今日更新:Composite Structures 4 篇,Composites Part A: Applied Science and Manufacturing 3 篇,Composites Part B: Engineering 1 篇
Reduced order homogenization of composites with strength difference effects in elastoplasticity coupled to damage
Xiaozhe Ju, Chenbin Zhou, Yangjian Xu, Lihua Liang
doi:10.1016/j.compstruct.2024.118564
复合材料弹塑性损伤与强度差异效应的降阶均质化研究
This paper addresses reduced order homogenization of composites with strength difference (SD) effects in elastoplasticity coupled to damage, while containing several well-known plasticity criteria as special cases. We extend two approaches for this purpose: 1. nonuniform transformation field analysis (NTFA by Michel and Suquet, 2003) and 2. a recent variant called cluster-based NTFA (CNTFA by Ri et al., 2021), and conduct a comparative study on them. For the NTFA approach, a space–time decomposition is done separately for volumetric and deviatoric inelastic strain fields. A coupled model is derived for the present case to govern the evolution of resulting reduced variables. For the CNTFA approach, a clustering analysis is additionally performed for a spatial decomposition of the micro-domain. Unlike the NTFA, the online analysis is formulated as a unified minimization problem, which does not require a major adaptation for the present case. For both approaches, localization rules are deduced from the superposition principle and then homogenized to obtain the effective responses. FE-based implementation is presented in detail for both approaches. Numerical results show that both approaches provide a striking acceleration rate against conventional FE computations. The CNTFA predictions are more accurate than the NTFA ones by involving clustered microscopic fields in the online analysis, thus resulting into a slightly increased memory requirement.
本文研究了强度差(SD)效应下复合材料弹塑性与损伤耦合的降阶均匀化问题,并包含了几个众所周知的塑性准则作为特例。我们为此扩展了两种方法:1。2.非均匀变换场分析(NTFA, Michel and Suquet, 2003)。一种最近的变体称为基于簇的NTFA (CNTFA by Ri et al., 2021),并对它们进行比较研究。对于NTFA方法,分别对体积非弹性应变场和偏差非弹性应变场进行时空分解。为本案例导出了一个耦合模型来控制由此产生的约简变量的演化。对于CNTFA方法,另外进行了微域空间分解的聚类分析。与NTFA不同,在线分析被制定为统一的最小化问题,不需要对当前情况进行重大调整。这两种方法都是根据叠加原理推导出局部化规则,然后进行均匀化,得到有效响应。详细介绍了这两种方法的基于fe的实现。数值结果表明,与传统的有限元计算相比,这两种方法都提供了惊人的加速度。通过在在线分析中加入聚类微观场,CNTFA的预测比NTFA的预测更准确,从而导致内存需求略有增加。
Plasticity analysis and a homogenized constitutive model of compressible multi-layer structure of battery
Pengfei Ying, Xiao Tian, Yong Xia
doi:10.1016/j.compstruct.2024.118586
电池可压缩多层结构的塑性分析及均质本构模型
A homogenized constitutive model for the compressible multi-layer structure of battery (CMLSB) under external loading is essential for optimizing the structural design of electric assemblies. Currently, there is no specific constitutive model that is both mechanically explanatory and operationally applicable to CMLSB under varied loading conditions. In this study, due to limited understanding of the in-plane behavior of CMLSB, an analytical model was developed to investigate plasticity in this specific direction using the strain probing method. The observed plastic characteristics inspired the formulation of a novel two-dimensional constitutive framework for CMLSB in the in-plane direction.By integrating this new constitutive framework with one-dimensional plastic descriptions, a hybrid constitutive model was introduced and implemented in finite element software. Calibration and validation of the model were performed using a commercial pouch cell battery and its segments under various loading conditions. Finite element simulations with the hybrid model demonstrated remarkable accuracy in predicting the mechanical behavior of the cell under various in-plane and out-of-plane compression scenarios. Additionally, simulations were carried out to analyze the impact of cell packaging and air pressure. The new hybrid battery model is considered a user-friendly, physically interpretable, and high-fidelity tool, poised to significantly facilitate the comprehensive design of electric devices.
