Composites Part C Open Access最新文献_第5页

Coelacanth-scale inspired thin-ply composites for load-bearing applications 用于承载应用的腔棘鱼级启发薄层复合材料

IF 7 Q2 MATERIALS SCIENCE, COMPOSITES

Composites Part C Open Access

Pub Date : 2025-10-01 Epub Date: 2025-10-18 DOI: 10.1016/j.jcomc.2025.100667

Marcel Neubacher , Farida Touni , Kohei Yamada , Masaaki Nishikawa , Bodo Fiedler

Thin-ply composites are known for their superior in-situ strength and manufacturing quality, offering higher unnotched tensile and compressive strengths compared to conventional laminates. However, their damage suppression capability leads to increased notch sensitivity, where the delamination and matrix cracking mechanisms are suppressed. As a result, thin-ply laminates are limited in their use in critical load-bearing applications. To address this, bio-inspired Bouligand structures, defined by their helical fibre arrangements, have shown promise in reducing notch sensitivity through helicoidal matrix cracking and stress redistribution. This study explores the mechanical performance of partial Bouligand layups derived from biological fibre architectures observed on coelacanth fish scales, where fibrils reorient under load. An analytical stiffness-based optimization was performed to match the mechanical properties of the conventional [0°, ± 45°, 90°] (50 %, 40 %, 10 % load introduction layup used in bolted and riveted aircraft structures, while integrating the partial Bouligand structure. The weights of the two-layer fibres (30 gsm and 60 gsm) were investigated, resulting in different pitch and stack angles. Tensile and bearing tests were conducted to evaluate the influence of the partial Bouligand structure on bearing sensitivity. The results indicate that bio-inspired fibre orientation can improve load redistribution and damage tolerance in thin-ply laminates, making them compatible for off-axis and notched applications.

薄层复合材料以其优越的原位强度和制造质量而闻名，与传统层压板相比，提供更高的无缺口拉伸和抗压强度。然而，它们的损伤抑制能力导致缺口灵敏度增加，其中分层和基体开裂机制被抑制。因此，薄层层压板在关键承重应用中的使用受到限制。为了解决这个问题，由螺旋纤维排列定义的仿生Bouligand结构显示出通过螺旋基质开裂和应力重新分配来降低缺口灵敏度的希望。本研究探讨了部分Bouligand铺层的力学性能，这些铺层来源于在腔棘鱼鳞片上观察到的生物纤维结构，其中纤维在负载下重新定向。为了匹配螺栓和铆接飞机结构中使用的传统[0°，±45°，90°]（50%,40%，10%载荷引入铺设）的力学性能，同时集成部分Bouligand结构，进行了基于刚度的分析优化。研究了两层纤维（30gsm和60gsm）的重量，导致不同的间距和堆叠角度。通过拉伸和承载试验，评价了局部布利甘结构对承载灵敏度的影响。结果表明，仿生纤维取向可以改善薄层复合材料的载荷再分配和损伤容限，使其适用于离轴和缺口应用。

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引用次数: 0

Recycling multi-layer plastic packaging waste as core material for aluminum composite panels in sustainable building applications 回收多层塑料包装废弃物作为铝复合板在可持续建筑应用中的核心材料

IF 7 Q2 MATERIALS SCIENCE, COMPOSITES

Composites Part C Open Access

Pub Date : 2025-10-01 Epub Date: 2025-10-16 DOI: 10.1016/j.jcomc.2025.100666

Sokleng Srou , Ponlapath Tipboonsri , Supaaek Pramoonmak , Walanrak Poomchalit , Anin Memon

This study focuses on the fabrication and comprehensive evaluation of aluminum composite panels (ACPs) using low-density polyethylene (LDPE) and aluminum foil derived from beverage carton packaging waste, with kraft paper removed, as a sustainable core material. Mechanical, microstructural, and environmental properties of the panels were systematically investigated under various compression molding conditions to determine optimal processing parameters. Compression temperatures ranged from 190 °C to 210 °C and pressures from 10 MPa to 14 MPa. Panels were successfully fabricated without the use of adhesive resin, achieving maximum tensile and flexural strengths of approximately 56 MPa and 99 MPa, respectively, at 210 °C and 14 MPa. Microstructural analysis revealed a uniform distribution of aluminum and LDPE within the core, with void content ranging from 5 % to 6 %. Carbon footprint assessment showed that the compression molding process generated about 0.18 kg CO₂ per panel. The findings demonstrate that recycled beverage carton packaging waste is a viable, eco-friendly, and mechanically robust alternative for ACP core materials, offering a promising pathway toward sustainable composite panel manufacturing. Future research will focus on enhancing surface bonding, assessing long-term durability, and scaling up fabrication processes for industrial applications.

