【新文速递】2025年9月24日复合材料SCI期刊最新文章

   

今日更新：Composite Structures 6 篇，Composites Science and Technology 1 篇

Composite Structures

Numerical investigation of the potential of double-double laminates to measure the interlaminar fracture toughness of multidirectional interfaces

Paulo Pereira, Federico Danzi, Pedro Pinto, Torquato Garulli, Carolina Furtado

doi:10.1016/j.compstruct.2025.119666

测量多向界面层间断裂韧性的双-双层板电位数值研究

The suitability of Double-Double (DD) laminates to assess the interlaminar fracture toughness of multidirectional interfaces in mode I and mode II is investigated numerically through the use of the 3D-Virtual Crack Closure Technique (3D-VCCT)-based and crack propagation ana lyses. The effects of ply thickness, mis match angle, and stacking sequence on the width-wise Energy Release Rate (ERR) distributions are investigated. Four thin-ply DD stacking sequences are an alyzed, revealing low mode mixity and spurious off-axis reaction forces, thanks to laminate homogenization. The stacking sequence [ − Φ / Ψ / Φ / − Ψ ] 10 / / [ Φ / − Ψ / − Φ / Ψ ] 10 , named Staggered 1, is identified as the most promising configuration, with a minimal spurious mode and reduced crack curvature. Further an alysis of ply thickness showed that lower ply thickness and more repetitions improve suitability for interlaminar fracture toughness measurements. Evaluation of Staggered 1 laminate with Ψ = 0 ∘ over several mis match angles (0°–45°) indicated negligible mode mixity, crack curvature, and crack skewness during crack propagation. The findings suggest that DD laminates are promising for assessing interlaminar toughness in multidirectional interfaces if appropriate ply thickness and sublaminate repetitions are used.

采用基于三维虚拟裂纹闭合技术（3D-VCCT）和裂纹扩展分析的方法，对双层（DD）层压板在I型和II型多向界面层间断裂韧性评估中的适用性进行了数值研究。研究了层厚、错配角和堆叠顺序对宽度方向能量释放率（ERR）分布的影响。分析了四种薄层DD堆叠序列，揭示了由于层压均匀化而产生的低模式混合和伪离轴反作用力。堆叠序列[−Φ / Ψ / Φ /−Ψ] 10 / / [Φ /−Ψ /−Φ / Ψ] 10，被认为是最有希望的配置，具有最小的杂散模式和减小的裂缝曲率。进一步分析表明，较低的厚度和较多的重复次数提高了层间断裂韧性测量的适用性。对Ψ = 0°下多个失配角（0°-45°）的交错1层压板的评估表明，在裂纹扩展过程中，模态混合、裂纹曲率和裂纹偏度可以忽略不计。研究结果表明，如果采用适当的层厚和次层重复次数，DD层合板有望用于评估多向界面的层间韧性。

Efficient multi-objective optimization of composite microstructures for thermal protection systems

Idan Distelfeld, Shmuel Osovski

doi:10.1016/j.compstruct.2025.119679

热防护系统复合微结构的高效多目标优化

This paper presents a surrogate model-based approach for multi-objective optimization of composite representative volume elements under thermo-mechanical loading. The RVE architecture, inspired by metallic honeycomb structures with inclined fibers, allows tailoring the anisotropy of thermal and mechanical properties. A parametric model is an alyzed using Finite Element An alysis with periodic boundary conditions and homogenization theory. The 10-dimensional design space is sampled using Latin Hypercube Sampling, and simulated to calculate effective elastic mod uli and thermal conductivity. This dataset is used to train a shallow neural network (SNN) model, offering computational efficiency and rapid exploration of complex design spaces. The SNN is employed in a multi-objective optimization process using the NSGA-II algorithm, allowing simultaneous optimization of elastic properties, thermal conductivity, and density. This reveals trade-offs between competing objectives, with resulting Pareto frontiers providing crucial information for informed design decisions. The method demonstrates a fast, accurate, and flexible approach for optimizing composite architectures. Combining advanced modeling techniques with efficient optimization algorithms, this work contributes to developing lightweight, multifunctional materials for aerospace, automotive, and other demanding applications. The approach has significant implications for optimizing composite materials in complex structures, advancing the state-of-the-art in composite materials research and providing a powerful tool for high-performance material design.

