Due to their mechanical load-bearing and functional wave transmission, adhesively bonded joints of carbon fiber–quartz fiber composites have been widely used in the new generation of stealth aviation equipment. However, the curing defects, caused by deviations between the process environment and the setting parameters, directly affect the service performance of the joint during the curing cycle. Therefore, the thermophysical parameter evolution of adhesive films was analyzed via dynamic DSC (differential scanning calorimeter), isothermal DSC and TGA (thermal gravimetric analyzer) tests. The various prefabricating defects within the adhesive layer were used to systematically simulate the impacts of void defects on the tensile properties, and orthogonal tests were designed to clarify the effects of the curing process parameters on the joints’ bonding performance. The results demonstrate that the J-116 B adhesive film starts to cure at a temperature of 160 °C and gradually forms a three-dimensional mesh-bearing structure. Furthermore, a bonding interface between the J-116 B adhesive film and the components to be connected is generated. When the curing temperature exceeds 200 °C, both the adhesive film and the resin matrix thermally degrade the molecular structure. The adhesive strength weakens with an increasing defect area ratio and number, remaining more sensitive to triangle, edge and penetration defects. By affecting the molecular structure of the adhesive film, the curing temperature has a significant impact on the bonding properties; when the curing degree is ensured, the curing pressure directly impacts the adhesive’s performance by influencing the morphology, number and distribution of voids. Conversely, the heating rate and heat preservation time have minimal effects on the bonding performance.

In some cases, the value of clothing pressure on the human body is more than desired which results in discomfort, especially for those designed for infants, aged people and professionals. In order to reduce the pressure so that it is at a comfortable level, this investigation developed a knitted fabric with ultra-low modulus based on the geometrical deformation mechanism. Experimental results show that the tensile deformation is due to the geometrical deformation and physical deformation. This paper focuses on the former, which is greatly improved by reknitting of the unroved yarn with remarkable crimps. The tensile results indicate that compared with the normally rib-knitted fabric, which the elongation of the reknitted fabric reaches at 50%, the tensile modulus decreases by 96%. It is proved that increasing the geometrical deformation is an effective method for reducing the elastic modulus. In addition, the pressure of men's cotton socks and ladies' stocking and tights on skin is measured. Experimental results show that the value of the pressure is as high as 2.959 kPa, which far exceeds the comfort pressure threshold of 0.667 kPa. Furthermore, this paper also studies the application of this developed reknitted fabric. The study of geometrical deformation mechanism-based ultra-low-modulus fabrics with knitting of the yarn with remarkable crimps has important significance, and provides scientific guidance for improving clothing pressure.

In order to characterize the process-induced distortions of 3D thin shell composites with complex shape, the multilayered biaxial weft knitted (MBWK) fabric reinforced high-performance composite helmet was selected as the research object, and the 3D laser scanning machine was used to scan the helmet surface, then the 3D scanning data was compared with the CAD model to evaluate the deformation. The results and discussion indicated that the conventional method was workable, but the speed of convergence was slow and the calculation results were easy to drop into local optimization. According to detailed analysis, a measurement method focusing on the principle of “Feature Distance” was developed. The measurement results shown that this method can not only give accurate results, but also reduce working procedure and greatly save the computing resources, which is proved to be a feasible approach for the deformation measurement foundation of 3D thin shell textile composites.

The epoxy-bonded joint between carbon-fiber-reinforced bismaleimide (CF-BMI) and quartz-fiber-reinforced bismaleimide (QF-BMI) composites can meet the structure–function integration requirements of next-generation aviation equipment, and the structural design of their bonding zones directly affects their service performance. Hence, in this study, the carbon-fiber-reinforced bismaleimide composite ZT7H/5429, the woven quartz-fiber-reinforced bismaleimide composite QW280/5429, and epoxy adhesive film J-116 were used as research materials to investigate the influence of the bonding area size on the mechanical properties, and this study proposes a novel design methodology combining radial basis function (RBF) neuron machine learning with the NSGA-II algorithm to enhance the mechanical properties of the bonded components. First, a finite element simulation model considering 3D hashin criteria and cohesion was established, and its accuracy was verified with experiments. Second, the RBF neuron model was trained using the finite element tensile strength and shear strength data from various adhesive layer parameter combinations. Then, the multi-objective parameter optimization of the surrogate model was accomplished through the NSGA-II algorithm. The research results demonstrate a high consistency between the finite element simulation results and experimental outcomes for the epoxy-bonded CF/QF-BMI composite joint. The stress distribution of the adhesive layers is similar under the different structural parameters of adhesive films, though the varying structural dimensions of the adhesive layers lead to distinct failure modes. The trained RBF neuron model controls the prediction error within 2.21%, accurately reflecting the service performance under various adhesive layer parameters. The optimized epoxy-bonded CF/QF-BMI composite joint exhibits 16.1% and 11.2% increases in the tensile strength and shear strength, respectively.

The mechanical properties of fiber-reinforced composites are highly dependent on the local fiber orientation. In this study, a low-cost yarn orientation reconstruction approach for the composite components’ surface was built, utilizing binocular structured light detection technology to accomplish the effective fiber orientation detection of composite surfaces. It enables the quick acquisition of samples of the revolving body shape without blind spots with an electric turntable. Four collecting operations may completely cover the sample surface, the trajectory recognition coverage rate reached 80%, and the manual verification of the yarn space deviation showed good agreement with the automated technique. The results demonstrated that the developed system based on the proposed method can achieve the automatic recognition of yarn paths of views with different angles, which mostly satisfied quality control criteria in actual manufacturing processes.

Curing pressure plays a critical role in controlling void evolution and improving the manufacturing quality of carbon fiber-reinforced polymer (CFRP) composites during autoclave processing. To gain deeper insight into this pressure–void relationship, a theoretical void-growth equation was employed as a reference framework to clarify the influence of curing pressure on void evolution. Based on this finding, 21 groups of autoclave curing experiments were designed using a nonlinear pressure gradient from 0 to 0.6 MPa. Void morphology and spatial distribution under different curing pressures were characterized by X-ray computed tomography, while the effects of void characteristics on mechanical behavior were evaluated through short-beam shear tests and scanning electron microscopy (SEM) observations. In addition, an FC-Transformer neural network model was developed to predict void morphology evolution and the corresponding mechanical properties during autoclave curing. The results show that increasing curing pressure significantly reduces porosity and transforms void morphology from elongated, clustered features to a more uniform near-spherical distribution. As porosity decreases, the Interlaminar Shear Strength (ILSS) of the composite increases markedly. These findings provide both experimental and data-driven support for understanding pressure-controlled void evolution and optimizing autoclave curing processes for CFRP composites.