本研究的重点是利用低密度聚乙烯（LDPE）和从饮料纸箱包装废料中提取的铝箔，去除牛皮纸作为可持续核心材料，制作铝复合板（acp）并进行综合评价。在不同的压缩成型条件下，系统地研究了面板的力学、微观结构和环境性能，以确定最佳的加工参数。压缩温度范围为190℃至210℃，压力范围为10 MPa至14 MPa。在不使用胶粘剂树脂的情况下成功制造了面板，在210°C和14 MPa下分别达到了约56 MPa和99 MPa的最大拉伸和弯曲强度。显微组织分析表明，铝和LDPE在芯内分布均匀，空洞含量在5% ~ 6%之间。碳足迹评估表明，压缩成型过程产生约0.18公斤的二氧化碳每面板。研究结果表明，回收的饮料纸盒包装废弃物是一种可行的、环保的、机械坚固的ACP核心材料替代品，为可持续复合板制造提供了一条有希望的途径。未来的研究将集中在增强表面粘合、评估长期耐久性和扩大工业应用的制造工艺上。

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引用次数: 0

A review on the yarn pull-out behavior of high-performance woven fabrics for impact resistance 高性能机织物抗冲击拔纱性能研究进展

IF 7 Q2 MATERIALS SCIENCE, COMPOSITES

Composites Part C Open Access

Pub Date : 2025-10-01 Epub Date: 2025-10-10 DOI: 10.1016/j.jcomc.2025.100658

Hanyuan Pan , Jinzheng Liu , Jiang Xie , Zhenyu Feng

High-performance woven fabrics have been widely used in transportation, military, and so on, due to their low density and superior mechanical properties. These structures are commonly exposed to impact loading, including bullets, fragments and blast waves. Yarn interaction is one of the key affecting the impact resistance of fabrics, and methods and results of aramid and ultra-high molecular weight polyethylene yarn pull-out test are mainly reviewed. The factors, including fabric configuration, pull-out speed, inter-yarn friction and so on, affecting the peak pull-out force (PPF) of yarn are summarized in detail. Moreover, the influencing mechanisms of these factors are discussed and revealed by comparison of previous studies. The results indicate that most factors have unified conclusions on the influence of PPF. However, Few conclusions still exist differences, such as whether the relationship of number of pulled yarns and PPF is liner or non-liner, but have been clarified in this paper. Furthermore, the influencing mechanism has become clearer after discussion, but so far, it still remains at the qualitative level. In future research, further standardization of yarn pull-out test is needed to obtain more comparable data. In addition, it is recommended to conduct more yarn pull-out research on the influencing results and mechanisms of these factors under dynamic loading.

高性能机织物以其低密度和优越的机械性能，在交通运输、军事等领域得到了广泛的应用。这些结构通常暴露在冲击载荷下，包括子弹、碎片和冲击波。纱线相互作用是影响织物抗冲击性能的关键之一，本文主要综述了芳纶和超高分子量聚乙烯纱线的拉拔试验方法和结果。详细总结了织物形态、抽拔速度、纱线间摩擦等因素对纱线最大抽拔力（PPF）的影响。并通过与前人研究的比较，探讨和揭示了这些因素的影响机制。结果表明，大多数因素对PPF的影响有统一的结论。然而，关于拉纱数与PPF的关系是线性关系还是非线性关系等少数结论仍存在分歧，本文对此进行了澄清。此外，经过讨论，其影响机制也更加清晰，但目前仍停留在定性层面。在今后的研究中，需要进一步规范抽纱试验，以获得更多可比较的数据。此外，建议对这些因素在动载下的影响结果和机理进行更多的拔纱研究。

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引用次数: 0

Estimating the creep rupture time of GFRP bars using machine learning 用机器学习估计GFRP筋的蠕变断裂时间

