Purpose The purpose of this study is to evaluate the effective elastic properties of carbon fiber/epoxy by adding carbon nanotubes and understand their mechanical performance using analytical and simulation approaches based on finite element analysis. Design/methodology/approach In Ansys material design modular, 3D RVE is designed to evaluate the effective elastic property of carbon fiber/epoxy by adding carbon nanotubes (CNT). A micromechanical approach has been implemented to validate the simulation-based effective elastic property of carbon fiber/epoxy +CNT. Thereafter, the artificial neural network (ANN) model is trained to predict the elastic properties of carbon fiber/epoxy + CNT, such as longitudinal Young’s modulus, transverse Young’s modulus and shear modulus. For further study, carbon fiber/epoxy + CNT composite laminate is created in Ansys ACP. In Ansys, tensile and bending tests have been performed, and a classical laminate theory (CLT) is developed to validate the simulation result. Findings The study demonstrates that incorporating carbon nanotubes (CNT) into carbon fiber/epoxy composites significantly enhances their elastic properties. In comparison to 1% CNT (Carbon fiber/epoxy + CNT1%), this study showed that 2.5% CNT (Carbon fiber/epoxy + CNT 2.5%) increased the longitudinal Young’s modulus by 8% and the shear modulus by 3%. Moreover, adding carbon nanotube content increased the bending and tensile strength, and the ANN model accurately predicted elastic characteristics with an R-squared of 0.99. Originality/value This study provides a valuable contribution to understanding that adding carbon nanotube to the carbon fiber could enhance mechanical performance through analytical and simulation approaches. By using 3D RVE, micromechanical validation and classical laminate theory, this work investigates how adding carbon nanotube could improve the effective elastic properties of carbon fiber. The work obtains a remarkable R-squared of 0.99 by combining artificial neural networks (ANN) with finite element analysis in a new way that generates accurate predictions of elastic characteristics.
Shohel et al. (Fri,) studied this question.