【新文速递】2024年5月23日复合材料SCI期刊最新文章

11天前浏览250

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

Composite Structures

Low-frequency vibration bandgaps and deep learning-based intelligent design method of Y-shaped core sandwich metabeams

Dingkang Chen, Yinggang Li, Ziyang Pan, Xunyu Li, Tianle Xu, Xiaobin Li

doi:10.1016/j.compstruct.2024.118214

y型芯芯夹层元梁低频振动带隙及基于深度学习的智能设计方法

In this paper, the wave propagation mechanics theoretical model of Y-shaped core sandwich metabeams with local resonators is established based on the spectral element method, and the flexural wave bandgaps and vibration isolation characteristics are studied. The reliability of the theoretical model of Y-shaped core sandwich metabeams is verified by the finite element method and experiment. On this basis, a deep learning-based intelligent design method for predicting the vibration transmission characteristics and structure design of Y-shaped core sandwich metabeams is proposed. A dataset is created using the theoretical model of wave dynamics, and deep neural networks are constructed to forward prediction of transmission characteristics and intelligent design of Y-shaped core sandwich metabeams, respectively. Results indicate that the bandgap range of Y-shaped core sandwich metabeams is 2.5 times that of Y-shaped core sandwich beams, and the start frequency of bandgap is reduced by 578 Hz. The prediction of the transmission characteristic curve is in good agreement with the target, and the average relative error of intelligent design is below 3 %, which verifies the precision of the intelligent design method. The innovative, intelligent design method provides a novel way to realize engineering vibration reduction design of acoustic metamaterials rapidly

基于谱元法，建立了带有局部谐振腔的y型芯芯夹层元梁的波传播力学理论模型，研究了其弯曲带隙和隔振特性。通过有限元法和试验验证了y型芯芯夹层元梁理论模型的可靠性。在此基础上，提出了一种基于深度学习的预测y型芯芯夹层元梁振动传递特性和结构设计的智能设计方法。利用波浪动力学理论模型建立数据集，构建深度神经网络，分别对y型芯芯夹层元梁的传输特性进行正演预测和智能设计。结果表明，y型芯芯夹层超梁的带隙范围是y型芯芯夹层超梁的2.5倍，带隙起始频率降低578 Hz。预测的传动特性曲线与目标吻合较好，智能设计的平均相对误差在3 %以下，验证了智能设计方法的精度。这种创新的智能设计方法为快速实现声学超材料的工程减振设计提供了新的途径

Composites Part B: Engineering

Non-covalent Interaction Induces Controlled Reinforcement of Thermoplastic Elastomer Composites Homologously Incorporated with Hydrophobized Cellulose Nanocrystals

Hyunho Lee, Seok Yeol Yoo, Donggyu Kim, Se Young Kim, Haemin Jeong, Jong Yeul Seog, Jeong Suk Yuk, Eunseon Noh, Woojin Jeong, Yoonsu Park, Sae Hume Park, Jihoon Shin

doi:10.1016/j.compositesb.2024.111579

非共价相互作用诱导疏水纤维素纳米晶体均匀掺入热塑性弹性体复合材料的可控增强

Surface hydrophobization of cellulose nanocrystal (CNC) is developed through Pickering emulsion method and esterification-grafting, consequentially enhancing its thermal properties and hydrophobicity, and tensile modulus in thermoplastic elastomer composites. n-Tetradecenylsuccinic anhydride (TDSA) provides a high level of compatibility owing to the structural homology by non-covalent interactions between CNC-graft-TDSA and poly(styrene)-block-poly(isoprene)-block-poly(styrene) (SIS). Theoretical calculations supports the molecular-level insight on how the nanofiller and SIS interacts. Density functional theory calculations and the energy decomposition analysis reveal the stabilization free energy of −2.59 kcal mol−1 gained through hydrophobic and OH–π interactions, which is assigned as the key component for the superior mechanical properties. At the filler contents of 0.6−2.5 vol %, the moduli of the TPE nanocomposites gradually strengthen while retaining thermoplasticity, without sacrificing the ultimate tensile characteristics of SIS, although it is below the calculated φc of 5.9 vol % (critical percolation threshold) for filler-filler interactions. Origin of the controlled boost in filler-matrix binding is rationalized by pronounced hydrophobic interactions in isoprene-rich rubbery phase (86 wt % in SIS). Interestingly, at 5.1 vol % near to filler-filler interactions, even though reinforcements in storage/initial modulus, and toughness increase by up to 14/4.0-, and 1.2-fold, the tensile stress at break only decreases by 27%.