IF 7 Q2 MATERIALS SCIENCE, COMPOSITES

Composites Part C Open Access

Pub Date : 2025-10-01 Epub Date: 2025-09-15 DOI: 10.1016/j.jcomc.2025.100648

M.Talha Junaid , Ahed Habib , Mazen Shrif , Samer Barakat

Fiber-reinforced polymer (FRP) bars are increasingly utilized in civil structures due to their advantages in terms of corrosion resistance and a high strength-to-weight ratio. Current research on long-term durability, particularly under sustained loading (creep-rupture), has not yet fully explored the use of methods like machine learning to accurately predict the creep rupture time of FRP bars. This study seeks to address this gap by applying machine learning techniques to estimate the creep rupture time of glass fiber-reinforced polymer (GFRP) bars. The motivation for this research comes from the shortcomings of traditional models, which are often inadequate for capturing the complex nonlinear behavior of materials subjected to long-term stress. This research aims to evaluate the effectiveness of different machine learning models, including neural networks, support vector machines, and ensemble methods, in predicting the creep behavior of GFRP bars. Within the study context, a large dataset consisting of 435 experimental tests is collected from the literature. In the testing phase, the optimized neural network achieved an RMSE of 926.29 h and an R² of 0.99 on a heterogeneous dataset that also included bars tested under environmental conditioning reported in the source studies. Gaussian process regression and support vector machines also performed well, albeit with higher errors. Sensitivity analysis revealed that the level of sustained stress and bar diameter were the most critical factors for environmentally conditioned bars. Importantly, the predictors reflect standard design and material descriptors (diameter, fiber content, modulus, UTS, sustained stress) and, when reported, environmental conditioning, which together capture the primary sources of variability relevant to civil engineering practice. Overall, the findings suggest that machine learning, particularly through optimized neural networks, offers a powerful tool for predicting complex material behavior and improving the reliability of GFRP-reinforced structures. This study contributes to the field by highlighting the potential of machine learning to enhance the precision of long-term performance predictions for engineering materials, facilitating improved design and material selection in critical infrastructure.

纤维增强聚合物（FRP）钢筋由于其耐腐蚀和高强度重量比的优点，在民用结构中得到越来越多的应用。目前对FRP筋长期耐久性的研究，特别是在持续荷载（蠕变破裂）下，尚未充分探索使用机器学习等方法来准确预测FRP筋蠕变破裂时间。本研究试图通过应用机器学习技术来估计玻璃纤维增强聚合物（GFRP）棒的蠕变破裂时间来解决这一差距。这项研究的动机来自于传统模型的缺点，这些模型往往不足以捕捉材料在长期应力作用下的复杂非线性行为。本研究旨在评估不同机器学习模型的有效性，包括神经网络、支持向量机和集成方法，以预测GFRP筋的蠕变行为。在研究背景下，从文献中收集了一个由435个实验测试组成的大型数据集。在测试阶段，优化后的神经网络在异构数据集上的RMSE为926.29 h， R²为0.99，该数据集还包括在源研究中报告的环境条件下测试的棒材。高斯过程回归和支持向量机也表现良好，尽管误差较高。敏感性分析表明，持续应力水平和杆径是环境条件杆的最关键因素。重要的是，预测因子反映了标准设计和材料描述符（直径、纤维含量、模量、UTS、持续应力），当报告时，还反映了环境条件，它们一起捕获了与土木工程实践相关的可变性的主要来源。总的来说，研究结果表明，机器学习，特别是通过优化的神经网络，为预测复杂的材料行为和提高gfrp增强结构的可靠性提供了一个强大的工具。这项研究通过强调机器学习的潜力来提高工程材料长期性能预测的准确性，促进关键基础设施的改进设计和材料选择，从而为该领域做出了贡献。

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引用次数: 0

Eigenmode-based inverse identification and multi-parameter sensitivity analysis of flax/Elium® laminates from unidirectional to cross-ply configurations validation 基于特征模型的亚麻/Elium®层压板从单向到交叉配置验证的反识别和多参数灵敏度分析

IF 7 Q2 MATERIALS SCIENCE, COMPOSITES

Composites Part C Open Access

Pub Date : 2025-10-01 Epub Date: 2025-11-10 DOI: 10.1016/j.jcomc.2025.100682

Ameny Ketata , Zouhaier Jendli , Mondher Haggui , Abderrahim El Mahi , Anas Bouguecha , Mohamed Haddar