提出了一种基于代理模型的复合材料代表性体积元热-机械载荷多目标优化方法。RVE建筑的灵感来自金属蜂窝结构和倾斜纤维，可以定制热性能和机械性能的各向异性。采用周期边界条件和均匀化理论对参数化模型进行了有限元分析。采用拉丁超立方采样法对10维设计空间进行采样，并对其进行模拟，计算有效弹性模量和导热系数。该数据集用于训练浅层神经网络（SNN）模型，提供计算效率和对复杂设计空间的快速探索。SNN采用NSGA-II算法进行多目标优化，可同时优化弹性性能、导热性和密度。这揭示了竞争目标之间的权衡，由此产生的帕累托边界为明智的设计决策提供了关键信息。该方法为优化复合体系结构提供了一种快速、准确、灵活的方法。将先进的建模技术与高效的优化算法相结合，这项工作有助于开发用于航空航天、汽车和其他苛刻应用的轻质多功能材料。该方法对复杂结构复合材料的优化、复合材料研究的进步以及高性能材料的设计都具有重要意义。

Influence of post-processing on the Mode I and Mode II static fracture toughness of additively manufactured carbon fiber composites

Zane Forbes, Johannes Reiner, Xiaobo Yu, Garth Pearce, Mathew W. Joosten

doi:10.1016/j.compstruct.2025.119664

后处理对增材碳纤维复合材料I型和II型静态断裂韧性的影响

The layer-by-layer fabrication process of additively manufactured (AM) composites can introduce interlaminar defects that may impact interlaminar fracture toughness. This study investigates the effect of post-processing on the interlaminar fracture toughness of AM continuous carbon fibre/polyamide composites produced via fused filament fabrication. Post-processing samples in an oven at 150 °C enhanced the initiation interlaminar fracture toughness by 9.12 % in Mode I and 17.94 % in Mode II, while the propagation interlaminar fracture toughness increased by 18.14 % and 9.37 %, respectively. Optical microscopy revealed that post-processing reduced the void content, from 6.24 % to 1.12 %. Scanning electron microscopy highlighted that the fracture surfaces of the post-processed samples showed a reduction in void-related defects. These findings demonstrate that the interlaminar fracture toughness of AM composites can be improved through post-processing consolidation, which enhances the materials resistance to crack initiation and propagation.

增材制造（AM）复合材料的逐层制备工艺会引入层间缺陷，影响层间断裂韧性。研究了后处理对增材制造碳纤维/聚酰胺连续复合材料层间断裂韧性的影响。在150 °C的烘箱中处理后的样品，在模式I和模式II下，初始层间断裂韧性分别提高了9.12 %和17.94 %，而在模式II下，扩展层间断裂韧性分别提高了18.14 %和9.37 %。光学显微镜显示，后处理降低了孔隙率，从6.24 %降至1.12 %。扫描电镜显示，后处理样品的断口表面显示与空洞相关的缺陷减少。研究结果表明，通过后处理固结可以提高增材复合材料的层间断裂韧性，增强材料抗裂纹萌生和扩展的能力。

Stress-gradient model for tensile damage in orthotropic materials

Franziska Seeber, Ani Khaloian-Sarnaghi, Elena Benvenuti, Fabian Duddeck, Jan-Willem van de Kuilen

doi:10.1016/j.compstruct.2025.119674

正交异性材料拉伸损伤的应力梯度模型

Reliable finite element simulation of orthotropic-dependent failure mechanis ms is crucial for understanding the mechanical behavior and optimizing engineered composites and fiber-based materials. Such materials behave brittle under tension and strongly depend on the orthotropic material orientation. Existing non-local models can reproduce brittle fracture for isotropic materials but, in most cases, they are based on the equivalent strain concept for damage initiation, which is unsuitable for orthotropic materials. This contribution introduces a stress-based non-local damage model enhanced with an implicit gradient formulation of the failure criteria. A localizing non-local length is assumed to avoid any pathological broadening of the damage band. The methodology introduces direction-dependent damage variables driven by non-local stress-based damage criteria and can thus distinguish different failure modes. The verification and validation are shown on numerical and experimental benchmark examples. The implicit gradient-based non-local damage approach allows mesh-independent results. Furthermore, it does not require a priori known crack paths and makes it possible to simulate complex failure modes. Perspectively, its effective implementation in the commercial software Abaqus and combination with other constitutive laws, e.g. to account for plasticity or moisture, make it an attractive tool for describing the mechanical material behavior of orthotropic materials, such as wood and fiber-composites.

