【新文速递】2024年4月4日复合材料SCI期刊最新文章

   

今日更新：Composite Structures 3 篇，Composites Part A: Applied Science and Manufacturing 1 篇，Composites Part B: Engineering 1 篇，Composites Science and Technology 2 篇

Composite Structures

Failure prediction and optimization for composite pressure vessel combining FEM simulation and machine learning approach

Qingfeng Wang, Huasong Qin, Liyong Jia, Ziyi Li, Guoqiang Zhang, Yushu Li, Yilun Liu

doi:10.1016/j.compstruct.2024.118099

结合有限元模拟和机器学习方法进行复合材料压力容器的失效预测和优化

Failure assessment is one of the fundamental tasks for optimization of composite pressure vessels (CPVs). However, the extensive design space of composites usually leads to costly and repetitive work of failure assessment that hinders the design and optimization of CPVs. In this work, a combined FEM simulation and machine learning approach is used to predict the failure factors R of CPVs for further optimization. CPVs with various design parameters are automatically generated and analyzed in ABAQUS to obtain failure factors under various external loadings. Then, an assembled deep neural network (DNN) namely Multi-DNN is trained to map relations between design parameters and R of CPVs. Further, transfer learning (TL) is introduced to improve the extensibility of our approach by fine-tuning the pre-trained Multi-DNN using scarce data of new design space. Such a TL-Multi-DNN model can precisely predict R of CPVs with new design parameters. Additionally, combining the trained models and genetic algorithm, the optimization of CPVs is carried out with high efficiency and low computational cost. As a result, R of CPV with various given and new design parameters can be estimated directly by the trained models and the optimal layer sequence of CPV can be obtained efficiently.

失效评估是优化复合材料压力容器（CPV）的基本任务之一。然而，由于复合材料的设计空间很大，通常会导致失效评估工作的高成本和重复性，从而阻碍了 CPV 的设计和优化。本研究采用有限元模拟和机器学习相结合的方法来预测 CPV 的失效系数 R，以便进一步优化。在 ABAQUS 中自动生成具有各种设计参数的 CPV，并对其进行分析，以获得各种外部负载下的失效系数。然后，训练组装的深度神经网络（DNN），即 Multi-DNN，以映射 CPV 的设计参数和 R 之间的关系。此外，我们还引入了迁移学习（TL），利用新设计空间的稀缺数据对预先训练好的多重 DNN 进行微调，从而提高我们方法的可扩展性。这样的 TL-Multi-DNN 模型可以精确预测具有新设计参数的 CPV 的 R。此外，将训练好的模型与遗传算法相结合，可以高效率、低计算成本地优化 CPV。因此，经过训练的模型可以直接估算出具有各种给定参数和新设计参数的 CPV R，并高效地获得 CPV 的最佳层序。

Auxetic lattice structures consisting of an enhanced trigram frame unit cell with superior stiffness

Mohammad Bashtani, Ehsan Etemadi, Hong Hu, Mahmoud Moradi

doi:10.1016/j.compstruct.2024.118100

由具有超强刚度的增强型三叉框单元格组成的辅助晶格结构

Superior stiffness is deemed essential for structures intended for several applications. The stiffness enhancement can be achieved by adopting structures that deform based on the stretching-dominated mechanism instead of the bending-dominated mechanism. This paper proposes a novel auxetic unit cell design and two distinct lattice structures dominated by stretching when subjected to quasi-static compressive and tensile loadings. The structures are manufactured via the fused deposition modeling (FDM) 3D printing method with the use of polylactic acid (PLA) filament as the patent material. The investigation also includes theoretical analyses and finite element (FE) simulations, all yielding consistent results. Response Surface Methodology (RSM) is also employed to understand the influence of three geometric parameters. Besides, single-objective optimization is conducted to maximize the stiffness through geometry design, and multi-objective approaches are implemented to improve the stiffness while simultaneously reducing relative density. Finally, different unit cells, which deform based on the stretching-dominated mechanism, are selected to represent their respective classifications, enabling a comparison of their potential stiffness capacity with the designed structures in this study. Remarkably, it is highlighted that the proposed unit cells and structures outperform others in terms of specific stiffness, making them a promising choice for use in structural engineering.

