今日更新:Composites Part B: Engineering 2 篇,Composites Science and Technology 1 篇
Composites Part B: Engineering
An efficient machine learning-based model for predicting the stress-strain relationships of thermoplastic polymers with limited testing data
Shengbo Ling, Zhen Wu, Jie Mei, Shengli Lv
doi:10.1016/j.compositesb.2024.111600
用有限的测试数据预测热塑性聚合物应力-应变关系的高效机器学习模型
Thermoplastic polymers used in aeronautical structures such as poly-ether-ether-ketone (PEEK) usually exhibit nonlinear stress-strain relationships, which can be usually predicted by the physical and phenomenological models. For different thermoplastic polymers, however, existing models may encounter difficulties in reasonably predicting the stress-strain relationships. By reasonably using experimental data, the machine learning-based model can accurately predict the stress-strain relationships. In this paper, an efficient machine learning-based model is built to predict the stress-strain relationships of thermoplastic polymers by using the Kriging model, where limited testing data are merely used. Experiments of 69 specimens for PEEK are firstly performed under uniaxial tensile, where the temperatures range from 23 °C to 140 °C and the strain rates range from 10-3 s-1 to 10-1 s-1. Genetic algorithm is employed to train the proposed model based on the stress-strain relationships obtained from experiments. Moreover, the results predicted by the proposed Kriging-based model are compared with those obtained from the DSGZ (Duan-Saigal-Greif-Zimmerman) model. The results indicate that the Kriging-based model possesses better accuracy, lower complexity, and better robustness than the selected model, because the prior estimation is introduced by the assumption of Gaussian stochastic processes. In addition, the proposed model is also applied to Poly-Propylene (PP), PEEK at high temperatures, and polycarbonate (PC), it can be found that the Kriging-based model can also predict the stress-strain relationships of other amorphous and semi-crystalline thermoplastic polymers.
A Sequential Meta-Transfer (SMT) Learning to Combat Complexities of Physics-Informed Neural Networks: Application to Composites Autoclave Processing
Milad Ramezankhani, Abbas. S.Milani
doi:10.1016/j.compositesb.2024.111597
序贯元迁移(SMT)学习以对抗物理信息神经网络的复杂性:应用于复合材料高压灭菌处理
Physics-Informed Neural Networks (PINNs) have gained popularity in solving nonlinear partial differential equations (PDEs) via integrating physical laws into the training of neural networks, making them superior in many scientific and engineering applications. However, conventional PINNs still fall short in accurately approximating the solution of complex systems with strong nonlinearity, especially in long temporal domains. Besides, since PINNs are designed to approximate a specific realization of a given PDE system, they lack the necessary generalizability to efficiently adapt to new system configurations. This entails computationally expensive re-training from scratch for any new change in the system. To address these shortfalls, in this work a sequential meta-transfer (SMT) learning framework is proposed, offering a unified solution for both fast training and efficient adaptation of PINNs in highly nonlinear systems with long temporal domains. Specifically, the framework decomposes PDE’s time domain into smaller time segments to create “easier” PDE problems for PINNs training. Then for each time interval, a meta-learner is assigned and trained to achieve an optimal initial state for rapid adaptation to a range of related tasks. Transfer learning principles are then leveraged across time intervals to further reduce the computational cost. Through a composites autoclave processing case study, it is shown that SMT is clearly able to enhance the adaptability of PINNs while significantly reducing computational cost, by a factor of 100.
Transverse Cracking Signal Characterization in CFRP Laminates using Modal Acoustic Emission and Digital Image Correlation Techniques
Michal Šofer, Jakub Cienciala, Pavel Šofer, Zbyněk Paška, František Fojtík, Martin Fusek, Pavel Czernek
doi:10.1016/j.compscitech.2024.110697
基于模态声发射和数字图像相关技术的CFRP层合板横向裂纹信号表征
The process of formation and subsequent propagation of transverse cracks in 90o plies of carbon-fiber laminated composites was studied using modal acoustic emission approach and digital image correlation techniques. The results from modal acoustic emission approach, which included a newly developed processing tool for acoustic emission waveforms, provided information for identification and subsequent characterization or localization of signals originating from transverse cracking by analysis of the separated flexural and extensional Lamb wave modes in terms of their modal parameters. The digital image correlation method served as a verification tool of the acoustic emission data outputs in the terms of activity of significant localized events originating from the formation of the transverse crack in the 90oply. This made it possible to specify more locally the accompanying activity belonging to the formation or propagation of the magistral transverse crack. The manuscript also presents results related to the evolution of flexural/extensional wave modal parameters as the function of loading force for both [0/0/0/90]S and [90/0/0/0]S panels. It can be concluded that the detection of transverse cracks requires the need for applying a more complex acoustic emission data analysis methodology, while the standard parametric analysis, including the waveform peak frequency, is not sufficient. The presented methodology may serve as a basis for development of robust analysis tool capable of detecting the investigated phenomena.