外载荷作用下可压缩多层电池(CMLSB)结构的均质本构模型对于优化电池组件结构设计至关重要。目前,对于不同载荷条件下的CMLSB,还没有一个既能在力学上解释又能在操作上适用的具体本构模型。在本研究中,由于对CMLSB的面内行为了解有限,因此采用应变探测方法建立了一个分析模型来研究该特定方向的塑性。观察到的塑性特性启发了CMLSB平面方向上新的二维本构框架的制定。将这种新的本构框架与一维塑性描述相结合,引入混合本构模型,并在有限元软件中实现。在各种负载条件下,使用商用袋式电池及其分段对模型进行了校准和验证。混合模型的有限元模拟结果表明,在各种面内和面外压缩情况下,单元的力学行为预测具有显著的准确性。此外,还进行了模拟,分析了电池封装和气压的影响。新的混合电池模型被认为是一种用户友好的、物理可解释的、高保真的工具,有望显著促进电子设备的综合设计。
Dielectric behavior and breakdown strength of glass fiber reinforced epoxy composites under dynamic mechanical fatigue
Xiaoxiao Kong, Chengyao Hou, Yun Chen, Qi Li, Yunqi Xing, Boxue Du
doi:10.1016/j.compstruct.2024.118569
动态机械疲劳下玻璃纤维增强环氧复合材料的介电性能和击穿强度
Glass fiber reinforced polymer (GFRP), used in insulating components like insulation rods, needs to withstand both high voltage and large dynamic mechanical fatigue during operation. In this paper, the effects of tension–compression fatigue loads on the dielectric properties of GFRP under various fatigue cycles and stress levels are investigated. The results show that DC conductivity has a strong negative correlation with stiffness, while breakdown strength is showing a positive correlation. Fatigue-induced internal damage could cause continuous charge accumulation and enhanced interfacial polarization, leading to the increase of dielectric constant by 46.89% and the reduction of breakdown strength by 15.05%, when the fatigue span ratio reaches 80% under 40% stress level. Understanding the evolution of dielectric properties of GFRP under dynamic mechanical fatigue conditions is helpful for ensuring the safe and stable operation of electrical power equipment subjected to both high voltage and fatigue loads.
玻璃纤维增强聚合物(GFRP)用于绝缘棒等绝缘部件,在工作过程中需要承受高电压和大的动态机械疲劳。本文研究了不同疲劳循环和应力水平下拉伸-压缩疲劳载荷对玻璃钢介电性能的影响。结果表明,直流电导率与刚度呈较强的负相关关系,击穿强度与刚度呈正相关关系。在40%应力水平下,当疲劳跨比达到80%时,疲劳损伤引起的内部电荷持续积累,界面极化增强,导致介电常数增加46.89%,击穿强度降低15.05%。了解GFRP在动态机械疲劳条件下介电性能的演变规律,有助于保证电力设备在高压和疲劳载荷作用下的安全稳定运行。
Improved creep resistance of short carbon fiber reinforced polyetherimide composite by solution mixing method
Quan-Xiu Liu, Yuan-Yuan Zhang, Tao Guan, Bo-Wen Guan, Xiao-Long Mo, Yuan-Qing Li, Ya-Qin Fu, Shao-Yun Fu
doi:10.1016/j.compstruct.2024.118575
用溶液混合法提高短碳纤维增强聚醚酰亚胺复合材料的抗蠕变性能
Creep resistance is critical for ensuring the dimensional stability and safe operation of composite components. However, the creep resistance of short fiber reinforced thermoplastic composites has been rarely reported and that of the composites manufactured by conventional extrusion compounding combined with injection molding is kind of low. In this work, in order to address this issue, two short carbon fiber-reinforced polyetherimide (SCF/PEI) composites named respectively as SCF/PEIE and SCF/PEIS are prepared by both conventional extrusion compounding and our newly developed solution mixing method combined with injection molding. The solution mixing method involves the dispersion and mixing of carbon fibers within a PEI solution and allows for the retention of longer fiber lengths. Experimentally, the creep behaviors of the SCF/PEI composites were examined through tensile and flexural creep testing at various stress levels in a wide temperature range. Theoretically, the creep behaviors were characterized by employing the Schapery model and the time–temperature superposition principle (TTSP), and the impact of fiber length retention on creep resistance was quantitatively analyzed using the Fu-Lauke model. The results demonstrate that compared to the SCF/PEIE composite, the SCF/PEIS composite exhibits a higher creep fracture stress level (175 MPa) and a more extensive linear viscoelastic region (0 ∼ 85 MPa) at room temperature. Furthermore, the SCF/PEIS composite was observed to have a significantly longer secondary creep stage at an elevated temperature of 210 °C. Overall, the creep resistance of the newly manufactured SCF/PEIS is significantly superior to that of the SCF/PEIE, which effectively extends the service life and operational capacity of injection-molded SCF/PEI composites.
