects structural performance. Therefore, clarifying the impact of drilling heat on hole quality is essential to ensure structural safety and reliability. To address this issue, a crystallinity prediction model for PEEK was established via DSC tests within the sub-forming temperature range (260-360 degrees C). Finite element simulations and experiments of single-pin double-shear, considering the thermal history around the hole, were conducted, analyzing the mechanical response and damage around the hole under load. The results indicate that resin begins to recrystallize when the temperature around the hole exceeds 300 degrees C. A delayed cooling strategy enhances the ultimate tensile load and peri-hole stiffness by controlling the crystallinity and interfacial properties around the hole, with a more pronounced optimization effect at high rotational speeds, reaching up to 16.09% and 74.99%, respectively. The reheat-slow cool coupling strategy proves most effective with the parameter combination n = 7000-9000 r/min and Vf = 10 mm/min. Morphology around the hole and simulation analysis confirm that high rotational speeds and high feed rates induce delamination, fiber damage, and matrix cracks, thereby reducing peri-hole stiffness and load-bearing capacity. The cooling rate influences the damage evolution process by regulating matrix crystallinity and interfacial bonding strength.

To address the shortcomings of existing studies on the cutting mechanisms of carbon fiber reinforced polyphenylene sulfide (CF/PPS)-which largely ignore initial temperature effects and limit simulations to macroscopic isothermal assumptions-this study establishes a mesoscale two-way thermo-mechanical coupled analysis framework incorporating the initial temperature. Combined with orthogonal cutting experiments, it systematically elucidates the effects of initial temperature and fiber orientation (theta) on temperature evolution in the cutting zone and surface quality. The results show that: First, the non-linear thermal softening effect at 155 degrees C significantly reduces and stabilizes the cutting force, and induces an anomalous negative temperature rise phenomenon at the 0 degrees orientation due to matrix softening and smooth chip evacuation. Second, the cutting temperature evolution and surface quality are highly dependent on fiber orientation; while auxiliary preheating improves surface morphology, it severely exacerbates heat accumulation in large-angle orientation regions. Accordingly, a "temperature-fiber orientation synergistic machining strategy" is proposed: prioritizing preheating for regions with theta <= 45 degrees to enhance surface quality, while implementing active localized cooling for regions with theta >= 135 degrees to suppress heat accumulation and delamination defects.

Carbon fiber reinforced polyphenylene sulfide composite (CF/PPS), as a novel FRTP, exhibits temperaturedependent mechanical properties in its matrix. To address this, this study employs a cross-scale modeling approach combining experiments, simulations, and physics-informed neural networks to characterize the temperature-related plastic behavior of CF/PPS and establish a macroscopic mechanical performance prediction model. Key contributions include: 1) Development of a temperature-dependent constitutive model for PPS elastoplastic behavior incorporating progressive hardening and thermal softening, achieving precise identification of hardening parameters. 2) Establishment of an SC-Hill48 yield criterion suitable for CF/PPS, enabling homogenized mechanical behavior characterization and revealing the continuous variation of yield parameters with temperature. 3) Creation of a physics-informed neural network model that efficiently predicts the macroscopic elastoplastic mechanical properties of unidirectional CF/PPS from constituent parameters, with validity demonstrated through laminate tensile/drilling simulations and experiments. This research provides theoretical support and methodological references for modeling temperature-dependent mechanical behavior and engineering applications (e.g., drilling process optimization) of thermoplastic composites.

The crystallisation behaviour of carbon fiber reinforced polyetheretherketone composite (CF/PEEK) is affected by temperature changes during moulding and reprocessing, which ultimately affects their mechanical properties. Current studies do not consider the comprehensive effects of maximum temperature and cooling rate on crystallinity, and they lack prediction models. Therefore, the crystallinity and tensile properties of PEEK under different temperatures and cooling rates were obtained through DSC tests and quasi-static tensile tests, respectively. The influence of processing parameters on crystallisation and mechanical properties was also analysed. In addition, mathematical models for predicting the crystallinity and mechanical properties of PEEK and CF/PEEK were established, and the accuracy of the models was verified by a series of experiments. The results showed that the maximum temperature close to the melting point and the lowest cooling rate resulted in higher crystallinity, optimal mechanical properties and energy-saving. Under the same processing parameters, the crystallinity of the composite was approximately 1.07 times higher than that of the resin. The error of all theoretical calculated values from the prediction models falls within 11.03%, and the crystallinity of the resin can be directly used to predict the mechanical properties of the composites.