对于一些应用领域的结构来说，卓越的刚度是必不可少的。通过采用以拉伸为主的变形机制而不是以弯曲为主的变形机制的结构，可以提高刚度。本文提出了一种新颖的辅助晶胞设计和两种不同的晶格结构，在承受准静态压缩和拉伸载荷时以拉伸为主。这些结构是通过熔融沉积建模（FDM）三维打印方法制造的，使用聚乳酸（PLA）长丝作为专利材料。调查还包括理论分析和有限元（FE）模拟，所有结果都是一致的。研究还采用了响应面方法（RSM）来了解三个几何参数的影响。此外，还进行了单目标优化，通过几何设计最大限度地提高刚度，并采用多目标方法在提高刚度的同时降低相对密度。最后，选择了基于拉伸主导机制变形的不同单元格来代表其各自的分类，从而将其潜在的刚度能力与本研究中设计的结构进行比较。值得注意的是，所提出的单元格和结构在特定刚度方面优于其他结构，使其成为结构工程中的理想选择。

Multiscale mechanical analysis of 3D nanoparticle-reinforced metal-based composites considering interface effects

Yongchao Zhang, Xiaotian Wang, Bin Li, Fangxin Wang, Xiaofan Gou

doi:10.1016/j.compstruct.2024.118092

考虑界面效应的三维纳米粒子增强金属基复合材料的多尺度力学分析

Nanoparticle-reinforced metal-based composites (NRMCs) have gained recognition for their remarkable interface effects and their demonstration of superior mechanical properties. To effectively account for interfacial effects and precisely assess the mechanical properties of NRMCs with arbitrarily complex internal structures, our study employs the principle of minimum energy and leverages the Gurtin-Murdoch interface theory to devise a finite element interface element that comprehensively considers the interface effect of NRMCs. Utilizing this novel interface element, we construct diverse NRMCs model and subject them to single-axis tension and compression simulations. Our findings reveal that the developed finite element model accurately captures the internal stress distribution of the NRMCs. Both the particle modulus and interfacial effects exert influence on the Young’s modulus of the model. When the particle modulus is small, interfacial effects have a large impact on Young’s modulus. Additionally, the effect of interface residual stress on Young’s modulus is strongly dependent on the direction of external loading. The developed finite element model offers a scientific approach for accurately predicting the mechanical performance of NRMCs.

纳米粒子增强金属基复合材料（NRMC）因其显著的界面效应和卓越的力学性能而获得广泛认可。为了有效考虑界面效应并精确评估具有任意复杂内部结构的 NRMC 的力学性能，我们的研究采用了最小能量原理，并利用 Gurtin-Murdoch 界面理论设计了一种有限元界面元素，全面考虑了 NRMC 的界面效应。利用这种新型界面元素，我们构建了多种 NRMC 模型，并对其进行了单轴拉伸和压缩模拟。研究结果表明，所开发的有限元模型能准确捕捉 NRMCs 的内部应力分布。颗粒模量和界面效应都会对模型的杨氏模量产生影响。当颗粒模量较小时，界面效应对杨氏模量的影响较大。此外，界面残余应力对杨氏模量的影响与外部加载方向密切相关。所开发的有限元模型为准确预测非弹性体材料的机械性能提供了一种科学方法。

Composites Part A: Applied Science and Manufacturing

Ultra-high strength metal matrix composites (MMCs) with extended ductility manufactured by size-controlled powder and spherical cast tungsten carbide

Yiqi Zhou, Li Wang, Decheng Kong, Bowei Zhang, Tingting Liu, Yu Yan, Li Zhang, Xiaogang Li, Dirk Engelberg, Chaofang Dong

doi:10.1016/j.compositesa.2024.108194

通过尺寸控制粉末和球形铸造碳化钨制造具有扩展延展性的超高强度金属基复合材料（MMC

The main challenge of particle-reinforced metal matrix composites (MMCs) is balancing strength and ductility. This research uses type 420 stainless steel and spherical cast tungsten carbide (WC/W2C) with a similar powder size and range as raw powders to manufacture laser powder bed fusion (LPBF) 420 + 5 wt% WC/W2C MMCs. LPBF 420 + 5 wt% WC/W2C MMCs contain austenite, martensitic, and W-rich carbides (WC/W2C, FeW3C, M6C, and M7C3) from nanometre to micrometre scale. The well-balanced composition creates a crack-free reaction layer between reinforced particles and the matrix. This reaction layer consists of two distinct layers, depending on the element composition. The LPBF 420 + 5 wt% WC/W2C MMCs achieved an excellent compressive strength of ∼5.5 GPa and a considerable fracture strain exceeding 50 %. The underlying mechanisms for the improved mechanical properties are discussed, providing further insight to advance the application of MMCs via additive manufacturing.