抗蠕变性能是保证复合材料部件尺寸稳定性和安全运行的关键。然而,短纤维增强热塑性复合材料的抗蠕变性能报道较少,常规挤出复合与注射成型相结合制备的复合材料的抗蠕变性能较低。为了解决这一问题,本文采用常规挤出复合和新开发的溶液混合与注射成型相结合的方法制备了两种短碳纤维增强聚醚酰亚胺(SCF/PEI)复合材料,分别命名为SCF/PEIE和SCF/PEIS。该溶液混合方法涉及在PEI溶液中分散和混合碳纤维,并允许保留较长的纤维长度。实验研究了SCF/PEI复合材料在不同应力水平和较宽温度范围下的拉伸和弯曲蠕变行为。理论上,采用Schapery模型和时间-温度叠加原理(TTSP)表征了纤维的蠕变行为,并采用Fu-Lauke模型定量分析了纤维长度保留对纤维抗蠕变性能的影响。结果表明,与SCF/PEIE复合材料相比,SCF/PEIS复合材料在室温下表现出更高的蠕变断裂应力水平(175 MPa)和更广泛的线性粘弹性区域(0 ~ 85 MPa)。此外,在210 ℃的高温下,SCF/PEIS复合材料的二次蠕变阶段明显延长。总体而言,新制备的SCF/PEIS的抗蠕变性能明显优于SCF/PEIE,有效地延长了注塑成型SCF/PEI复合材料的使用寿命和使用能力。
Optimizing heterostructure parameters towards enhanced toughening in micro/nano-reinforced bimodal-grained Al alloy composites
Farhad Saba, Hang Sun, Elham Garmroudi Nezhad, Bo Cui, Genlian Fan, Zhanqiu Tan, Sijie Wang, Zhenming Yue, Zhiqiang Li
doi:10.1016/j.compositesa.2024.108442
优化微/纳米增强双晶铝合金复合材料的异质结构参数
We manipulated soft coarse-grained (CG) domains to design optimized and toughened B4Cp/6061Al composites, featuring bimodal domains with in-situ MgO nanoparticles (n-MgO) and ex-situ carbon nanotubes (CNTs). Manipulating CG fractions influenced heterostructure parameters such as CG band width and domain distribution. A systematic optimization strategy integrating intrinsic/extrinsic toughening and strengthening mechanisms identified optimal conditions for maximizing strength-ductility-toughness. The optimal 20:80 CG-to-ultrafine-grain (UFG) weight ratio offered an ultimate strength of 550 MPa and ∼7 % elongation. Intrinsic toughening mechanisms enhanced UFG domain dislocation storage capacity. Multiscale analysis of crack behavior revealed pronounced crack-blunting in well-dispersed CG domains. Extrinsic toughening mechanisms included nanobridge formation, crack-deflection, micro-crack proliferation, and crack-branching. Micromechanical behavior was examined using the strain gradient model. Designing strong and ductile micro/nano-reinforced bimodal grained composites requires selecting a smaller amount of CG domains, a CG band width larger than double the interface affected zone (IAZ), and larger grain sizes in the CG domains.