通过皮克林乳液法和酯化接枝法对纤维素纳米晶（CNC）进行表面疏水化，从而提高了其热性能、疏水性以及在热塑性弹性体复合材料中的拉伸模量。正十四烯基丁二酸酐（TDSA）通过 CNC-接枝-TDSA 与聚（苯乙烯）-块状-聚（异戊二烯）-块状-聚（苯乙烯）（SIS）之间的非共价作用形成结构同源性，因此具有很高的兼容性。理论计算支持从分子层面深入了解纳米填料和 SIS 之间的相互作用。密度泛函理论计算和能量分解分析表明，通过疏水和 OH-π 相互作用获得的稳定自由能为 -2.59 kcal mol-1，这被认为是获得优异机械性能的关键因素。在填料含量为 0.6-2.5 Vol % 时，TPE 纳米复合材料的模量逐渐增强，同时保持了热塑性，尽管低于计算得出的填料-填料相互作用的φc 5.9 Vol %（临界渗流阈值），但并没有牺牲 SIS 的极限拉伸特性。富含异戊二烯的橡胶相（在 SIS 中占 86 wt %）中明显的疏水相互作用是填料与基质结合力受控增强的原因。有趣的是，在填料-填料相互作用的 5.1 体积%附近，尽管存储/初始模量和韧性增强了 14/4.0 倍和 1.2 倍，但断裂拉伸应力仅降低了 27%。

Shape Compensation for Carbon Fiber Thermoplastic Composite Stamp Forming

R. Byron Pipes, Justin Hicks, Eduardo Barocio, Kwanchai Chinwicharnam, Shigeto Yamamoto

doi:10.1016/j.compositesb.2024.111577

碳纤维热塑性复合材料冲压成形的形状补偿

Shape compensation of tooling for stamp forming of carbon fiber thermoplastic laminated composites is described as a process wherein the virtual twin, FORM3D is utilized to determine the tooling geometry required to produce the desired part geometry in a single step process. FORM3D is a physics-based virtual twin that incorporates anisotropic phenomena including transient heat transfer, thermoviscoelasticity, thermoelastic and crystallization shrinkage, and polymer crystallization and melting kinetics. This procedure for determining the compensated tool geometry is illustrated for a composite laminate constructed of AS4/PEKK prepreg, supplied by Solvay Thermoplastic Composites, for a quasi-isotropic layup of variable thickness and double curvature: major radius of curvature of 1556.7 mm and minor radius of curvature of 75.7 mm. The resulting, compensated shape was shown to agree with the desired geometry within +0.25 mm.

碳纤维热塑性层压复合材料冲压成形模具的形状补偿被描述为一个过程，其中虚拟孪生体FORM3D被用来确定在单步工艺中生产所需零件几何形状所需的模具几何形状。FORM3D是一种基于物理的虚拟孪生体，结合了瞬态传热、热粘弹性、热弹性和结晶收缩、聚合物结晶和熔化动力学等各向异性现象。本文介绍了由Solvay Thermoplastic Composites公司提供的AS4/PEKK预浸料构成的复合材料层压板的补偿刀具几何形状的确定过程，该层压板具有可变厚度和双曲率的准各向同性层压:主要曲率半径为1556.7 mm，次要曲率半径为75.7 mm。结果显示，补偿形状在+0.25 mm内与所需的几何形状一致。

Composites Science and Technology

Buckling and failure assessment of curved butt-joint stiffened thermoplastic composite panels with roller boundary conditions

Kevin van Dooren, Jan Waleson, Mark Chapman, Chiara Bisagni

doi:10.1016/j.compscitech.2024.110667

具有滚子边界条件的弯曲对接加劲热塑性复合材料板屈曲与破坏评估

Two curved thermoplastic composite multi-stringer panels with roller boundary conditions are analysed and tested to investigate the buckling and failure behaviour. The panels are made of AS4D/PEKK-FC thermoplastic composite, have five stringers with an angled cap on the side and are joined to the skin with the short-fibre reinforced butt-joint technique. The panels have a roller attached to each loading edge, approximating simply-supported boundary conditions to apply compression and bending. One panel has an initial damage representing a barely visible impact damage in one of the stringer butt-joints, and one panel is in pristine condition. Finite element analyses are performed to predict the structural behaviour, and different approximations of the roller boundary conditions are compared. The analyses include material damage initiation and evolution. The out-of-plane displacement of the panels is measured by digital image correlation, and failure is captured with high-speed cameras. The panels fail in a sudden manner when the cap separates from the web, followed by web failure and skin-stringer separation in the butt-joint. The numerical analysis predicts the overall structural behaviour but cannot capture well the sudden panel collapse due to material damage.

