Predictive model for carbon nanotube–reinforced nanocomposite modulus driven by micromechanical modeling and physical experiments

Abstract

This article proposes an improved surrogate model for the prediction of the elastic modulus of carbon nanotube–reinforced-nanocomposites. By statistically combining micromechanical modeling results with limited amounts of experimental data, a better predictive surrogate model is constructed using a two-stage sequential modeling approach. A set of data for multi-walled carbon nanotube–bismaleimide nanocomposites is used in a case study to demonstrate the effectiveness of the proposed surrogate modeling procedure. In the case study, the theoretical composite modulus is computed with micromechanical models, and the experimental modulus is measured through tensile tests. Both theoretical and experimental composite moduli are integrated by using a statistical adjustment method to construct the surrogate model. The results demonstrate an improved predictive ability compared to the original micromechanical model.

Keywords:

• Surrogate modeling
• carbon nanotube
• micromechanical modeling
• nanocomposite
• statistical model adjustment