This article presents an extended validation of a previously developed inverse identification method, initially applied to unidirectional (UD) flax/Elium® laminates. The study aims to (i) confirm the robustness of the ply-level inverse approach under more complex cross-ply configurations and (ii) identify the dominant mechanical parameters influencing the vibration behavior of biocomposites beyond UD layouts. A comprehensive sensitivity analysis is conducted to assess the influence of material and geometrical parameters on the first seven vibration modes. While E1 and G12 predominantly govern the dynamic response in UD laminates, cross-ply configurations reveal additional influences from the transverse modulus E2 and interlaminar shear modulus G13. Enhanced coupling effects involving G12 are also observed. Structural variability is considered through parameters such as thickness and density, which reflect the heterogeneous nature of bio-based materials. A compensation mechanism is highlighted: increased thickness raises stiffness but also adds mass, partially offsetting frequency gains. The study demonstrates a progressive transition in dominant mechanical parameters across modes: lower modes (f1, f2) are controlled by E1, while higher modes become increasingly sensitive to G12, G13, and, to a lesser extent, E2. The proposed inverse method shows excellent agreement between simulated and experimental modal responses for both 4-ply and 8-ply laminates. The genetic algorithm converges toward realistic values—thickness between 3,03 mm and 3,17 mm and density around 1292 kg/m³—confirming the robustness of the approach. By accounting for both material variability and process-induced dispersion, the method contributes to more reliable modeling and optimized design of natural fiber composites.

本文提出了先前开发的反识别方法的扩展验证，最初应用于单向（UD）亚麻/Elium®层压板。该研究旨在(i)确认在更复杂的交叉铺层结构下铺层级逆方法的鲁棒性，以及（ii）确定在UD布局之外影响生物复合材料振动行为的主要力学参数。对材料和几何参数对前7种振型的影响进行了综合灵敏度分析。虽然E1和G12主导着UD层合板的动态响应，但横向模量E2和层间剪切模量G13对交叉层合板的动态响应有额外的影响。还观察到涉及G12的增强耦合效应。结构变异性是通过厚度和密度等参数来考虑的，这些参数反映了生物基材料的异质性。一个补偿机制被强调：增加的厚度提高了刚度，但也增加了质量，部分抵消了频率增益。研究表明，各模态的主要力学参数呈渐进式转变：较低的模态（f1, f2）受E1控制，而较高的模态对G12， G13越来越敏感，E2在较小程度上也越来越敏感。所提出的反方法表明，4层和8层复合材料的模拟模态响应与实验模态响应非常吻合。遗传算法收敛于实际值-厚度在3.03 mm和3.17 mm之间，密度在1292 kg/m³左右-证实了该方法的鲁棒性。该方法考虑了材料的可变性和工艺引起的分散，有助于更可靠的天然纤维复合材料建模和优化设计。

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引用次数: 0

A critical review on laser-assisted paint removal from carbon fibre-reinforced polymer: Insights into process parameters, material integrity, and numerical modelling 激光辅助去除碳纤维增强聚合物的油漆：对工艺参数、材料完整性和数值模拟的见解

IF 7 Q2 MATERIALS SCIENCE, COMPOSITES

Composites Part C Open Access

Pub Date : 2025-10-01 Epub Date: 2025-09-29 DOI: 10.1016/j.jcomc.2025.100654

Shiyao Zhu , Jojibabu Panta , Richard (Chunhui) Yang , Lin Ye , Y.X. Zhang

Laser-based paint stripping has emerged as a precise, efficient, and environmentally sustainable technique for removing paints/coatings from carbon fibre-reinforced polymer (CFRP) composites. This review presents a comprehensive analysis of laser-material interaction mechanisms that govern paint removal, including thermal ablation, thermally induced interfacial failure, plasma shock wave generation, and photochemical bond disruption. The influences of thermal and optical properties of CFRP and paint on interaction dynamics and removal behaviours are critically examined. The key laser processing parameters are systematically analysed in relation to stripping efficiency, substrate preservation, and thermal loading. Experimental methods used for monitoring process response and evaluating removal quality are also reviewed. Numerical modelling approaches based on the finite element method are discussed, with a focus on simulating transient heat transfer, interfacial stresses, and coupled effects. Limitations of current models in capturing the complexity of pulsed laser interaction with multilayered paint-composite structure are addressed. The review highlights that while laser stripping offers selective, damage-free paint removal, challenges remain in managing thermal effects, ensuring layer-specific selectivity, and achieving process scalability. Addressing these challenges is essential for translating laser-based stripping into reliable maintenance solutions for aerospace, defence, renewable energy, and automotive industries.

