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

Abstract

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.

Keywords:

• AI
• ML
• nanocomposites
• neural network
• polymer
• composite properties

Acknowledgements

The authors would like to thank Prof. J. Guilleminot (Duke University, Durham, USA), for his helpful advice and comments on this paper.

Conflicts of interest

The authors declare no conflict of interest.

Additional information

Funding

This research is funded by PHENIKAA University under grant number 2-05.2020.04.