Evaluation of elastic properties of multi walled carbon nanotube reinforced composite

Abstract Exceptional mechanical properties like high strength, stiffness and aspect ratio exhibited by carbon nanotubes, make them ideal reinforcements for nanocomposites. In this paper load transfer in multi-walled carbon nanotube (MWCNT) composites is studied under tension and compression loading conditions. Continuum mechanics model is used to evaluate the effective material properties using a representative volume element (RVE) approach. Numerical results are obtained using Finite Element Modeling (FEM) and these results have been validated with rule of mixture results. FEM results are found to be quite closer to the results obtained from rule of mixture. In the present work we have considered a range of matrix material, the range covers the matrix material from metal to polymer, i.e. taken in a form of the ratio of effective modulus of elasticity of CNT to that of matrix material E t / E m from 5 to 200. With the addition of the multi-walled CNT in a matrix at the volume fractions of 5.1%, the stiffness of the composite is increased by 46% for compressive loading and 14.9% for tensile loading, as compared with that of the matrix in the case of long CNT at E t / E m  = 10. Multi-walled carbon nanocomposite are found to provide better value of young’s modulus in compression as compared in tension, this is due to the higher inter-tube load transfer in compression. Comparative evaluation of material properties with single walled carbon nanocomposite is also done. It is established that multi-walled carbon nanotube composite provide a better resistance against compression as compared to single walled carbon nanotube composite. Effect of change in diameter and length of multi-walled carbon nanotube on stiffness of nanocomposite have also been investigated. Longer multi-walled carbon nanotubes are found to be more effective in reinforcing the composite as compared to shorter ones. FEM results are also found to be in close approximation with the experimental results, which validates the current model.

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