Development of artificial intelligence based model for the prediction of Young's modulus of polymer/carbon-nanotubes composites

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-co...

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Bibliographic Details
Published inMechanics of advanced materials and structures Vol. 29; no. 27; pp. 5965 - 5978
Main Authors Ho, Nang Xuan, Le, Tien-Thinh, Le, Minh Vuong
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 14.12.2022
Taylor & Francis Ltd
Subjects
Artificial intelligence
Artificial neural networks
Carbon nanotubes
composite properties
Computing time
Correlation coefficients
Modulus of elasticity
Nanocomposites
neural network
Numerical methods
polymer
Polymer matrix composites
Polymers
Training
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Summary: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.
ISSN:1537-6494
1537-6532
DOI:10.1080/15376494.2021.1969709