可靠的正交各向异性相关破坏机制的有限元模拟对于理解力学行为和优化工程复合材料和纤维基材料至关重要。这种材料在拉伸作用下表现为脆性，并且强烈依赖于材料的正交异性取向。现有的非局部模型可以再现各向同性材料的脆性断裂，但在大多数情况下，它们是基于等效应变的损伤起裂概念，这并不适合于正交各向异性材料。这一贡献引入了一种基于应力的非局部损伤模型，增强了失效准则的隐式梯度公式。假设一个局部的非局部长度以避免任何病理性的损伤带拓宽。该方法引入了由非局部应力损伤准则驱动的方向相关损伤变量，从而可以区分不同的破坏模式。通过数值算例和实验基准算例进行了验证和验证。隐式基于梯度的非局部损伤方法允许网格无关的结果。此外，它不需要先验已知的裂纹路径，使模拟复杂的破坏模式成为可能。从长远来看，它在商业软件Abaqus中的有效实现以及与其他本构律的结合，例如，考虑塑性或水分，使其成为描述正交异性材料（如木材和纤维复合材料）的机械材料行为的有吸引力的工具。

Data-driven failure criteria prediction in composite wing boxes using machine learning

Dario Magliacano, Vincenza Tufano, Annalisa Letizia, Bernardo Sessa, Matteo Filippi

doi:10.1016/j.compstruct.2025.119675

基于机器学习的复合材料翼盒数据驱动故障准则预测

Modern transport aircraft exploit composite wing-box architectures to maximize strength-to-weight efficiency, yet the through-thickness damage states that govern air-worthiness remain difficult to quantify by closed-form an alysis. A fully labeled benchmark data set, comprising 1017 finite-element (FE) simulations of a Cirrus-class carbon-fiber wing-box (nine undamaged cases plus 1008 damage scenarios obtained by combining 28 intralaminar damage locations with four severity levels for each of nine orthotropic materials) is therefore generated. Five classical failure criteria (Max-Stress, Tsai–Wu, Tsai–Hill, Hashin and Christensen) are evaluated at the most-stressed element and adopted as supervised-learning targets. Two regression surrogates, Random Forest (RF) ensembles and Support Vector Regression (SVR), are trained on the material-property vector and damage descriptors. A material-wise leave-one-out (LOO) cross-validation strategy demonstrates that the RF model attains a root-mean-square error RMSE = 0.076 for the Hashin index, while preserving RMSE ≤ 0.15 on the Max-Stress index. The resulting RF surrogate furnishes near-instant predictions of composite failure indices and provides a reliable machine-learning benchmark for operational wing-box health assess ment.

现代运输机采用复合材料翼盒结构来最大限度地提高强度重量比效率，但控制适航性的全厚度损伤状态仍然难以通过封闭形式分析来量化。因此，生成了一个完全标记的基准数据集，包括1017个cirruss级碳纤维翼盒的有限元（FE）模拟（9个未损坏情况加上1008个损坏情况，这些情况是通过结合28个层内损伤位置，每种材料的四个严重级别获得的）。在最大应力单元上评估了5个经典失效准则（Max-Stress、Tsai-Wu、Tsai-Hill、Hashin和Christensen），并将其作为监督学习目标。两个回归代理，随机森林（RF）集成和支持向量回归（SVR），在材料属性向量和损伤描述符上进行训练。材料方面的留一（LOO）交叉验证策略表明，RF模型在Hashin指数上获得均方根误差RMSE = 0.076，而在最大应力指数上保持RMSE≤0.15。由此产生的RF代理提供了近乎即时的复合故障指数预测，并为操作翼箱健康评估提供了可靠的机器学习基准。

Manufacture, process simulation, modelling and testing of thick-walled thermoset fibre-polymer composite laminates — A review

Richard Protz, Eckart Kunze, Tim Luplow, Linus Littner, Jonas Drummer, Sebastian Heimbs, Marc Kreutzbruck, Bodo Fiedler, Maik Gude

doi:10.1016/j.compstruct.2025.119678

厚壁热固性纤维聚合物复合层压板的制造、工艺模拟、建模和试验。综述

Thick-walled thermoset fibre-reinforced polymer (FRP) composites present unique challenges across their manufacturing, simulation, modelling, and testing processes. This paper provides a comprehensive overview of the current challenges and research needs associated with thick-walled FRP, particularly in light of their growing relevance in demanding application domains, such as wind energy. It is important to emphasise that the designation of a laminate as thick-walled is determined not solely by its nominal thickness, but also by the direction of the applied load. In particular, laminates subjected to compressive loading are typically considered thick-walled from a wall thickness of 4 mm or greater. While conventional manufacturing techniques remain applicable to thick-walled FRPs, process adaptations, such as adjusted curing cycles or alternative curing methods, are necessary to mitigate manufacturing defects, e.g. residual stresses induced by inhomogeneous curing due to local temperature overshoot. Modelling of the curing process and accurate prediction of residual stress development remain key areas of ongoing research with significant gaps in understanding. The influence of the wall thickness can also be seen in quasi-static and impact tests. Self-heating must be taken into account in fatigue tests and must be incorporated into future guidelines for the design of thick-walled FRP structures. While well-established non-destructive testing (NDT) techniques are generally applicable, their effectiveness is reduced with increasing laminate thickness due to limitations in resolution. The findings underscore the need for continued interdisciplinary efforts to refine processing and evaluation methods for thick-walled FRP composites.

