This review examines the application of aramid fiber-reinforced plastics (AFRPs) in the aerospace industry, highlighting their significance in enhancing aircraft performance. Aramid fibers, such as Kevlar^® and Twaron^®, have emerged as key materials due to their exceptional tensile strength, low density, and thermal stability. However, challenges persist in manufacturing, durability, and multifunctionality. This paper evaluates the latest advancements in AFRP, focusing on how molecular structure, interfacial engineering, and manufacturing innovations influence performance. It addresses questions on improving adhesion, efficient manufacturing methods, enhancing durability under extreme conditions, and developing multifunctional AFRP. By analyzing breakthroughs from 2020 to 2025 and proposing targeted solutions, this review aims to help AFRP meet the demands of future aerospace systems.

In aeronautical applications, composite additive manufacturing (CAM) is transforming aircraft design by enabling unprecedented lightweighting and functional integration. However, industrial adoption remains limited due to insufficient understanding of the complex interplay among materials, processes, designs, and performance. Existing reviews lack an integrated analytical framework; thus, this study pioneers a closed-loop “Material-Process-Design-Performance (MPDP)” framework for critical analysis. We systematically categorize aerospace-relevant composite systems and core AM technologies, emphasizing their interactions and constraints. Crucially, we demonstrate how AM design bridges material limitations and performance breakthroughs. Validation via aerospace case studies confirms the framework’s efficacy and reveals core bottlenecks: performance consistency, quality control, and certification gaps. We argue that transitioning from isolated optimization to collaborative co-optimization of materials, processes, and design is essential for CAM’s advancement. Finally, we propose a strategic development pathway to guide future research and industrial translation, particularly for next-generation aircraft and spacecraft systems.

While magnesium alloys have garnered attention for their lightweight properties across diverse applications, their susceptibility to corrosion presents a formidable challenge. Recent years have witnessed the emergence of machine learning (ML) as a formidable tool for predicting and augmenting material properties, notably corrosion resistance. This comprehensive review investigates the latest advancements and hurdles in utilizing ML techniques to investigate the corrosion behavior of magnesium alloys. This article delves into a spectrum of ML algorithms, encompassing artificial neural networks, support vector machines, and random forests, elucidating their roles in predicting corrosion rates, morphologies, and other corrosion-related characteristics in magnesium alloys. Furthermore, it underscores the pivotal challenges and opportunities within this field, such as data quality, model interpretability, and model transferability. Finally, it examines the potential of ML methods in the conception and enhancement of magnesium alloys endowed with superior corrosion resistance. This review aspires to offer valuable insights into harnessing ML’s potential for optimizing magnesium alloy designs with heightened corrosion resistance, a facet of paramount importance across diverse industries, including the automotive, aerospace, and biomedical sectors. By addressing the challenges inherent in using ML to forecast corrosion rates, including data limitations and the intricacies of corrosion mechanisms, ML stands poised to emerge as a potent instrument for advancing the development of corrosion-resistant materials.

Dissimilar weld joints of QP980 and TRIP800 were fabricated by a fibre laser. The microstructure and mechanical properties of dissimilar weld joints were studied. The tensile properties of weld joints were not influenced by the narrow softening zone with a less reduction in hardness of ∼25 HV from the QP980, which contributed to fracture in TRIP800. The DIC images showed that the location of maximum strain was changed from the QP980 side to TRIP800 side during the tensile test of QP980 (1.2 mm)–TRIP800 (1.4 mm) joints. The dissimilar weld joints were characterised by the limited dome height test (LDH). The LDH values were varied with the punch pole location, as a result of the softening zone and non-uniform microstructure on the heat-affected zone of joints.

The Ni-based coatings were deposited on the 1Cr10Mo1NiWVNbN steel by using laser cladding process. The microstructure and properties of the coatings interface area were investigated by OM (Optical Microscopy), SEM (Scanning Electron Microscope), XRD (X-Ray Diffraction) microhardness test and EDS (Energy Spectrum Analysis) analysis. The results show that the bonding condition of the coatings interface is different in the monolayer and the trilayer. The monolayer coatings have a small dilution area. The dilution rate in a coating layer increases by layers. The scale of ferrite (α) phase increases with the layer increases. The surface cladding quality of a monolayer is better than that of the trilayer coatings. The width of the interface increases with the increase of the layer. The width of the interface region in the trilayer coatings increases significantly. The microhardness of the interface zone is much higher than that in the coatings zone and the substrate zone. The microhardness of trilayer coatings is higher than that of the monolayer.