Applied Composite Materials

Wang, Yujie; Yang, Qiang; Shen, Zhengyuan; Meng, Songhe; Guo, Zhengyou; Xie, Weihua

doi: 10.1007/s10443-026-10488-9pmid: N/A

The C/SiC composite T-joint is a typical hot structure of hypersonic vehicles, yet its behavior and reusability under repeated loading has not been fully understood. This study targets this typical structure by designing representative loading scenarios, integrating experimental tests and numerical simulations to achieve the analysis of structural mechanical response. The findings indicate that failure modes under monotonic loading exhibit pronounced directional dependence: horizontal tensile loading primarily causes dual rivet line rupture of the outer skin, while tensile loading in the perpendicular direction induces competing failure modes involving interfacial delamination and structural fracture. During cyclic loading (reused conditions), the damage accumulation dynamics were revealed: the maximum historical load defines the damage boundary, whereas the accumulation of irreversible strain linearly reflects stiffness degradation. Despite experiencing approximately 17.26% stiffness reduction, the structure’s residual stress release during cyclic loading results in a 10% increase in ultimate strength. These results showed that C/SiC structure can be reused and its damage can be reflected by the maximum strain. This paper lays foundations for structural reuse and health monitoring.

Wang, Xiaoqiang; Zhang, Xu; Chen, Xingling; Chen, Yuhan; Zhao, Tingdi; Liu, Xinhua; Ma, Chengkun; Lu, Shaowei; Zhang, Lu; Hushvaktov, Hakim; Chen, Yuxiang

doi: 10.1007/s10443-026-10489-8pmid: N/A

The response mechanisms and performance evolution of a new Double-Double (DD) laminate configuration during hygrothermal aging remain unclear. This limits its application in high-humidity environments. Two configurations were designed for glass fiber-reinforced DD laminates: DD-A, which is equivalent in in-plane stiffness, and DD-D, which is equivalent in bending stiffness. Tests were conducted under accelerated water bath aging conditions at 80 °C. Moisture absorption, dynamic mechanical properties, and post-aging mechanical properties were characterized. The saturated moisture absorption rate of DD laminates differed little from that of Quad laminates, but the diffusion coefficient was lower. After saturation, the tensile strength retention of DD-A laminates was 18.5% higher than that of Quad laminates. In the three-point bending test, the strength retention of DD-D was over 13% higher than that of the Quad laminate. However, in the open-hole tensile test, the Quad performed better than both DD variants. These contrasting trends suggest that the uniform DD structure enhances resistance to hydrothermal under uniform loading but is more susceptible to damage when geometric discontinuities impair matrix-dominated load transfer.

Wang, Qian; Liu, Huanyu; Jia, Caixia; Li, Zhixin

doi: 10.1007/s10443-026-10492-zpmid: N/A

Fiber-reinforced polymer (FRP) composites play a pivotal role in aerospace, automotive manufacturing, and medical industries due to their exceptional specific strength and lightweight properties. However, traditional manufacturing optimization faces challenges such as inefficiency and high costs. Machine learning (ML), with its data-driven predictive capabilities, is emerging as a powerful tool in this field. This paper systematically reviews the latest applications of ML in the manufacturing processes of fiber-reinforced polymer composites, covering its role in critical thermoset manufacturing processes (e.g., autoclave curing, liquid composite molding, filament winding) and thermoplastic manufacturing processes (e.g., additive manufacturing, injection molding). The analysis indicates that, despite significant progress, the application of ML in composite manufacturing continues to face fundamental challenges, including the complexity of integrating ML models into real-time control loops, the scarcity of high-quality industrial datasets, and insufficient model generalization across different material systems. Therefore, future directions should focus on developing hybrid models with physics-informed fusion, establishing cross-industry benchmark datasets, and advancing edge computing solutions for adaptive manufacturing. By addressing information fragmentation and uncovering scenario-based insights, this review aims to guide the field toward a more systematic and highly reliable development path.Graphical Abstract[graphic not available: see fulltext]

