【新文速递】2024年4月29日复合材料SCI期刊最新文章

   

今日更新：Composite Structures 1 篇，Composites Part A: Applied Science and Manufacturing 1 篇，Composites Science and Technology 1 篇

Composite Structures

Multi-scale collaborative prediction of optimal configuration for carbon fiber woven composites based on deep learning neural networks

Zefei Wang, Changcai Zhao, Zhuoyun Yang, Keqi Wang, Guojiang Dong, M.D. Starostenkov

doi:10.1016/j.compstruct.2024.118165

基于深度学习神经网络的碳纤维机织复合材料优化构型多尺度协同预测

Since carbon fiber woven composite panels contain complex fiber bundle structures, the aim of this paper is to investigate the factors influencing the macro-equivalent properties and topological flexibility of composite panels resulting from variations in fiber bundle configuration. Firstly, the structural topology optimization design of carbon fiber composite plates was achieved using a multi-scale modeling approach with the variable density method. Nonlinear relationships between carbon fiber bundle configuration parameters and macroscopic equivalent properties, as well as macroscale topological flexibility, were determined using deep learning methods, respectively. The maximum error in the predicted values of the deep learning network model does not exceed 0.5% when compared with the results of the finite element calculations. In addition, utilizing deep learning networks to predict macro-equivalent properties of composites can significantly decrease computational time. The impact of varying the number of filaments in fiber bundles and the spacing of the fiber bundles on the flexibility of the topology was determined using a deep learning network. The optimal topological flexibility, along with its corresponding fiber bundle spacing and number of fiber bundle filaments, are globally searched within the design domain using an adaptive genetic algorithm. The study in this paper can provide a reference for designing fiber bundle configurations of composites for practical applications.

由于碳纤维编织复合板含有复杂的纤维束结构，本文旨在研究纤维束结构变化对复合板宏观等效性能和拓扑柔韧性的影响因素。首先，采用变密度法的多尺度建模方法，实现了碳纤维复合材料板的结构拓扑优化设计;利用深度学习方法分别确定了碳纤维束构型参数与宏观等效性能以及宏观拓扑柔性之间的非线性关系。与有限元计算结果相比，深度学习网络模型预测值的最大误差不超过0.5%。此外，利用深度学习网络预测复合材料的宏观等效特性可以显著减少计算时间。利用深度学习网络确定了不同纤维束中长丝数量和纤维束间距对拓扑灵活性的影响。采用自适应遗传算法在设计域内全局搜索最优拓扑柔性，以及相应的纤维束间距和纤维束细丝数。本文的研究可为实际应用中的复合材料纤维束构型设计提供参考。

Composites Part A: Applied Science and Manufacturing

Resistivity model of carbon fiber cloth considering the effect of interlayer contact resistance

Tomoyoshi Horie, Rikuto Miyake, Yoshikazu Tanaka, Nobuyuki Kamihara, Daiki Matsuyama, Kiyoka Takagi, Mikio Muraoka

doi:10.1016/j.compositesa.2024.108232

考虑层间接触电阻影响的碳纤维布电阻率模型

A resistivity model of a plain-woven carbon fiber dry cloth that considers the interlayer contact resistance is proposed for induction heating and welding analysis of carbon fiber reinforced thermoplastics. Although the cloth, which is regarded as a single-layer thin plate, does not exhibit homogeneous resistivity, homogenized effective thin-shell resistivity is required to analyze the thin-shell complex structures of aircraft. The influence of the direction angle and strip width on the effective resistivity can be expressed using this model. The mechanism for the generation of a large difference in resistivity between the 0° and 45° directions of the cloth is clarified. The proposed model is verified through comparison with experimental results. According to the induction heating experiments, the overall temperature distribution of the cloth is predicted well by the shell finite element analysis using the effective resistivity.

提出了一种考虑了层间接触电阻的平织碳纤维干布电阻率模型，用于碳纤维增强热塑性塑料的感应加热和焊接分析。虽然被视为单层薄板的碳纤维干布并不表现出均匀的电阻率，但在分析飞机的薄壳复杂结构时需要均匀化的有效薄壳电阻率。利用该模型可以表示方向角和带宽对有效电阻率的影响。该模型阐明了在布的 0° 和 45° 方向上产生较大电阻率差异的机理。通过与实验结果的对比，验证了所提出的模型。根据感应加热实验，利用有效电阻率进行的壳体有限元分析很好地预测了布的整体温度分布。

Composites Science and Technology

Preparation of integrated carbon fiber stitched fabric reinforced (SiBCN) ceramic/resin double-layered composites for ablation resistance, thermal insulation and compression resistance performance

Chunlei Xia, Weihua Xie, Songhe Meng, Bo Gao, Jinrui Ye

doi:10.1016/j.compscitech.2024.110629

 

综合碳纤维缝合织物增强(SiBCN)陶瓷/树脂双层复合材料的制备及其抗烧蚀、保温和抗压性能

This paper reports a novel integrated carbon fiber fabric reinforced ceramic/resin double-layered composite. The composites were prepared by a low energy consumption process of stratified impregnate pyrolysis. The double-layered composites consist of an amorphous SiBCN ceramic upper layer and a SiBCN resin bottom layer. In the ablation experiment, the temperature of the composite material's ablated surface exceeded 1850 °C, while the temperature of the backside remained below 70 °C within 60 s of ablation. Even after 90 s of ablation, the backside temperature was still significantly lower than the theoretical application temperature of SiBCN resin. The ablated surface has no visible pits or cracks. The maximum mass ablation rate and line ablation rate were -0.01259 mg/s and 0.000933 mm/s, respectively. High-density amorphous SiBCN ceramic layer effectively play the role of high temperature and ablation resistance. In addition, the compressive strengths of the composites before and after ablation were above 340 MPa. Integrated composites that combine anti-ablation, insulation, and compression resistance have potential in aerospace applications.

本文报道了一种新型集成碳纤维织物增强陶瓷/树脂双层复合材料。采用低能耗的分层浸渍热解工艺制备复合材料。双层复合材料由非晶SiBCN陶瓷上层和SiBCN树脂底层组成。在烧蚀实验中，复合材料烧蚀表面温度超过1850℃，而烧蚀后60 s内背面温度保持在70℃以下。即使烧蚀90 s后，背面温度仍明显低于SiBCN树脂的理论应用温度。烧蚀表面没有可见的凹坑或裂纹。最大质量消融速率和线消融速率分别为-0.01259 mg/s和0.000933 mm/s。高密度非晶SiBCN陶瓷层有效地发挥了耐高温和抗烧蚀的作用。烧蚀前后复合材料的抗压强度均在340 MPa以上。综合抗烧蚀、绝缘和抗压缩的复合材料在航空航天领域具有应用潜力。