Development of artificial intelligence based model for the prediction of Young's modulus of polymer/carbon-nanotubes composites
参考中译:基于人工智能的聚合物/碳纳米管复合材料杨氏模数预测模型的建立


          

刊名：Mechanics of Advanced Materials and Structures
作者：Nang Xuan Ho（Faculty of Vehicle and Energy Engineering, PHENIKAA University）
Tien-Thinh Le（PHENIKAA Research and Technology Institute (PRATI), A&A Green Phoenix Group JSC）
Minh Vuong Le（Laboratoire Modelisation et Simulation Multi Echelle, Universite Paris-Est）
刊号：712C0115
ISSN：1537-6494
出版年：2022
年卷期：2022, vol.29, no.27
页码：5965-5978
总页数：14
分类号：TB33
关键词：AI；ML；Nanocomposites；Neural network；Polymer；Composite properties
参考中译：人工智能；ML；纳米复合材料；神经网络；聚合物；复合材料性能
语种：eng
文摘：In this paper, an Artificial Intelligence (AI) model is constructed for the behavior prediction, i.e. Young's modulus, of polymer/carbon-nanotube (CNTs) composites. The AI is proposed to overcome the difficulties when studying the properties of novel composite materials, for example the time-consuming of experimental studies of resource-consuming of other numerical methods. Artificial Neural Network (ANN) model was chosen and optimized in architecture based on a parametric study. The main objective of this study is to firstly confirm that the proposed AI method performs well for nanocomposites and it can then be optimized in terms of computational time and resources in further studies. The obtained results have shown that the proposed model exhibits great performance in both training and testing phases, where the correlation coefficient is 0.986 for training part and 0.978 for the testing part.
参考中译：建立了聚合物/碳纳米管复合材料行为预测的人工智能模型，即杨氏S模数。人工智能的提出是为了克服研究新型复合材料性能的困难，例如其他数值方法的实验研究耗费资源的耗时。在参数研究的基础上，选择了人工神经网络(ANN)模型，并对其进行了优化。这项研究的主要目的是首先确认所提出的人工智能方法对纳米复合材料具有很好的性能，然后在进一步的研究中可以在计算时间和资源方面进行优化。结果表明，该模型在训练阶段和测试阶段都表现出了良好的性能，训练阶段的相关系数为0.986，测试阶段的相关系数为0.978。



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