【新文速递】2023年11月18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 Part B: Engineering 1 篇，Composites Science and Technology 1 篇
Composite Structures
Modeling on the effect of automated fiber placement induced gaps in curved composite laminates
Chen Qisen, Ye Yaoyao, Qian Shimeng, Xu Qiang, Qu Weiwei, Song Xiaowen, Ke Yinglin
doi:10.1016/j.compstruct.2023.117721
曲面复合材料层压板中自动纤维铺放诱导间隙效应的建模
Automated placement technology has been widely used due to its excellent processing adaptability for large composite components with complex geometric structures. Whereas, it still poses challenges to study the effect of manufactured gaps induced by automated placement on curved composite parts. This study proposes a new geometric reconstruction approach based on consolidation and sweeping formation to model manufactured gaps. Additionally, a mechanical model incorporating the reconstructed geometry and the cohesive layer method is established to predict the physical behaviors of curved laminates. The micrograph of automated placement-induced gaps with 90° orientation was used to verify the rationality of the geometric reconstruction approach of gaps forming in the preparation process of composite materials. According to predictions of the proposed mechanical model, the effect of −45° oriented gap defects on curved laminates was validated via four-point bending tests that confirmed alignment between predictions and experimental results for peak load, crack propagation, and failure patterns. Experimental results show that due to the differences in spatial local geometric features, orientations of gap defects would reduce the bending strength to different degrees and significantly affect the failure pattern of curved laminates. Furthermore, curved laminates with wider gaps experience a more considerable reduction in load-bearing capacity.
自动贴装技术因其出色的加工适应性而被广泛应用于具有复杂几何结构的大型复合材料部件。然而，研究自动贴装技术对曲面复合材料部件产生的加工间隙的影响仍是一项挑战。本研究提出了一种基于固结和扫掠形成的新几何重构方法，以模拟制造间隙。此外，还建立了一个包含重建几何形状和内聚层方法的力学模型，以预测曲面层压板的物理行为。利用自动贴片引起的 90° 方向缝隙的显微照片来验证复合材料制备过程中形成缝隙的几何重构方法的合理性。根据提出的力学模型的预测，通过四点弯曲试验验证了-45°取向间隙缺陷对曲面层压板的影响，证实了峰值载荷、裂纹扩展和破坏模式的预测与实验结果之间的一致性。实验结果表明，由于空间局部几何特征的差异，间隙缺陷的取向会在不同程度上降低弯曲强度，并显著影响曲面层压板的破坏模式。此外，间隙较宽的曲面层压板的承载能力下降幅度更大。
Composites Part A: Applied Science and Manufacturing
Effect of atomic oxygen exposure on polybenzoxazine/POSS nanocomposites for space applications
He Yanjun, Suliga Agnieszka, Brinkmeyer Alex, Schenk Mark, Hamerton Ian
doi:10.1016/j.compositesa.2023.107898
原子氧暴露对用于太空应用的聚苯并恶嗪/POSS 纳米复合材料的影响
A new thermoset resin system, based on a polybenzoxazine blend, has been subjected to high ATOX fluence (2.69 ×1021 atom/cm2), equating to a period of 300 days in low Earth orbit. Several baseline tests were carried out on the resin and the addition of POSS decreased ATOX erosion yield by 69% compared with unmodified resin system. SEM and FTIR results confirm that the protection mechanism involves the formation of a silicon-rich surface layer in response to ATOX exposure, shielding the resin below from further erosion and principal components analysis was used to elucidate the degradation mechanism. Carbon fibre reinforced polymer (CFRP) laminates based on the new resin systems were tested for their mechanical properties. The addition of 6 wt% POSS leads to a 50% increase in the energy required to initiate fracture and 41% increase in the energy required to propagate a crack. Mode II fracture toughness is also improved by the addition of POSS (61.5% increase in energy required to initiate a crack and 35.7% increase in energy required to propagate it).
