It is known that carbon fibre–reinforced aluminium laminate is the third generation of fibre metal materials. This study investigates the response of carbon fibre–reinforced aluminium laminates (CARALL) under tensile loading and three-point bending tests, which evaluate the damage initiation and propagation mechanism. The 2D Hashin and 3D Hashin VUMAT models are used to analyse and compare each composite layer for finite element modelling. A bilinear cohesive contact model is modelled for the interface failure, and the Johnson cook model describes the aluminium layer. The mechanical response and failure analysis of CARALL were evaluated using load versus deflection curves, and the scanning electron microscope was adopted. The results revealed that the failure modes of CARALL were mainly observed in the aluminium layer fracture, fibre pull-out, fracture, and matrix tensile fracture under tensile and flexural loading conditions. The 2D Hashin and 3D Hashin models were similar in predicting tensile properties, flexural properties, mechanical response before peak load points, and final failure modes. It is highlighted that the 3D Hashin model can accurately reveal the failure mechanism and failure propagation mechanism of CARALL.

Herein, we report a BiVO ₄ quantum dot (QDs)-decorated BiPO ₄ nanorods 0D/1D heterojunction via an in situ growth method.

This study unveils a machine learning (ML)-assisted framework designed to optimize the stacking sequence and orientation of carbon fiber-reinforced polymer (CFRP)/metal composite laminates, aiming to enhance their mechanical properties under quasi-static loading conditions. This work pioneers the expansion of initial datasets for ML analysis in the field by uniquely integrating the experimental results with finite element simulations. Nine ML models, including XGBoost and gradient boosting, were assessed for their precision in predicting tensile and bending strengths. The findings reveal that the XGBoost and gradient boosting models excel in tensile strength prediction due to their low error rates and high interpretability. In contrast, the decision trees, K-nearest neighbors (KNN), and random forest models show the highest accuracy in bending strength predictions. Tree-based models demonstrated exceptional performance across various metrics, notably for CFRP/DP590 laminates. Additionally, this study investigates the impact of layup sequences on mechanical properties, employing an innovative combination of ML, numerical, and experimental approaches. The novelty of this study lies in the first-time application of these ML models to the performance optimization of CFRP/metal composites and in providing a novel perspective through the comprehensive integration of experimental, numerical, and ML methods for composite material design and performance prediction.

Polyimide film (Kapton) is an important polymer material used for the construction of spacecrafts. The performance of Kapton can be degraded for atomic oxygen erosion in space. Commonly used atomic oxygen protective layers have issues such as poor toughness and poor adhesion with the film. In this paper, Kapton/Al₂O₃ nanocomposite films were prepared via an ion exchange method, and the optical properties, mechanical properties, and mechanisms for the change in the mass and microstructure, before and after atomic oxygen exposure, were analyzed. The results show that the deposition of the Kapton/Al₂O₃ surface nanocomposite film prepared via the ion exchange method has no obvious effects on the internal structure and optical transmittance of the Kapton film matrix. The tensile strength and elongation of the prepared film were much higher than those of the pure Kapton film, demonstrating its good flexibility. Scanning electron microscope (SEM) analysis showed that the etching pits had a carpet-like morphology on the composite film surface and were relatively small after atomic oxygen erosion. In contrast with the C–C bond rupture in the oxydianiline (ODA) benzene in Kapton films, the Kapton/Al₂O₃ nanocomposite film mainly destroyed the C=C bond in the pyromellitic dianhydride (PMDA) benzene ring. On exposure to an atomic oxygen environment for a short period, the Kapton/Al₂O₃ nanocomposite film exhibited improved atomic oxygen erosion resistance because the Al₂O₃ layer inhibited atomic oxygen diffusion. With increasing atomic oxygen exposure time, the atomic oxygen diffused into the Kapton matrix via the pores of the Al₂O₃ layer, causing damage to the substrate. This resulted in a detachment of the surface Al₂O₃ layer and exposure of the Kapton matrix, and thereby the atomic oxygen resistance was decreased. The applicability of the ion exchange mechanism of trivalent Al element on the surface modification of the polyimide is explored in this study. The behavior of the Kapton/Al₂O₃ composite film under the atomic oxygen environment of space is investigated, which provides the basis for studying the effects of atomic oxygen on the flexible protective Kapton film.

The two-speed automatic transmission can adjust the drive motor speed of electric vehicles and expand their output torque range. This study proposes a rule-based partitioned gear-shifting strategy for pure electric vehicles equipped with a two-speed dual-clutch transmission, combining economic and dynamic shifting strategies to ensure low energy consumption and strong power. Specifically, fuzzy logic is applied to adaptively modify the partition shifting strategy online, to reduce invalid gearshifts, increase the service life of the transmission, and improve driving comfort. Finally, we compare the economic performance and dynamic performance of pure electric vehicles equipped with a two-speed dual-clutch transmission and a single-speed final drive. The results show that the vehicle equipped with the two-speed dual-clutch transmission has better economic and dynamic performance. In addition, its maximum climbing ability was verified by rig testing. These results prove that the two-speed dual-clutch automatic transmission and the gear-shifting strategy proposed in this study can comprehensively improve the performance of pure electric vehicles.