Wright, Jacey; Sikes, Morgan; Richard, Kai; Sheldon, Peter; Sattar, Siavash

doi: 10.1007/s10443-026-10491-0pmid: N/A

As fused filament fabrication (FFF) fiber-reinforced composites are increasingly adopted in structural and load-bearing applications, understanding their fracture behavior is essential for reliable design. This study investigates the conditional fracture toughness (\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\:K_Q$$\end{document}) and apparent energy release rate (\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\:{G}_{Q}$$\end{document}) of glass-fiber-reinforced polyamide composites (PA6 + GF30) fabricated via FFF, with emphasis on the effects of raster orientation, notch preparation method, and specimen geometry. Four raster architectures (0°, 45°, 90°, and concentric) were evaluated using Single Edge Notch Bending (SENB) and Compact Tension (CT) specimens containing either printed notches or post-process mechanically cut notches. Full-field strain evolution and crack propagation were characterized using in situ Digital Image Correlation (DIC). The measured conditional fracture toughness values ranged from 1.35 to 5.73 MPa√m, while the apparent energy release rates ranged from 5.35 to 31.82 kJ/m². The results revealed a consistent change in orientation-dependent fracture ranking between printed and mechanically cut notch conditions, demonstrating that manufacturing-induced notch geometry strongly influences crack initiation and propagation behavior. In printed-notch specimens, the raster configuration with filaments aligned parallel to the notch direction, 0° printing orientation, exhibited the highest apparent fracture resistance due to local perimeter-wall reinforcement and localized reinforcement and improved load transfer near the notch tip. In contrast, mechanically cut notches produced higher fracture resistance in the 90° and concentric configurations by promoting crack deflection, interlayer engagement, frictional sliding, and distributed energy dissipation. The apparent energy release rate exhibited greater sensitivity to raster architecture and damage evolution than the fracture toughness, indicating that energy-based fracture metrics are more responsive to the distributed and mesostructure-dependent fracture behavior characteristic of FFF composites. The results demonstrate that fracture characterization of FFF fiber-reinforced composites requires simultaneous consideration of raster architecture, notch preparation method, and specimen geometry.

Han, Xiao; Zhou, Zikai; Zheng, Zhikang; You, Chao; Chen, Xihui; Xiguang, Gao; Song, Yingdong

doi: 10.1007/s10443-026-10481-2pmid: N/A

Ceramic matrix composites in aerospace thermal components endure high-temperature fatigue loading where interfacial degradation is critical. However, existing identification methods relying on single hysteresis loop features often lack reliability, as they fail to fully capture the complex interfacial damage state, leading to inconsistent results. This study proposes a machine learning approach integrating multiple hysteresis loop features. Theoretical sample points covering the experimental data range of multiple hysteresis features were generated based on the shear-lag model and Latin hypercube sampling. An optimized artificial neural network established the mapping between features and interfacial shear stress. By incorporating component parameters derived from tensile curves and experimental hysteresis loop data tested at 1400 °C into the data-driven model, the degradation law of the interfacial shear stress was identified. This enabled prediction of hysteresis features and fatigue life, with maximum prediction errors below 10% for all features. The method demonstrates superior accuracy over single-feature approaches, confirming its effectiveness for interfacial degradation assessment in high-temperature applications.

Wang, Xuezhi; Wang, Hanying; Cong, Ming; Wang, Qijia; Hou, Ning; Ma, Shujuan; Wang, Minghai

doi: 10.1007/s10443-026-10490-1pmid: N/A

Continuous silicon carbide fiber-reinforced silicon carbide matrix composites (SiCf/SiC) have become key for high-temperature components within advanced aero-engine hot sections due to exceptional hardness, superior oxidation resistance, outstanding thermal stability, and excellent corrosion resistance. However, due to its heterogeneous structure, this material results in processing-induced damage, including matrix cracking, edge chipping, fiber distortion, and fiber pull-out, concurrently leading to accelerated tool wear. To address the aforementioned issues, this study employed an electroplated diamond grinding wheel and combined it with ultrasonic-assisted grinding (UAG) technology. The influences of different process parameters on grinding force, surface integrity, and wheel wear were comparatively evaluated for UAG and conventional grinding (CG). The findings demonstrate that UAG markedly diminishes the grinding forces, improves surface integrity, and mitigates wheel wear. At a feed rate of 200 mm/min, a spindle speed of 4000 r/min, and a grinding depth of 0.1 mm, the normal force and tangential force under UAG were lowered by 31.97% and 11.89%, respectively. With ultrasonic parameters set at 19 kHz frequency and 4 μm amplitude, tangential and normal grinding force components decreased by 4.75% and 8.10%, respectively, relative to CG. Furthermore, UAG effectively suppressed surface defects such as fiber burrs and edge chipping. Analysis of grinding wheel wear further indicated that UAG has the potential to improve grinding wheel service performance by mitigating abrasive grain pull-out and reducing wheel loading. Based on the experimental results, the combination of UAG with an elevated spindle speed, a low feed rate, and shallow grinding depth can serve as an effective approach for achieving low-damage machining of SiCf/SiC composites. Theoretical insights and practical guidelines are presented in this study for high-performance precision grinding of SiCf/SiC.