激光脱漆技术是一种精确、高效、环保的碳纤维增强聚合物（CFRP）复合材料脱漆技术。本文综述了影响涂料去除的激光与材料相互作用机制的综合分析，包括热烧蚀、热诱导界面破坏、等离子体冲击波产生和光化学键破坏。CFRP和涂料的热学和光学性质对相互作用动力学和去除行为的影响进行了严格的检查。系统地分析了激光加工的关键参数与剥离效率、衬底保存和热载荷的关系。本文还综述了用于监测过程响应和评价去除质量的实验方法。讨论了基于有限元方法的数值模拟方法，重点是模拟瞬态传热、界面应力和耦合效应。解决了当前模型在捕捉脉冲激光与多层涂料-复合材料结构相互作用的复杂性方面的局限性。该综述强调，虽然激光剥离提供了选择性、无损伤的油漆去除，但在管理热效应、确保层特异性选择性和实现工艺可扩展性方面仍然存在挑战。解决这些挑战对于将激光剥离转化为航空航天、国防、可再生能源和汽车行业的可靠维护解决方案至关重要。

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引用次数: 0

Bayesian modelling approach to hydrogen permeation in fibre-reinforced polymer composites 纤维增强聚合物复合材料氢渗透的贝叶斯建模方法

IF 7 Q2 MATERIALS SCIENCE, COMPOSITES

Composites Part C Open Access

Pub Date : 2025-10-01 Epub Date: 2025-08-05 DOI: 10.1016/j.jcomc.2025.100630

Andrew Angus , Mustafa Okumuş , Łukasz Figiel

A Bayesian modelling approach is proposed to enable uncertainty quantification of hydrogen permeation in fibre-reinforced polymer composites. Specifically, the approach combines surrogate modelling via Gaussian Process (GP) regression, Bayesian optimisation and Markov Chain Monte Carlo (MCMC) to predict uncertainties in constituent (input) and overall (output) permeability across selected composite scales. By utilising training data from physics-based models (both numerical and analytical) and some experimental data available in the literature, the probabilistic approach is illustrated with examples demonstrating its capability in statistical inference of fibre permeability at the microscale, uncertainty quantification of effective permeability in a multilayered system, and simple probabilistic design at the component level.

提出了一种贝叶斯建模方法来实现纤维增强聚合物复合材料中氢渗透的不确定量化。具体来说，该方法结合了通过高斯过程（GP）回归、贝叶斯优化和马尔可夫链蒙特卡罗（MCMC）的代理建模，以预测在选定的复合尺度上组成（输入）和总体（输出）渗透率的不确定性。通过利用基于物理的模型（数值和解析）的训练数据以及文献中可用的一些实验数据，用实例说明了概率方法在微观尺度上纤维渗透率的统计推断、多层系统中有效渗透率的不确定性量化以及部件级别的简单概率设计方面的能力。

引用次数: 0

Analysis of the structure and characteristics of bioglass–iron oxide composite layers on Ti-6Al-4V alloy via electrophoretic deposition 电泳沉积Ti-6Al-4V合金生物玻璃-氧化铁复合层的结构与特性分析

