【新文速递】2025年9月14日复合材料SCI期刊最新文章_ACT_Mechanical_Additive

【新文速递】2025年9月12日复合材料SCI期刊最新文章

今日更新：Composite Structures 2 篇，Composites Part A: Applied Science and Manufacturing 2 篇，Composites Part B: Engineering 1 篇Composite StructuresEffect of strain gradient on micro-buckling behaviors in biological staggered compositesSiyuan Zhang, Shun Zhu, Tongcheng Zhang, Yanwei Liudoi:10.1016/j.compstruct.2025.119642应变梯度对生物交错复合材料微屈曲行为的影响Biological staggered composites have exceptional mechanical properties and efficient biological functions because of their hierarchical structural characteristics. The staggered structure is among the most common microstructural arrangements in biological composites, where mineral platelets have a high aspect ratio, thus inducing buckling-dominated failure under compression. However, as the scale decreases, the mechanical behavior of staggered structures exhibits significant size effects, yet their trans-scale buckling mechanis ms remain unclear. Therefore, in this paper, strain gradient theory is applied to establish a trans-scale buckling model for staggered structures. An alytical solutions for buckling displacement and stress fields with size-dependent characteristics are obtained, and the regulatory mechanis ms of microstructural features on macroscopic buckling behavior are identified. The results show that strain gradient effects significantly affect the material’s size-dependent behavior. The higher-order stresses in the organic layers dominate the nonlinear variation of critical buckling strength and significantly influence structural stability. Moreover, the buckling resistance performance is synergistically governed by material stiffness, geometric parameters (e.g., aspect ratio, mineral volume fraction), and characteristic length parameters. By tailoring the matching relationship between organic layer thickness and characteristic length parameters, we can optimize the strain gradient effects and interfacial stress distribution, thus providing guidance for the buckling-resistant design of staggered composites. This study deepens the understanding of biological staggered composites’ trans-scale mechanical behavior and provides a theoretical basis for the anti-buckling design of staggered structural composites.生物交错复合材料由于其层次化的结构特点，具有优异的力学性能和高效的生物学功能。交错结构是生物复合材料中最常见的微观结构排列之一，其中矿物血小板具有高长径比，从而在压缩下诱导屈曲为主的破坏。然而，随着尺度的减小，交错结构的力学行为表现出明显的尺寸效应，但其跨尺度屈曲机制尚不清楚。因此，本文采用应变梯度理论建立了交错结构的跨尺度屈曲模型。得到了具有尺寸依赖特征的屈曲位移和应力场的解析解，并确定了微观结构特征对宏观屈曲行为的调控机制。结果表明，应变梯度效应显著影响材料的尺寸依赖行为。有机层内的高阶应力主导了临界屈曲强度的非线性变化，对结构稳定性有显著影响。此外，抗屈曲性能受材料刚度、几何参数（如长径比、矿物体积分数）和特征长度参数的协同影响。通过剪裁有机层厚度与特征长度参数之间的匹配关系，优化应变梯度效应和界面应力分布，为交错复合材料的抗屈曲设计提供指导。本研究加深了对生物交错复合材料跨尺度力学行为的认识，为交错结构复合材料的抗屈曲设计提供了理论依据。TPMS-based PLA/PETG interpenetrating composites: The synergistic enhancement of mechanical properties for bone implantChenyu Wang, Haowen Xue, Qian Wan, Bin Zhou, Xingchen Guo, Yue Lu, Qing Han, Hao Chen, Jincheng Wangdoi:10.1016/j.compstruct.2025.119644基于tpms的PLA/PETG互穿复合材料：骨种植体力学性能的协同增强Maxillofacial bone repair demands implants that match native mechanics, yet conventional designs struggle to balance strength and toughness. We present an interpenetrating phase composite (IPC) that couples a PLA lattice with a PETG network, printed via multimaterial FDM into P-type TPMS architectures with offset thickness d = 0.4–1.2 mm. The IPC shows a synergistic “1 + 1 > 2” effect: versus porous PLA, compressive strength increases by 153–244 %. Finite-element an alysis reveals more uniform stress fields and delayed local buckling under compression. A modified dual-phase Gibson–Ashby model predicts elastic modulus and yield strength across densities and compositions, with good agreement to experiments. Calcein-AM/PI and CCK-8 assays indicate high cell viability and negligible cytotoxicity. ALP activity and Alizarin Red staining support preserved osteogenic potential, while short-term subcutaneous implantation demonstrates favorable tissue responses with neovascularization and collagen remodeling. Simulated body-fluid immersion shows composition-dependent, predictable hydrolytic behavior. Collectively, the rigid–flexible coupling of PLA/PETG IPC TPMS structures yields concurrent gains in strength, toughness, and energy absorption with reassuring biosafety, positioning this platform as a promising option for load-bearing, patient-specific craniofacial implants and offering a practical framework for performance prediction and design optimization.