【新文速递】2024年1月14日复合材料SCI期刊最新文章
今日更新：Composite Structures 1 篇，Composites Part A: Applied Science and Manufacturing 1 篇，Composites Part B: Engineering 1 篇，Composites Science and Technology 1 篇
Composite Structures
vAnalysis of cure kinetics of CFRP composites molding process using incremental thermochemical information aggregation networks
Bo Yang, Haoping Huang, Fengyang Bi, Liqiong Yin, Qi Yang, Hang Shen
doi:10.1016/j.compstruct.2024.117904
基于增量热化学信息聚合网络的CFRP复合材料成型过程固化动力学分析
This work focuses on addressing the pain points of poor generalization performance and difficulty in continuous learning that exist in the phenomenological and neural network surrogate models. Therefore, this study proposes a lightweight adaptive thermochemical information aggregation networks (ATANets) to overcome the gradient conflict challenge, and combines the generative knowledge distillation (GKD) algorithm to compress the model to capture finer-grained and enriched information on kinetics behaviors, which yields a thermochemical feature information extraction networks (FENets) with incremental learning capability. The experimental results demonstrated that as the complexity of the learning task deepened, the FENets models obtained by incremental training with ATANets and GKD still had excellent continuous learning capability, with the relative variations of the coefficients of determination and the mean square error being smaller than 6.7×10-3 and 0.3860×10-3, respectively. Meanwhile, the accurate characterization of cure kinetics behaviors was achieved in the thermochemical coupling analysis of CFRP, with the maximum values of the average and maximum temperature differences of 0.0176 °C and 0.2538 °C, respectively. Overall results show that our proposed incremental model is remarkably preferable to existing models and is beneficial in promoting the widespread reuse of the existing knowledge of cure kinetics behavior of resins in this domain.
这项工作的重点是解决现象学和神经网络代理模型中存在的泛化性能差和持续学习困难的痛点。因此，本研究提出了一种轻量级的自适应热化学信息聚合网络(ATANets)来克服梯度冲突挑战，并结合生成知识蒸馏(GKD)算法对模型进行压缩，以捕获更细粒度和更丰富的动力学行为信息，从而产生具有增量学习能力的热化学特征信息提取网络(FENets)。实验结果表明，随着学习任务复杂度的加深，使用ATANets和GKD增量训练得到的fenet模型仍然具有优异的连续学习能力，决定系数和均方误差的相对变化量分别小于6.7×10-3和0.3860×10-3。同时，在CFRP的热化学偶联分析中，实现了固化动力学行为的准确表征，平均温差最大值为0.0176℃，最大温差最大值为0.2538℃。总体结果表明，我们提出的增量模型明显优于现有模型，有利于促进该领域树脂固化动力学行为现有知识的广泛重用。
Composites Part A: Applied Science and Manufacturing
A self-supervised learning framework based on physics-informed and convolutional neural networks to identify local anisotropic permeability tensor from textiles 2D images for filling pattern prediction
John M. Hanna, José V. Aguado, Sebastien Comas-Cardona, Yves Le Guennec, Domenico Borzacchiello
doi:10.1016/j.compositesa.2024.108019
基于物理信息和卷积神经网络的自监督学习框架，从纺织品二维图像中识别局部各向异性渗透率张量，用于填充图案预测
In liquid composite molding processes, variabilities in material and process conditions can lead to distorted flow patterns during filling. These distortions appear not only within the same part but also from one part to another. Notably, minor deviations in the dry fibrous textiles cause local permeability changes, resulting in flow distortions and potential defects. Traditional permeability models fall short in predicting these localized fluctuations, especially for anisotropic textiles, whereas reliance on homogeneous permeability models creates substantial discrepancies between forecasted and observed filling patterns. This study presents a self-supervised framework that determines in-plane permeability tensor field of textiles from an image of that textile in dry state. Data from central injection experiments is used for training, including flow images and pressure inlet data. This work demonstrates that this model proficiently predicts flow patterns in unobserved experiments and captures local flow distortions, even when trained on a relatively small dataset of experiments.