颗粒增强金属基复合材料（MMC）的主要挑战是平衡强度和延展性。本研究使用粉末尺寸和范围相似的 420 型不锈钢和球形铸造碳化钨（WC/W2C）作为原料粉，制造激光粉末床熔融（LPBF）420 + 5 wt% WC/W2C MMC。LPBF 420 + 5 wt% WC/W2C MMC 包含奥氏体、马氏体和富含 W 的碳化物（WC/W2C、FeW3C、M6C 和 M7C3），从纳米级到微米级。均衡的成分在增强颗粒和基体之间形成了无裂纹反应层。根据元素成分的不同，反应层由两个不同的层组成。LPBF 420 + 5 wt% WC/W2C MMC 的抗压强度达到 5.5 GPa，断裂应变超过 50%。本文讨论了提高机械性能的基本机制，为通过增材制造推进 MMC 的应用提供了进一步的见解。

Composites Part B: Engineering

Enalaprilat-loaded polyphenol nanoparticle composite hydrogel promotes myocardial protection after acute infarction

Zhiyu Zhao, Hong Xu, Annuo Li, Hongxia Pu, Peiyi She, Gaocan Li, Yunbing Wang

doi:10.1016/j.compositesb.2024.111430

依那普利拉负载多酚纳米粒子复合水凝胶促进急性心肌梗死后的心肌保护

Heart failure (HF) has been increasing in morbidity and mortality worldwide, and one of the important causes of heart failure is myocardial infarction (MI), in which the coronary arteries are unable to provide sufficient blood flow to myocardial tissues, leading to necrosis and apoptosis, activation of inflammatory responses and neuroendocrine systems such as renin-angiotensin-aldosterone system (RAAS), generation of adverse ventricular remodeling, and finally the development of heart failure. In this paper, a drug-loaded polyphenol nanoparticles (ESaB NPs) containing active Angiotensin-converting enzyme inhibitor (ACEI) drugs enalaprilat and salvianolic acid B have been designed for the pathologic characteristics of acute MI. On this basis, a collagen/fucoidan hydrogel with good bioactivity and mechanical properties has been prepared, and the nanoparticles are incorporated into the aforementioned hydrogel to obtain a composite hydrogel (Gel-ESaB) with multiple synergistic therapeutic functions. The introduction of fucoidan can not only improve the mechanical strength and injectability of the collagen-based hydrogel, but also confer the collagen material with anticoagulant properties that are essential for future clinical applications as cardiovascular biomaterials. While providing mechanical support to the infarct site, the ESaB NPs delivered in situ to the infarct site play a key role in protecting myocardial tissue and restoring cardiac function through ROS scavenging, inflammation alleviation and tissue RAAS inhibitory ability. Both in vitro and in vivo experiments have confirmed the excellent therapeutic ability of Gel-ESaB in the treatment of MI and the prevention of HF after MI, which is expected to be an effective approach for MI treatment.

心力衰竭（HF）在全球的发病率和死亡率不断上升，而心力衰竭的重要原因之一是心肌梗死（MI），心肌梗死时冠状动脉无法为心肌组织提供足够的血流，导致心肌组织坏死和凋亡，激活炎症反应和神经内分泌系统，如肾素-血管紧张素-醛固酮系统（RAAS），产生不良的心室重构，最终发展为心力衰竭。本文针对急性心肌梗死的病理特征，设计了一种含有活性血管紧张素转换酶抑制剂（ACEI）药物依那普利拉和丹酚酸 B 的药物负载多酚纳米颗粒（ESaB NPs）。在此基础上，制备了具有良好生物活性和机械性能的胶原蛋白/褐藻糖胶水凝胶，并将纳米颗粒融入上述水凝胶中，得到了具有多种协同治疗功能的复合水凝胶（Gel-ESaB）。褐藻糖胶的引入不仅能提高胶原蛋白水凝胶的机械强度和可注射性，还能赋予胶原蛋白材料抗凝特性，这对未来作为心血管生物材料的临床应用至关重要。ESaB NPs 在为心肌梗塞部位提供机械支撑的同时，通过清除 ROS、缓解炎症和抑制组织 RAAS 的能力，在保护心肌组织和恢复心脏功能方面发挥了关键作用。体外和体内实验均证实了 Gel-ESaB 在治疗心肌梗死和预防心肌梗死后高房颤动方面的卓越治疗能力，有望成为治疗心肌梗死的有效方法。

Composites Science and Technology

Bacterial cellulose-based composite films with liquid metal/graphene synergistic conductive pathways for superior electromagnetic interference shielding and Joule heating performance

Yuanhang Ge, Lequan Wang, Bo Hu, Hongbin Lu, Yizhen Shao

doi:10.1016/j.compscitech.2024.110582

 