厚壁热固性纤维增强聚合物（FRP）复合材料在其制造、模拟、建模和测试过程中提出了独特的挑战。本文全面概述了当前与厚壁FRP相关的挑战和研究需求，特别是考虑到它们在风能等苛刻应用领域的日益相关性。需要强调的是，厚壁层压板的名称不仅取决于其标称厚度，还取决于所施加载荷的方向。特别是，承受压缩载荷的层压板通常被认为是厚壁的，壁厚为4毫米或更大。虽然传统的制造技术仍然适用于厚壁frp，但工艺调整，如调整固化周期或替代固化方法，对于减轻制造缺陷是必要的，例如，由于局部温度超调而引起的不均匀固化引起的残余应力。固化过程的建模和残余应力发展的准确预测仍然是正在进行的研究的关键领域，在理解上存在重大差距。在准静态和冲击试验中也可以看到壁厚的影响。疲劳试验中必须考虑到自热，并且必须将其纳入厚壁FRP结构设计的未来指南中。虽然成熟的无损检测（NDT）技术普遍适用，但由于分辨率的限制，其有效性随着层压厚度的增加而降低。研究结果强调需要继续跨学科的努力，以完善加工和评价方法的厚壁FRP复合材料。

Composites Science and Technology

Cellulose Nanofibers-Enabled Interfacial Engineering for Thermally Conductive Composites with Superior Mechanical Durability

Wen-yan Wang, Yan-ji Yin, Yuan-chao Jiang, Rui Han, Min Nie

doi:10.1016/j.compscitech.2025.111390

 

具有优异机械耐久性的导热复合材料的纤维素纳米纤维界面工程

Cellulose nanofibers (CNFs), derived from renewable biomass, offer exceptional mechanical properties, a high aspect ratio, and abundant surface hydroxyl groups, making them highly attractive for polymer composite functionalization. In this study, CNFs are employed as both dispersing and reinforcing agents to address the dual challenges of filler aggregation and poor interfacial adhesion in nylon-based thermally conductive composites. By leveraging their strong hydrogen bonding capability, CNFs not only enable the uniform dispersion of boron nitride (BN) fillers in aqueous systems but also facilitate the construction of robust interfacial networks within the polymer matrix. Using a simple vacuum-assisted filtration and compression molding strategy, we fabricated laminated composites featuring highly aligned BN structures. This unique architecture promotes the formation of efficient thermal pathways, resulting in an in-plane thermal conductivity of 4.5 W·m-1·K-1 at 24.5 wt% BN—an 1857% enhancement over pure nylon. Simultaneously, the CNF-induced interfacial reinforcement leads to excellent mechanical strength and fatigue resistance, with the composite retaining 92% of its thermal conductivity and 85% of its tensile strength after 100,000 bending cycles. These findings demonstrate the significant potential of CNF-assisted interfacial engineering for developing high-performance, thermoplastic-based thermal management materials suitable for flexible electronics and other advanced applications.

纤维素纳米纤维（CNFs）来源于可再生生物质，具有优异的机械性能、高长径比和丰富的表面羟基，使其在聚合物复合功能化方面具有很高的吸引力。在这项研究中，CNFs被用作分散剂和增强剂，以解决尼龙基导热复合材料中填料聚集和界面粘附不良的双重挑战。CNFs利用其强大的氢键能力，不仅使氮化硼（BN）填料在水体系中均匀分散，而且有助于在聚合物基体中构建坚固的界面网络。使用简单的真空辅助过滤和压缩成型策略，我们制造了具有高度排列BN结构的层压复合材料。这种独特的结构促进了高效热通道的形成，在24.5% wt% bn时，其面内导热系数为4.5 W·m-1·K-1，比纯尼龙提高了1857%。同时，cnf诱导的界面增强带来了优异的机械强度和抗疲劳性能，在10万次弯曲循环后，复合材料保持了92%的导热系数和85%的抗拉强度。这些发现证明了cnf辅助界面工程在开发适用于柔性电子和其他先进应用的高性能热塑性热管理材料方面的巨大潜力。