颌面骨修复要求植入物与自然力学相匹配，但传统的设计难以平衡强度和韧性。我们提出了一种互穿相复合材料（IPC），它将PLA晶格与PETG网络耦合，通过多材料FDM打印成偏移厚度为d = 0.4-1.2 mm的p型TPMS结构。IPC表现出协同的“1 + 1 > 2”效应：与多孔PLA相比，抗压强度提高了153-244 %。有限元分析表明，在压缩作用下，应力场更加均匀，局部屈曲延迟。改进的双相Gibson-Ashby模型预测了不同密度和成分的弹性模量和屈服强度，与实验结果吻合良好。Calcein-AM/PI和CCK-8检测显示细胞活力高，细胞毒性可忽略不计。ALP活性和茜素红染色支持保留成骨潜能，而短期皮下植入显示出良好的组织反应，包括新生血管和胶原重塑。模拟的体液浸泡显示了成分依赖的、可预测的水解行为。总的来说，PLA/PETG IPC TPMS结构的刚柔耦合在强度、韧性和能量吸收方面同时获得增益，同时具有可靠的生物安全性，使该平台成为承载、患者特异性颅面植入物的有前途的选择，并为性能预测和设计优化提供了实用的框架。Composites Part A: Applied Science and ManufacturingAchieving deformation coordination and comprehensive performance regulation in copper matrix composites via multiscale modified parallel-connected soft/hard region heterostructure designSiyuan Guo, Xiang Zhang, Chunsheng Shi, Dongdong Zhao, Xudong Rong, Enzuo Liu, Chunnian He, Naiqin Zhaodoi:10.1016/j.compositesa.2025.109296 通过多尺度修正平行连接软/硬区异质结构设计实现铜基复合材料的变形协调和综合性能调节Ingenious multi-level structural design demonstrates a more versatile paradigm than single heterostructure modification to reconcile the strength-ductility and strength-conductivity trade-off in Cu materials. In this study, we explore an innovative parallel-connected heterostructure design strategy for graphene/Cu composites utilizing powder metallurgy. The composite comprises hard micro-regions characterized by ultrafine-grained structures containing intragranular carbon nanoparticles, and soft micro-regions composed of coarse-grained structures with intergranular graphene nanoplatelets encapsulating Al2O3 particles. This well-balanced heterogeneous system achieves exceptional mechanical properties, exhibiting a yield strength of 460 MPa and an ultimate tensile strength of 515 MPa, along with a remarkable elongation-to-failure of 13 % and a toughness of 65.5 MJ/m3. Microstructure characterization and molecular dynamics simulations reveal that coordinated plastic deformation between the micro-regions can be promoted due to the well-bonded interfacial structure. The parallel-connected heterostructure induces prevailing isotropic hardening and moderate kinetic hardening, which dominates the stable deformation process. The crucial role of balanced dislocation propagation capabilities is confirmed in achieving superior mechanical properties in the parallel-connected heterostructure. Furthermore, the quasi-continuous carrier trans mission pathway, created by heterostructure facilitates maintaining high electrical conductivity (90 % IACS). These findings open a novel avenue for enhancing comprehensive performance of composites by simultaneously tailoring the parallel-connected structure and micro-region interfaces.巧妙的多层次结构设计展示了一种比单一异质结构修饰更通用的范式，以协调Cu材料的强度-延性和强度-电导率之间的权衡。在这项研究中，我们利用粉末冶金技术探索了一种创新的石墨烯/铜复合材料并联异质结构设计策略。该复合材料包括以含有晶内碳纳米颗粒的超细晶结构为特征的硬微区和由包裹Al2O3颗粒的晶间石墨烯纳米片组成的粗晶结构的软微区。这种平衡良好的非均质体系具有优异的力学性能，屈服强度为460 MPa，极限抗拉强度为515 MPa，断裂延伸率为13% %，韧性为65.5 MJ/m3。微观结构表征和分子动力学模拟表明，良好结合的界面结构可以促进微区之间的协调塑性变形。平行连接异质结构引起了普遍的各向同性硬化和中度的动力学硬化，主导了稳定变形过程。在平行连接异质结构中，平衡位错扩展能力对获得优异的力学性能起着至关重要的作用。此外，由异质结构产生的准连续载流子传输途径有助于保持高电导率（90 % IACS）。这些发现为同时定制平行连接结构和微区域界面来提高复合材料的综合性能开辟了一条新的途径。A MILP-based topology optimization for cost-effective hybrid fiber-reinforced polymer composites under tensile loadingHuseyin Kosedoi:10.1016/j.compositesa.2025.109291拉伸载荷下基于milp的高性价比混杂纤维增强聚合物复合材料拓扑优化This study proposed a three-stage MILP-based topology optimization framework for cost-effective fiber-reinforced polymer composites under tensile strength constraints. Unlike previous studies relying on experimental procedures, a fully numerical approach is adopted to optimize fiber type, composite thickness (t), and fiber volume fraction (Vf). Model 1 considers fiber selection and t as decision variables. Model 2 introduces Vf as a variable, enhancing design flexibility. Model 3 extends the framework to hybrid composites by optimizing the t and Vf