我们利用软粗晶(CG)结构域设计了优化增韧的B4Cp/6061Al复合材料,该复合材料具有原位MgO纳米颗粒(n-MgO)和非原位碳纳米管(CNTs)双峰结构域。操纵CG分数会影响异质结构参数,如CG带宽和畴分布。系统的优化策略整合了内在/外在的增韧和强化机制,确定了最大化强度-延展性-韧性的最佳条件。最佳的超细晶粒(UFG)重量比为20:80,其极限强度为550 MPa,伸长率为 ~ 7 %。本征增韧机制增强了UFG畴位错存储能力。裂纹行为的多尺度分析揭示了明显的裂纹钝化在良好分散的CG域。外部增韧机制包括纳米桥形成、裂纹偏转、微裂纹扩展和裂纹分支。采用应变梯度模型研究了微力学行为。设计强韧的微/纳米增强双峰晶复合材料需要选择更少的CG域,CG带宽大于界面影响区(IAZ)的两倍,以及更大的CG域晶粒尺寸。
Physics-constrained neural network for design and feature-based optimization of weave architectures
Haotian Feng, Sabarinathan P. Subramaniyan, Hridyesh Tewani, Pavana Prabhakar
doi:10.1016/j.compositesa.2024.108465
基于物理约束的神经网络编织结构设计与特征优化
Woven fabrics play an essential role in everyday textiles for clothing/sportswear, water filtration, retaining walls, and reinforcements in stiff composites for lightweight structures in aerospace, sporting, automotive, and marine industries. Several possible weave architectures (combinations of weave patterns and material choices) present a challenging question about how they could influence the physical and mechanical properties of woven fabrics and reinforced structures. This paper presents a novel Physics-Constrained Neural Network (PCNN) to predict the mechanical properties (like modulus) of weave architectures and the inverse problem of predicting pattern/material sequence for a design/target modulus value. The inverse problem is particularly challenging as it usually requires many iterations to find the appropriate architecture using traditional optimization approaches. We show that the proposed PCNN can more accurately predict weave architecture for the desired modulus than several baseline models considered. We present a feature-based optimization strategy to improve predictions using features in the Grey Level Co-occurrence Matrix space. We combine PCNN with feature-based optimization to discover near-optimal weave architectures and facilitate the initial design of weave architecture. The proposed frameworks will primarily enable the woven composite analysis and optimization process and be a starting point to introduce knowledge-guided neural networks into the complex structural analysis.
机织织物在服装/运动服装、水过滤、挡土墙的日常纺织品中发挥着重要作用,在航空航天、体育、汽车和海洋工业的轻质结构的硬质复合材料中也起着增强作用。几种可能的编织结构(编织模式和材料选择的组合)提出了一个具有挑战性的问题,即它们如何影响织物和增强结构的物理和机械性能。本文提出了一种新的物理约束神经网络(PCNN)来预测编织结构的力学性能(如模量),以及预测图案/材料序列的设计/目标模量值的反问题。反问题尤其具有挑战性,因为它通常需要多次迭代才能使用传统的优化方法找到合适的体系结构。我们表明,与几种基线模型相比,所提出的PCNN可以更准确地预测所需模量的编织结构。我们提出了一种基于特征的优化策略来改进使用灰度共生矩阵空间中的特征的预测。我们将PCNN与基于特征的优化相结合,发现了接近最优的编织结构,并促进了编织结构的初始设计。提出的框架将主要实现编织复合材料的分析和优化过程,并作为将知识引导神经网络引入复杂结构分析的起点。
Realizing ultrahigh strength and excellent stability of ultrasonically welded joints upon co-consolidating an extra resin layer (eRL) on the thermoplastic composites
Xuemin Wang, Dong Quan, Dongsheng Yue, Jiaming Liu, Jiaying Pan, Guoqun Zhao
doi:10.1016/j.compositesa.2024.108475
通过在热塑性复合材料上添加额外的树脂层(eRL),实现超声焊接接头的超高强度和优异的稳定性
Ultrasonic welding is a promising technique well-suited for joining thermoplastic composites. On the way towards upscaling and industrializing this technology, it is crucial to improve the welding process stability and joint structure integrity. Herein, a novel-structured carbon fiber (CF)/polyetherimide (PEI) composite topped with an extra PEI resin layer was developed using a co-consolidation process for ultrasonic welding. The experimental results proved that the extra resin layer effectively promoted the heat generation efficiency at the joining interface, improved the quality and uniformity of the welding line, as well as played a thermal barrier role to prevent composite overheating. This significantly improved the welding stability and the mechanical performance of the joints. For example, a superior lap-shear strength of 45.9 MPa was obtained by simultaneously optimizing the thicknesses of the extra resin layer and the energy director. Moreover, the bending and squeezing-out of carbon fibers at the welding interface were successfully eliminated.