对两种具有滚子边界条件的弯曲热塑性复合材料多弦板进行了屈曲和破坏行为分析和试验。面板由AS4D/PEKK-FC热塑性复合材料制成，侧面有五个带角度帽的弦，并通过短纤维增强对接技术连接到皮肤上。面板有一个滚轮连接到每个加载边缘，近似简支边界条件施加压缩和弯曲。一个面板的初始损伤在其中一个弦对接处几乎看不出撞击造成的损伤，另一个面板处于原始状态。进行了有限元分析来预测结构性能，并比较了不同近似的滚子边界条件。分析包括材料损伤的起裂和演化。采用数字图像相关测量面板的面外位移，并用高速摄像机捕捉故障。当帽板与腹板分离时，板板突然失效，随后腹板失效，并在对接处发生皮筋分离。数值分析可以预测结构的整体性能，但不能很好地捕捉由于材料损伤而导致的面板突然倒塌。

来源：复合材料力学仿真Composites FEM

【新文速递】2024年5月4日复合材料SCI期刊最新文章

今日更新：Composites Part A: Applied Science and Manufacturing 1 篇，Composites Part B: Engineering 1 篇Composites Part A: Applied Science and ManufacturingAchieving excellent physico-mechanical properties of Cu matrix composites by incorporating a low content of a three-dimensional graphene networkJunrui Huang, Yubo Zhang, Xi Yang, Jiajing Liu, Xiaona Li, Tingju Lidoi:10.1016/j.compositesa.2024.108246 通过加入低含量的三维石墨烯网络，获得优异的Cu基复合材料的物理力学性能In-depth investigation of the distribution form of the reinforcement and associated mechanisms has great significance for fabricating highly strengthened and conductive Cu matrix composites. The three-dimensional graphene (3DG) network can withstand more strain due to its large specific surface area, and provides interlinked high-speed conductive paths within the composites. In this study, we propose a feasible method to fabricate 3DG-reinforced Cu matrix composites; here, the Cu foam serves as the initial substrate during the chemical vapour deposition (CVD) process and as the matrix of the composites. Through constructing an ideal 3DG network, the graphene content is approximately 0.28 vol%, and a remarkable 230 % improvement in the ultimate tensile strength (418 MPa) with a high electrical conductivity (97.02 %IACS) is obtained in the composite. The continuous high-quality 3DG network is conducive for achieving excellent physico-mechanical properties of the composites, and the enhanced efficiency is superior. A new concept of fabricating Cu matrix composites was proposed for future research.深入研究增强的分布形式及其机制对制备高强度导电铜基复合材料具有重要意义。三维石墨烯(3DG)网络由于其较大的比表面积可以承受更多的应变，并在复合材料内提供相互连接的高速导电路径。在本研究中，我们提出了一种可行的3d增强Cu基复合材料制备方法;在这里，Cu泡沫作为化学气相沉积(CVD)过程中的初始衬底和复合材料的基体。通过构建理想的3DG网络，石墨烯含量约为0.28 vol%，复合材料的抗拉强度(418 MPa)提高了230 %，电导率(97.02 %IACS)较高。连续高质量的3DG网络有利于获得优异的复合材料物理力学性能，增强效率优越。提出了一种制备铜基复合材料的新思路。Composites Part B: EngineeringEfficient analysis of composites manufacturing using multi-fidelity simulation and probabilistic machine learningCaleb Schoenholz, Enrico Zappino, Marco Petrolo, Navid Zobeirydoi:10.1016/j.compositesb.2024.111499基于多保真度仿真和概率机器学习的复合材料制造高效分析This paper introduces an innovative approach for the efficient analysis of composites manufacturing processes and phenomena. The method combines low- and high-fidelity simulation schemes with limited amounts of experimental data to train surrogate machine learning (ML) models. Guided by a novel approach, Spatially Weighted Gaussian Process Regression (SWGPR), a predictive model is efficiently constructed and calibrated by assigning datapoint-dependent noise levels to simulation points, establishing a multi-scale data-driven uncertainty structure. This study demonstrates the effectiveness of the method in accurately predicting process-induced deformations (PIDs) for L-shaped cross-ply laminates using minimal experimental efforts. The presented method aims to provide a cost-effective and broadly applicable framework for understanding and improving the design, development, and manufacturing of composites.本文介绍了一种有效分析复合材料制造过程和现象的创新方法。该方法结合了低保真度和高保真度仿真方案以及有限数量的实验数据来训练代理机器学习(ML)模型。在空间加权高斯过程回归(SWGPR)方法的指导下，通过将数据点相关的噪声水平分配给模拟点，有效地构建和校准预测模型，建立多尺度数据驱动的不确定性结构。本研究证明了该方法在使用最小实验努力准确预测l形交叉层合板的过程诱导变形(pid)方面的有效性。提出的方法旨在为理解和改进复合材料的设计、开发和制造提供一个具有成本效益和广泛适用的框架。来源：复合材料力学仿真Composites FEM