of two fiber types simultaneously. Numerical results indicate that Model 1 effectively identifies the optimal fiber and optimize t to meet tensile strength requirements. Model 2 achieves up to 66 % cost reduction compared to Model 1. Model 3 offers further improvements, reducing costs by up to 41 % relative to Model 2 and by up to 74 % overall compared to Model 1. These findings confirm that topology optimization of microstructural parameters enables high-performance, low-cost composite production. Additionally, a sensitivity a nalysis confirmed the robustness of the proposed MILP-based models under parameter variations, ensuring their reliability for engineering applications. This work constitutes the first MILP-based tensile strength–oriented topology optimization of fiber-reinforced composites, offering a scalable and systematic alternative to experimental-based design strategies.本研究提出了一种基于三阶段混合整数线性规划（MILP）的拓扑优化框架，用于在拉伸强度约束条件下实现成本效益高的纤维增强聚合物复合材料的设计。与以往依赖实验程序的研究不同，本研究采用了一种完全数值化的方法来优化纤维类型、复合材料厚度（t）和纤维体积分数（Vf）。模型 1 将纤维选择和 t 作为决策变量。模型 2 引入 Vf 作为变量，增强了设计的灵活性。模型 3 将框架扩展到混合复合材料，同时优化两种纤维类型的 t 和 Vf。数值结果表明，模型 1 能够有效地识别出最优纤维，并优化 t 以满足拉伸强度要求。模型 2 相较于模型 1 成本降低了多达 66%。模型 3 则进一步改进，相较于模型 2 成本降低多达 41%，相较于模型 1 总体成本降低多达 74%。这些发现证实了对微观结构参数进行拓扑优化能够实现高性能、低成本复合材料的生产。此外，敏感性分析证实了所提出的基于混合整数线性规划（MILP）模型在参数变化下的稳健性，确保了其在工程应用中的可靠性。这项工作构成了首个基于 MILP 的以抗拉强度为导向的纤维增强复合材料拓扑优化，为基于实验的设计策略提供了一种可扩展且系统化的替代方案。Composites Part B: EngineeringDelamination-Informed Lifecycle Decisions: A Dielectric and Machine Learning Framework for Composite Sorting and RecyclingMonjur Morshed Rabby, Tahmid Hasan Oni, Partha Pratim Das, Vamsee Vadlamudi, Ahmed Arabi Hassen, Rassel Raihandoi:10.1016/j.composites b.2025.113007分层知情生命周期决策：复合材料分类和回收的介质和机器学习框架Composite materials are widely used in aerospace, marine, and automotive sectors due to their high strength-to-weight ratio and durability. However, their long-term reliability can be compromised by damage accumulation. Specifically, delamination initiation serves as a precursor to structural failure, which is often difficult to detect during damage inspection. Identifying and sorting delamination initiation in samples not only increases operational safety while providing critical information for end-of-life decisions, which influences both the service life extension value and the efficiency of fiber extraction during recycling. This research addresses two challenges: (1) developing a nondestructive, ex-situ framework to sort composite materials based on damage severity, particularly delamination, and (2) understanding how damage in composites influences resin removal during pyrolysis. Both experimental work and finite element an alysis were performed to predict critical stress levels that are associated with delamination onset. Based on these results, three loading levels 50%, 75%, and 90% of maximum stress, were selected for controlled experiments, generating composite samples with varying extents of damage for machine learning model training. Microscopic imaging of these samples confirmed the damage progression from matrix cracking to delamination, validating the computational predictions. We explored supervised machine learning using dielectric measurements to classify damage states. Preliminary results show an artificial neural network can identify early delamination which is a potential precursor to failure, with 94.44% accuracy on our dataset. A parallel investigation into the effect of damage severity on pyrolysis recycling showed that heavily delaminated samples required significantly less energy for comparable matrix removal than undamaged samples.复合材料因其高强度重量比和耐久性而广泛应用于航空航天、船舶和汽车领域。然而，它们的长期可靠性可能会受到损伤累积的影响。具体来说，分层起始是结构破坏的前兆，在损伤检测中往往难以检测到。识别和分类样品中的分层起始不仅提高了操作安全性，而且为寿命终止决策提供了关键信息，这影响了使用寿命延长值和回收过程中纤维提取的效率。本研究解决了两个挑战：(1)开发一种非破坏性的非原位框架，用于根据损伤严重程度（特别是分层）对复合材料进行分类；(2)了解复合材料中的损伤如何影响热解过程中的树脂去除。进行了实验工作和有限元分析，以预测与分层发生相关的临界应力水平。基于这些结果，选择最大应力的50%、75%和90%三个加载水平进行对照实验，生成不同程度损伤的复合样品用于机器学习模型训练。这些样品的显微成像证实了从基体开裂到分层的损伤过程，验证了计算预测。我们探索了使用介电测量对损伤状态进行分类的监督机器学习。初步结果表明，人工神经网络可以识别早期分层，这是潜在的故障前兆，在我们的数据集上准确率为94.44%。一项关于损伤严重程度对热解循环影响的平行研究表明，严重分层的样品比未损伤的样品需要更少的能量来去除类似的基质。来源：复合材料力学仿真Composites FEM