IF 7 Q2 MATERIALS SCIENCE, COMPOSITES

Composites Part C Open Access

Pub Date : 2025-10-01 Epub Date: 2025-08-19 DOI: 10.1016/j.jcomc.2025.100639

Zahra Sohani, Hamed Jamshidi Aval, Sayed Mahmood Rabiee

This study investigates the structural and functional properties of bioglass–iron oxide (Fe₃O₄) composite layers deposited on Ti-6Al-4V substrates via electrophoretic deposition (EPD). Suspensions with varying Fe₃O₄ contents (10, 15, 25, and 50 wt %) were prepared to identify the optimal composition. SEM and elemental mapping revealed that the B90-F10 sample (90 % bioglass, 10 % Fe₃O₄) produced a more uniform and denser coating compared to other compositions, while minimizing porosity and crack formation. The Vickers microhardness of the B90-F10 coating reached 321.3 ± 3.4 HV, higher than that of the pure bioglass coating B100-F0 (295.1 ± 2.3 HV). Surface roughness measurements showed that B90-F10 had a lower average roughness (0.82 ± 0.41 µm) than B100-F0 (2.10 ± 0.46 µm), indicating a smoother, more compact surface. The mean coating thickness for B90-F10 was 148.32 ± 0.02 µm, slightly greater than B100-F0 (140.01 ± 0.01 µm). Contact angle tests confirmed improved hydrophilicity, with B90-F10 showing a reduced contact angle (22.56°) compared to the uncoated substrate (55.16°). Electrochemical tests revealed that although coatings slightly reduced corrosion resistance compared to bare alloy due to residual porosity, the addition of Fe₃O₄ significantly increased charge transfer resistance, indicating better barrier performance than pure bioglass coatings. In vitro bioactivity tests confirmed enhanced formation of hydroxyapatite layers, critical for osseointegration. These findings highlight the coatings’ capacity to augment implant performance by improving mechanical durability, surface characteristics, and bioactivity, thus offering a valuable functional enhancement beyond the untreated substrate.

研究了电泳沉积（EPD）在Ti-6Al-4V衬底上制备的生物玻璃-氧化铁（Fe₃O₄）复合层的结构和功能特性。制备了不同Fe₃O₄含量（10、15、25和50% wt %）的混悬液，以确定最佳组成。SEM和元素映射显示，B90-F10样品（90%生物玻璃，10% Fe₃O₄）与其他成分相比，产生了更均匀、更致密的涂层，同时最大限度地减少了孔隙度和裂纹的形成。B90-F10涂层的维氏显微硬度达到321.3±3.4 HV，高于纯生物玻璃涂层B100-F0（295.1±2.3 HV）。表面粗糙度测量表明，B90-F10的平均粗糙度（0.82±0.41µm）低于B100-F0(2.10±0.46µm)，表明表面更光滑，更致密。B90-F10的平均涂层厚度为148.32±0.02µm，略大于B100-F0（140.01±0.01µm）。接触角测试证实亲水性得到改善，与未涂覆的基材（55.16°）相比，B90-F10的接触角（22.56°）减小。电化学测试表明，尽管由于残余孔隙率的存在，涂层的耐腐蚀性略低于裸合金，但Fe₃O₄的加入显著提高了电荷转移阻力，表明涂层的阻隔性能优于纯生物玻璃涂层。体外生物活性测试证实羟基磷灰石层的形成增强，这对骨整合至关重要。这些发现强调了涂层通过提高机械耐久性、表面特性和生物活性来增强植入物性能的能力，从而提供了比未经处理的基底更有价值的功能增强。

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引用次数: 0

Strain and damage sensing performance of functionally graded nanocomposite lattices enabled by DLP 3D printing DLP 3D打印实现功能梯度纳米复合材料晶格的应变和损伤传感性能

IF 7 Q2 MATERIALS SCIENCE, COMPOSITES

Composites Part C Open Access

Pub Date : 2025-10-01 Epub Date: 2025-08-07 DOI: 10.1016/j.jcomc.2025.100634

Omar Waqas Saadi , Andreas Schiffer , S Kumar

This research examines the mechanical and piezoresistive characteristics of geometrically graded octet and kelvin lattices fabricated via Digital Light Processing (DLP) additive manufacturing technique. The geometrically graded lattice structures feature varying unit cell sizes with constant relative density (20, 30, and 40 %), and are composed of electrically conductive nanocomposite photoresin loaded with 0.05 phr multi-walled carbon nanotubes (MWCNTs). Under monotonic compression, the peak stress and energy absorption of the graded octet lattice are found to rise with increasing level of gradation, reporting enhancements in the latter properties by factors of up to 2.6 and 2.0, respectively, in comparison to their non-graded counterparts of equal weight. In contrast, the graded kelvin lattice structures show lower enhancements in energy absorption of up to 1.2 times the non-graded equivalent. The piezoresistive response of both octet and kelvin lattices is characterized by a sharp initial drop in electrical resistance followed by a nonlinear response that shows signatures related to distinct failure processes observed in the studied structures. The initial gauge factor of the lattice structures is found to increase with increasing level of gradation and relative density. The geometric gradients also enhance the structure’s recoverability, allowing the struts in the softer layers to fold and unfold during cyclic compressive loading, yielding enhanced cyclic stability in piezoresistive behavior. The findings of this study suggest that the adoption of functional geometry gradients in nanocomposite lattices can assist in achieving enhanced energy absorption and strain/damage sensing functionalities under various loading conditions.