在液体复合成型过程中，材料和工艺条件的变化会导致填充过程中流动模式的扭曲。这些扭曲不仅出现在同一部分，而且从一个部分到另一个部分。值得注意的是，干燥纤维纺织品中的微小偏差会导致局部渗透性变化，从而导致流动扭曲和潜在缺陷。传统渗透率模型在预测这些局部波动方面存在不足，特别是对于各向异性纺织品，而依赖均质渗透率模型会在预测和观察到的填充模式之间产生巨大差异。本研究提出了一种自监督框架，从纺织品干燥状态的图像中确定纺织品的面内渗透率张量场。中心注射实验数据用于训练，包括流动图像和入口压力数据。这项工作表明，即使在相对较小的实验数据集上训练，该模型也能熟练地预测未观察到的实验中的流动模式，并捕获局部流动扭曲。
Composites Part B: Engineering
Concerns in tension-tension fatigue testing of unidirectional composites: Specimen design and test setup
Babak Fazlali, Stepan V. Lomov, Yentl Swolfs
doi:10.1016/j.compositesb.2024.111213
单向复合材料拉伸-拉伸疲劳试验中的问题:试样设计和试验设置
Tension-tension fatigue of unidirectional (UD) composites is often used to represent the fatigue behavior of composites. The standard proposes to use end tabs for UD composites. However, obtaining a reliable S–N curve requires avoiding premature failure, which in turn requires minimizing stress concentrations near the grips and end tabs. This work examines the fatigue life of different specimen and end tab designs. Conventional end tabs and other well-known designs, along with novel arrow end tabs, are tested to identify the method that is least influenced by the stress concentrations and yields the highest and the most valid fatigue life. Rectangular specimens with rectangular and tapered end tabs, which is the standard configuration, yielded the highest fatigue life. This differs from the preferences in the quasi-static test, where arrow end tabs performed the best. Moreover, the effect of parameters in the manufacturing process and test setup on the fatigue life of two different carbon fiber/epoxy prepregs material systems are discussed. The results reveal the significant effect of the test setup on tension-tension fatigue of UD composites and will inform the community on how to perform more reliable fatigue tests.
单向复合材料的拉伸-拉伸疲劳常被用来表示复合材料的疲劳行为。该标准建议在UD复合材料中使用结束标签。然而，获得可靠的S-N曲线需要避免过早失效，这反过来又需要最小化握把和末端卡箍附近的应力集中。本工作考察了不同试样和端片设计的疲劳寿命。测试了传统的端卡和其他知名的设计，以及新型的箭头端卡，以确定受应力集中影响最小的方法，并产生最高和最有效的疲劳寿命。具有矩形和锥形端片的矩形试样是标准配置，产生了最高的疲劳寿命。这与准静态测试中的首选项不同，在准静态测试中，箭头结束选项卡表现最好。此外，还讨论了制造工艺参数和试验设置对两种不同碳纤维/环氧预浸料材料体系疲劳寿命的影响。研究结果揭示了试验设置对UD复合材料拉伸-拉伸疲劳的显著影响，并将为如何进行更可靠的疲劳试验提供指导。
Composites Science and Technology
Bayesian optimization-based prediction of the thermal properties from fatigue test IR imaging of composite coupons
Martin Demleitner, Rodrigo Q. Albuquerque, Ali Sarhadi, Holger Ruckdäschel, Martin A. Eder
doi:10.1016/j.compscitech.2024.110439
基于贝叶斯优化的复合材料疲劳红外成像热性能预测
The prediction of the prevailing self-heat transfer parameters of a glass/epoxy composite coupon during fatigue testing in general and the distinction between viscoelastic- and frictional crack growth-related energy dissipation in particular, are not trivial problems. This work investigates the feasibility of predicting the convective film coefficient, the total work loss as well as the ratio between viscoelastic and fracture-induced damping from thermal images using Bayesian optimization in conjunction with 3D FE thermal analysis. To this end, glass fiber/epoxy biax coupons are pre-damaged by means of a drop weight impact machine and subsequently tested under uniaxial tension-tension high cycle fatigue conditions. IR images are taken of the self-heating thermal profile at steady-state conditions. Synthetic surface thermal images are generated by numerical thermal analysis of the damage distribution obtained byμ-CT scanning prior to testing. Bayesian optimization of the aforementioned parameters is conducted by minimizing the loss function between the as-measured and the synthetic IR image. The predicted work-loss is consequently validated against the measured hysteretic response, from which a very good agreement is found.
预测玻璃/环氧复合材料在疲劳试验中普遍存在的自传热参数，特别是区分粘弹性和摩擦裂纹扩展相关的能量耗散，不是一个简单的问题。本研究研究了利用贝叶斯优化结合三维有限元热分析，从热图像中预测对流膜系数、总功损失以及粘弹性和裂缝诱导阻尼之间的比率的可行性。为此，采用落锤冲击试验机对玻璃纤维/环氧双轴材料进行预损伤，并在单轴拉伸-拉伸高周疲劳条件下进行试验。对稳态条件下的自热热廓线进行了红外成像。对得到的损伤分布进行数值热分析，生成合成的表面热图像μ-测试前的ct扫描。上述参数的贝叶斯优化是通过最小化实测图像与合成红外图像之间的损失函数来实现的。因此，预测的工作损失与测量的滞后响应进行了验证，从中发现了非常好的一致性。