一种基于聚苯并恶嗪混合物的新型热固性树脂系统经受了相当于在低地球轨道上运行 300 天的高 ATOX 通量（2.69 ×1021 原子/平方厘米）的考验。对该树脂进行了多次基线测试，与未改性的树脂系统相比，添加 POSS 后 ATOX 侵蚀率降低了 69%。扫描电子显微镜和傅立叶变换红外光谱结果证实，保护机制包括在暴露于 ATOX 时形成富含硅的表层，从而保护下面的树脂免受进一步侵蚀，并利用主成分分析阐明了降解机制。对基于新树脂体系的碳纤维增强聚合物（CFRP）层压板进行了机械性能测试。添加 6 wt% POSS 后，引发断裂所需的能量增加了 50%，裂纹扩展所需的能量增加了 41%。加入 POSS 后，模态 II 断裂韧性也得到了改善（引发裂纹所需的能量增加了 61.5%，裂纹扩展所需的能量增加了 35.7%）。
Composites Part B: Engineering
Optimization of mechanical properties of multiscale hybrid polymer nanocomposites: A combination of experimental and machine learning techniques
Champa-Bujaico Elizabeth, Díez-Pascual Ana M., Lomas Redondo Alba, Garcia-Diaz Pilar
doi:10.1016/j.compositesb.2023.111099
优化多尺度杂化聚合物纳米复合材料的机械性能：实验与机器学习技术的结合
Machine learning (ML) models provide fast and accurate predictions of material properties at a low computational cost. Herein, the mechanical properties of multiscale poly(3-hydroxybutyrate) (P3HB)-based nanocomposites reinforced with different concentrations of multiwalled carbon nanotubes (MWCNTs), WS2 nanosheets and sepiolite (SEP) nanoclay have been predicted. The nanocomposites were prepared via solution casting. SEM images revealed that the three nanofillers were homogenously and randomly dispersed into the matrix. A synergistic reinforcement effect was attained, resulting in an unprecedented stiffness improvement of 132% upon addition of 1:2:2 wt% SEP:MWCNTs:WS2. Conversely, the increments in strength were only moderates (up to 13.4%). A beneficial effect in the matrix ductility was also found due to the presence of both nanofillers. Four ML approaches, Recurrent Neural Network (RNN), RNN with Levenberg's algorithm (RNN-LV), decision tree (DT) and Random Forest (RF), were applied. The correlation coefficient (R2), mean absolute error (MAE) and mean square error (MSE) were used as statistical indicators to compare their performance. The best-performing model for the Young's modulus was RNN-LV with 3 hidden layers and 50 neurons in each layer, while for the tensile strength was the RF model using a combination of 100 estimators and a maximum depth of 100. An RNN model with 3 hidden layers was the most suitable to predict the elongation at break and impact strength, with 90 and 50 neurons in each layer, respectively. The highest correlation (R2 of 1 and 0.9203 for the training and test set, respectively) and the smallest errors (MSE of 0.13 and MAE of 0.31) were obtained for the prediction of the elongation at break. The developed models represent a powerful tool for the optimization of the mechanical properties in multiscale hybrid polymer nanocomposites, saving time and resources in the experimental characterization process.
机器学习（ML）模型能以较低的计算成本快速准确地预测材料特性。本文预测了不同浓度的多壁碳纳米管（MWCNTs）、WS2 纳米片和海泡石（SEP）纳米粘土增强的多尺度聚（3-羟基丁酸酯）（P3HB）基纳米复合材料的力学性能。纳米复合材料是通过溶液浇注法制备的。扫描电镜图像显示，三种纳米填料均匀、随机地分散在基体中。在添加 1:2:2 wt% 的 SEP:MWCNTs:WS2 后，增强效果达到了前所未有的 132%。相反，强度仅有适度提高（最多 13.4%）。由于两种纳米填料的存在，基体延展性也得到了改善。应用了四种 ML 方法：循环神经网络 (RNN)、RNN 与莱文伯格算法 (RNN-LV)、决策树 (DT) 和随机森林 (RF)。相关系数（R2）、平均绝对误差（MAE）和平均平方误差（MSE）被用作比较它们性能的统计指标。对于杨氏模量，表现最好的模型是具有 3 个隐藏层、每层有 50 个神经元的 RNN-LV，而对于拉伸强度，则是使用 100 个估计器组合和最大深度为 100 的 RF 模型。具有 3 个隐藏层的 RNN 模型最适合预测断裂伸长率和冲击强度，每层分别有 90 个和 50 个神经元。在预测断裂伸长率时，相关性最高（训练集和测试集的 R2 分别为 1 和 0.9203），误差最小（MSE 为 0.13，MAE 为 0.31）。所开发的模型是优化多尺度杂化聚合物纳米复合材料机械性能的有力工具，节省了实验表征过程中的时间和资源。
Composites Science and Technology
Prediction of mechanical properties of 3D tubular braided composites at different temperatures using a multi-scale modeling framework based on micro-CT
Zhang Yuyang, Li Huimin, Liu Xin, Chen Yanhong, Qin Chengwei, Fang Daining
doi:10.1016/j.compscitech.2023.110349
利用基于微计算机断层扫描的多尺度建模框架预测三维管状编织复合材料在不同温度下的力学性能
It is of great significance to establish a real three-dimensional (3D) tubular braided composites mechanical properties prediction model at different temperatures. In this paper, a multi-scale modeling framework based on micro-computed tomography (micro-CT) is adopted to consider the characteristics of the real yarn cross section, fiber shape deviation and internal defects within the matrix after composite formation, and a realistic trans-scale finite element model for 3D tubular braided composite is established. The micro-scale and macro-scale mechanical properties of 3D tubular braided composites at different temperatures are sequentially simulated by using the elastic-plastic damage model considering temperature and the tractor-separation constitutive model. Comparison with experiments shows that temperature significantly affects the mechanical properties. With the increase of temperature, the overall failure degree of the 3D tubular braided composite under axial compressive load increases significantly, its axial compressive strength and modulus decrease significantly, and the post-peak response of the stress-strain curve gradually flattens. The proposed trans-scale model demonstrates high predictive accuracy.
建立真实的三维（3D）管状编织复合材料在不同温度下的力学性能预测模型具有重要意义。本文采用基于微计算机断层扫描（micro-CT）的多尺度建模框架，考虑了复合材料形成后真实纱线截面、纤维形状偏差和基体内部缺陷等特征，建立了真实的三维管状编织复合材料跨尺度有限元模型。利用考虑温度的弹塑性损伤模型和牵引分离构成模型，依次模拟了三维管状编织复合材料在不同温度下的微观尺度和宏观尺度力学性能。与实验的比较表明，温度对力学性能有显著影响。随着温度的升高，三维管状编织复合材料在轴向压缩载荷作用下的整体破坏程度显著增加，其轴向压缩强度和模量显著降低，应力-应变曲线的峰后响应逐渐趋于平缓。所提出的跨尺度模型具有很高的预测精度。