The bidirectional thermo-mechanical coupling effect during CF/PEEK drilling significantly impacts the hole quality and component safety, presenting a current challenge in relevant research. To tackle this issue, this paper establishes a constitutive model that incorporates temperature effects, based on quasi-static tensile tests and theoretical analyses of the elastic response, damage criterion, and stiffness reduction of CF/PEEK materials. A drilling thermo-mechanical monitoring platform is set up, and a bidirectional thermo-mechanical coupling model for drilling thermoplastic composites is established using the VUMAT subroutine. A simulation analysis method is proposed, and the reliability of the finite element model is verified. The results indicate that the average absolute errors of the maximum drilling axis force and the highest temperature obtained from the simulation and experiments for all process parameters are 5.255 and 4.34

During the drilling of Carbon Fiber Reinforced Polyetheretherketone (CF/PEEK), the recrystallization in the high-temperature area surrounding the hole can alter the overall performance of the structural component. Therefore, clarifying the impact of hole-making heat on the quality of the hole is crucial to ensuring the safety and reliability of the material. To address this issue, we established the correlation between the cooling rate, crystallinity, and mechanical parameters through static tensile tests and Differential Scanning Calorimetry (DSC) tests. Furthermore, we analyzed the mechanical response and material damage of holes under load by conducting a finite element model and experiments on the single-pin double-shear specimen which consider the thermal history surrounding the holes. The results showed that when the spindle speed increased from 5000 r/min to 7000 r/min, the molten area surrounding the hole expanded, the cooling rate inside the hole decreased, and the average stiffness of the structure improved by 10.2%. Additionally, the ultimate tensile load of the component, as well as the axial stiffness and radial stiffness surrounding the hole, were positively correlated with the spindle speed. The absolute error between the simulation and experimental results did not exceed 7%, demonstrating the reliability of predicting hole-making quality based on the thermal history surrounding the hole.

The pipeline system is crucial for engine stability, necessitating an understanding of pipe joint sealing characteristics under assembly deviations. This study proposes a cross-scale analysis method for predicting assembly sealing with macroscopic deviations. We simulate the assembly process to analyze stress distribution on contact surfaces at the macroscopic scale. We propose an innovative method that replaces the traditional probabilistic model with measured data for sealing analysis. The three-dimensional morphology of the sealing contact surface is measured, and a mesoscale contact simulation is conducted, producing contact stress maps under various axial force conditions. Image processing techniques analyze effective contact areas to predict leakage in weak sealing regions. This method establishes a correlation between macroscopic deviations and mesoscale leakage, validated through assembly sealing experiments.

With the advancement of space technology, satellites are assuming an increasingly pivotal role in various fields. Currently, satellite structures incorporate a growing number of thin-walled parts. During the assembly process, the movement and connection of these parts are prone to generating assembly deviations, which seriously affects the overall assembly quality of the satellites. In order to accurately predict assembly deviations before assembly, this paper introduced an assembly deviation analysis model which took rigid and flexible properties into account. Starting from the attitude adjustment of satellite assembly, the transmission law of manufacturing error was established. Then, the assembly deviation analysis model integrating rigid and flexible properties was established. Finally, the assembly experiment was carried out to verify the effectiveness of the proposed model. The results showed that the average absolute error between the measurement results and calculation results was only 0.032 mm and the maximum absolute error was 0.074 mm. The average solving accuracy of the assembly deviation was 94%. The result fully proved the effectiveness of the proposed model.

The composite structure composed of carbon fiber reinforced composite and titanium alloy is widely used in aviation and aerospace fields. Due to the different physical properties of these two materials, in the process of lamination, it is easy to appear the pore in the direction of thickness is inconsistent, which is called double-step pore defects (DSPD). The existence of DSPD will undoubtedly have an impact on the connection performance of components, but there is no clear conclusion on the extent of its impact on the connection performance, which restricts the improvement of the pore quality evaluation system, and may cause a waste of resources or leave a safety hazard for aircraft. To solve this problem, the finite element model was established to analyze the initial damage phenomenon of the component with DSPD during bolt tightening. The influence of DSPD on the static strength and fatigue properties of the component was studied by designing experiments, and the failure mechanism was revealed from the perspectives of failure form and damage evolution. The purpose of this study is to provide guidance for the improvement of the evaluation standard and the high quality and stability of the lamination component.

