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.

Polymers enhance sports goods, offering lightweight, durable, and eco-friendly designs. This review explores the types, applications, and future trends of polymers while addressing challenges like cost and durability.