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.
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
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.
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.
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.