本研究考察了通过数字光处理（DLP）增材制造技术制造的几何梯度八元体和开尔文晶格的机械和压阻特性。几何梯度晶格结构具有不同的单元尺寸和恒定的相对密度（20%，30%和40%），由导电纳米复合光树脂负载0.05 phr多壁碳纳米管（MWCNTs）组成。在单调压缩下，梯度八元晶格的峰值应力和能量吸收随着梯度水平的增加而增加，与同等重量的非梯度八元晶格相比，后者的性能分别提高了2.6和2.0倍。相比之下，梯度开尔文晶格结构在能量吸收方面表现出较低的增强，高达非梯度等效的1.2倍。八极体和开尔文晶格的压阻响应的特征是电阻的急剧初始下降，然后是非线性响应，显示与所研究结构中观察到的不同失效过程相关的特征。发现晶格结构的初始规范因子随层序和相对密度的增加而增加。几何梯度还增强了结构的可恢复性，允许较软层中的支撑在循环压缩载荷下折叠和展开，从而增强了压阻行为的循环稳定性。本研究的结果表明，在纳米复合材料晶格中采用功能几何梯度有助于在各种载荷条件下实现增强的能量吸收和应变/损伤传感功能。

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引用次数: 0

A hybrid approach for predicting fatigue life of fiber-reinforced polypropylene composite (PPGF40): Integrating micromechanical modelling 纤维增强聚丙烯复合材料（PPGF40）疲劳寿命预测的混合方法：集成微观力学模型

IF 7 Q2 MATERIALS SCIENCE, COMPOSITES

Composites Part C Open Access

Pub Date : 2025-10-01 Epub Date: 2025-10-07 DOI: 10.1016/j.jcomc.2025.100660

Mohammadali Shirinbayan , Samia Nouira , Jihed Zghal , Joseph Fitoussi

This paper presents a hybrid approach for predicting the fatigue life of PPGF40. The approach combines micromechanical modeling with empirical techniques, based on an intrinsic relationship. Micromechanical modeling is used to analyze the material's monotonic behavior. The study presents a micromechanical model, based on Mori and Tanaka's approach, for simulating damage at the fiber-matrix interface. The model incorporates a local criterion and linearizes the plastic behavior of the matrix using the secant modulus method. The model parameters are identified by comparing them with experimental stiffness reduction results, and S-N curves for different modeled orientations (0°, 45°, and 90°) are presented. The study concludes by establishing the Tsai-Wu fatigue failure criterion based on hybrid modeling results, demonstrating its usefulness in designing structures such as tailgates. The versatility of the micromechanical model extends to other microstructures upon validation. This methodology provides a framework for linking process, microstructure, and properties, and can be coupled in the future with microstructure prediction tools, such as Moldflow, to support fatigue optimization in PPGF40 and similar materials.

提出了一种预测PPGF40疲劳寿命的混合方法。该方法结合了基于内在关系的微观力学建模和经验技术。采用微观力学模型分析材料的单调行为。该研究提出了一个基于Mori和Tanaka方法的微观力学模型，用于模拟纤维-基质界面的损伤。该模型采用局部准则，并采用割线模量法线性化了矩阵的塑性行为。通过与试验刚度折减结果的对比，确定了模型参数，并给出了不同建模方向（0°、45°和90°）下的S-N曲线。最后，在混合模型的基础上建立了Tsai-Wu疲劳破坏准则，证明了其在尾板等结构设计中的有效性。微力学模型的多功能性在验证后扩展到其他微观结构。该方法为连接工艺、微观结构和性能提供了一个框架，并且可以在未来与微观结构预测工具（如Moldflow）相结合，以支持PPGF40和类似材料的疲劳优化。

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