The durability of bolted composite joints has long been a significant concern within the field. However, the specific influence of transverse vibration relaxation on bolted composite joints has not been extensively studied. This study aims to investigate the effects of transverse vibration relaxation on bolted composite joints. A series of transverse vibration experiments were conducted to investigate the effect of initial preload, displacement load, and lubrication position on bolt preload relaxation. Additionally, tensile tests were performed on composite joints after relaxation and without relaxation to evaluate mechanical properties quantitatively. A finite element model was established to reveal the mechanism of damage evolution. The results indicate that displacement load and thread lubrication have the most significant influence on bolt preload relaxation. The clamping force of the composite structure generated by the smaller preload force has a limited effect on damage suppression during the tensile process. The relaxation of bolt preload can be effectively reduced by increasing the initial preload properly. The tensile strength of composite laminated structures with 10%, 22%, and 32% relaxation (10.4 kN initial preload) decreased by 5%, 6%, and 11%, respectively. Transverse vibration relaxation affects the tensile strength of composite structures, which is caused by the decay of preload. In contrast, the damage to the hole wall of the connection domain caused by transverse vibration almost does not affect the bearing capacity of the composite joints. Overall, this research contributes to the understanding of bolted composite joints’ durability by uncovering the novel effects of transverse vibration relaxation and providing valuable insights for design and optimization strategies in composite joint applications.

Large size is one of the typical characteristics of aircraft panel components. These characteristics are prone to deformation during assembly process, which affects the assembly quality of aircraft panel seriously. Therefore, to solve this problem, a combined prediction model is proposed for predicting panel assembly deformation. Firstly, the prediction model of skin assembly deformation based on Adam optimized BP neural network algorithm is proposed to solve the normal deformation of key characteristics in skin assembly stage. Then, a panel assembly deformation prediction model based on substructure is established. The prediction results derived from the skin assembly stage are taken as the input of the prediction model of stringer assembly stage. Thereby, the combined prediction model is established. Finally, a panel assembly experiment is conducted to validate the combined model. By comparing the predicted results coming from the model and the results from assembly experiment, it can be found out that the average absolute error of aircraft panel assembly deformation is 0.155 mm. Therefore, this study provides a reliable and efficient new approach for the assembly deformation prediction of aircraft panel.

The interference-fit size has a significant effect on the riveted lap joints of CFRP/Al alloy laminates. The requirements for the interference-fit size are different because of the strengthening of heterogeneous materials. However, in the riveting process of CFRP/Al alloys, the heterogeneous laminates lead to poor structural strength because of the different interference-fit size requirements. Therefore, differently assembled riveting molds are designed to acquire a novel interference-fit size, and the tensile test is adopted to evaluate their tensile properties. In addition, the fracture failure of CFRP/Al alloy laminate riveted lap joints is observed with an ultra-depth-of-field microscope. Finally, the best assembly type is identified as the trapezoid riveting mold combined with an arc riveting die, and the sidewall intersection angle of the trapezoid riveting mold is 66°, which could achieve a suitable interference-fit size and a better mechanical performance.

The heavy metallic fasteners and undetectable defects in the joining area have long posed challenges for composite structures. In this study, we propose a composite fastener that incorporates a nano-coating on the thread and embeds magnet in the shank. Based on magneto-mechanical effect, a physics-guided neural network (PGNN) is proposed to establish a correlation between magnetic density and bearing stress. For the strengthening effect of nano-coating, the bearing strength of composite joint reinforced by nano-coated composite fastener reaches 196, representing a 26.5% increase compared to the composite joint reinforced by an un-coated fastener. When all composite joint fails, the nano-coated fastener shears off at the shank, whereas the un-coated fastener shears off at the thread. The predicted bearing stress from PGNN can reasonably quantify the real stress situation of testing joints. The maximum prediction error of the PGNN is merely 18.9 MPa, which is 51.7% lower than that of a neural network (NN) without physical guidance. Compared with the NN, the PGNN shows the abilities of reducing prediction randomness, better convergence, and higher prediction accuracy. This research presents a possible substitute for metallic fasteners to reduce the composite structure weight and monitor structure health.