超声波焊接是一种很有前途的热塑性复合材料焊接技术。在实现该技术规模化和产业化的道路上,提高焊接过程的稳定性和接头结构的完整性至关重要。本文研究了一种新型结构的碳纤维(CF)/聚醚酰亚胺(PEI)复合材料,其顶部有额外的PEI树脂层,采用共固结工艺用于超声波焊接。实验结果表明,额外的树脂层有效地提高了连接界面的产热效率,提高了焊缝的质量和均匀性,并起到了防止复合材料过热的热障作用。这大大提高了焊接稳定性和接头的力学性能。例如,通过同时优化额外树脂层和能量导向层的厚度,获得了45.9 MPa的优越剪切强度。此外,还成功地消除了碳纤维在焊接界面处的弯曲和挤压。
Wide-Response-Range and High-Sensitivity Piezoresistive Sensors with Triple Periodic Minimal Surface (TPMS) Structures for Wearable Human-Computer Interaction Systems
Jiahong Han, Zhongming Li, Shuoshuo Kong, Shan Tang, Dong Feng, Bin Li
doi:10.1016/j.compositesb.2024.111840
用于可穿戴人机交互系统的三周期最小表面结构的宽响应范围高灵敏度压阻传感器
High-performance pressure sensors are garnering interest in human-computer interaction technology, wearable devices, and bionic electronic skin development. However, highly sensitive sensors frequently have a limited response range. In this work, we developed composites with outstanding conductive network structures through the synergistic effect of transition metal carbides (MXene) and multi-walled carbon nanotubes (MWCNTs). Additionally, pressure sensors with various TPMS structures were prepared using innovative parametric design and Fused Deposition Molding (FDM) printing. Due to the stable synergistic conductive network and distinctive curved surface structure, the sensors exhibit exceptional sensing performance. This includes high sensitivity ranging from 4.67 MPa-1 to 7.03 MPa-1 (within the range of 0-0.1 MPa), a broad operating range (maximum 10 MPa), rapid response and recovery times (326 ms/193.4 ms), and long-term fatigue resistance (over 10,000 s cycles). By integrating mechanical properties, sensing properties, and finite element simulations, we analyzed the mechanism underlying the impact of various TPMS pore structures on the sensitivity and response range of the pressure sensor. In addition, the sensors were arrayed as 4×4 modules to successfully recognize a wide range of foot movements from different volunteers. These findings illuminate potential applications in human motion detection, healthcare rehabilitation, and artificial intelligence.
高性能压力传感器在人机交互技术、可穿戴设备和仿生电子皮肤的开发中引起了人们的兴趣。然而,高灵敏度的传感器往往有一个有限的响应范围。在这项工作中,我们通过过渡金属碳化物(MXene)和多壁碳纳米管(MWCNTs)的协同作用,开发了具有优异导电网络结构的复合材料。此外,采用创新的参数化设计和熔融沉积成型(FDM)打印技术制备了各种TPMS结构的压力传感器。由于稳定的协同导电网络和独特的曲面结构,传感器表现出优异的传感性能。这包括4.67 MPa-1至7.03 MPa-1 (0-0.1 MPa范围内)的高灵敏度,宽工作范围(最大10 MPa),快速响应和恢复时间(326 ms/193.4 ms),以及长期抗疲劳性(超过10,000 s循环)。通过综合力学性能、传感性能和有限元模拟,分析了不同TPMS孔结构对压力传感器灵敏度和响应范围的影响机制。此外,传感器被排列成4×4模块,以成功识别来自不同志愿者的各种足部运动。这些发现阐明了在人体运动检测、医疗康复和人工智能方面的潜在应用。