具有液态金属/石墨烯协同导电途径的细菌纤维素基复合薄膜，可实现卓越的电磁干扰屏蔽和焦耳加热性能

Modern integrated electronics are in great demand for high-performance electromagnetic interference (EMI) shielding materials with exceptional mechanical properties. Liquid metal (LM) has demonstrated great potential in EMI shielding by its superior electrical conductivity. However, its real-world EMI application is limited by the poor compatibility, insulating oxide shells, and unpredictable leakage. Here, graphene oxide (GO) is used to encapsulate LM to form LM@GO microdroplets dispersion, and bacterial cellulose (BC) is applied to construct a biocompatible fabric network. Moreover, GO is in-situ reduced by hydrazine vapor, which generates synergistic LM/reduced graphene oxide (rGO) conductive pathways with the aid of roll-in process, obtaining flexible LM/rGO/BC (LGB) composite film with outstanding electrical conductivity of 4.5 × 104 S/m and exceptional shielding effectiveness of 64.0 dB. The rGO sheets and BC network demonstrate layered structure after roll-in process, effectively impeding the leakage and oxidation of LM and achieving a tensile strength up to 62.9 MPa of LGB films. Meanwhile, the LGB films exhibit exceptional Joule heating performance, and the stable surface temperature reaches 110 °C with high stability and reliability when the applied voltage is 4 V. This work provides a feasible engineering approach to prepare LM-based films for applications in EMI shielding and wearable electronics.

现代集成电子设备对具有优异机械性能的高性能电磁干扰（EMI）屏蔽材料的需求量很大。液态金属 (LM) 凭借其卓越的导电性，在 EMI 屏蔽方面展现出巨大的潜力。然而，由于兼容性差、绝缘氧化物外壳和不可预测的泄漏，液态金属在 EMI 领域的实际应用受到了限制。在这里，氧化石墨烯（GO）被用来封装 LM，形成 LM@GO 微滴分散体，细菌纤维素（BC）被用来构建生物相容性织物网络。此外，GO 被肼蒸气原位还原，借助卷入工艺产生协同的 LM/ 还原氧化石墨烯（rGO）导电通路，从而获得柔性 LM/rGO/BC (LGB) 复合薄膜，其导电率高达 4.5 × 104 S/m，屏蔽效果高达 64.0 dB。卷入工艺后，rGO 片材和 BC 网络呈现分层结构，有效阻止了 LM 的泄漏和氧化，使 LGB 薄膜的拉伸强度达到 62.9 兆帕。同时，LGB 薄膜表现出优异的焦耳加热性能，在施加 4 V 电压时，其表面温度稳定在 110 ℃，具有很高的稳定性和可靠性。这项工作为制备基于 LM 的薄膜提供了一种可行的工程方法，可应用于 EMI 屏蔽和可穿戴电子设备。

Self-catalysed frontal polymerisation enables fast and low-energy processing of fibre reinforced polymer composites

Jeroen Staal, Baris Caglar, Véronique Michaud

doi:10.1016/j.compscitech.2024.110584

 

自催化正面聚合可实现纤维增强聚合物复合材料的快速、低能耗加工

Frontal polymerisation has the potential to bring unprecedented reductions in energy demand and process time to produce fibre reinforced polymer composites. Production of epoxy-based fibre reinforced polymer parts with high fibre volume content, commonly encountered in industry, is however hindered by the heat sink created by the fibres and the mould, overcoming the heat output of the chemical reaction, thus preventing front propagation. We propose a novel self-catalysed frontal polymerisation manufacturing method based on the integration of thin resin channels in thermal contact with the composite stack as a strategy for low-energy production of high fibre volume fraction polymer composites without the need for a continuous energy input. Frontal polymerisation inside the resin channel proceeds faster and preheats the fabric stack, thus catalysing the process. Parts with up to 60% fibre content are successfully produced independently of the sample thickness. Fillers added within the resin channels provide means to tailor the frontal polymerisation process kinetics. The parts have a significantly higher glass transition temperature than those produced in a conventional oven, and comparable mechanical properties while energy consumption is reduced by over 99.5%.

在生产纤维增强聚合物复合材料的过程中，正面聚合有可能带来前所未有的能源需求和工艺时间的减少。然而，由于纤维和模具所产生的散热片超过了化学反应所产生的热量，从而阻碍了正面聚合的进行。我们提出了一种新型自催化正面聚合制造方法，该方法基于与复合材料堆栈热接触的薄树脂通道的整合，是一种无需持续输入能量即可低能耗生产高纤维体积分数聚合物复合材料的策略。树脂通道内的正面聚合反应进行得更快，并预热织物堆栈，从而催化这一过程。可成功生产出纤维含量高达 60% 的部件，而不受样品厚度的影响。在树脂通道内添加填料可以调整正面聚合过程的动力学。这些部件的玻璃化温度明显高于传统烘箱生产的部件，机械性能也不相上下，而能耗却降低了 99.5% 以上。