Effects of the carbon nanotube distribution on the macroscopic stiffness of composite materials
Having extremely high stiffness and low specific weight, carbon nanotubes (CNTs) have been known recently as perfect reinforcing fibers in nanotechnology. They can improve the stiffness and strength of nanocomposites by being used as reinforcing elements for example in polymer matrices. The correspo...
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| Main Authors: | , , |
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| Format: | Article |
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Elsevier Ltd.
2012
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/46902/ http://dx.doi.org/10.1016/j.commatsci.2011.08.003 |
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| Summary: | Having extremely high stiffness and low specific weight, carbon nanotubes (CNTs) have been known recently as perfect reinforcing fibers in nanotechnology. They can improve the stiffness and strength of nanocomposites by being used as reinforcing elements for example in polymer matrices. The corresponding properties of the fibers and matrix, such as volume fraction and aspect ratio are some of the significant factors in the characterization of mechanical properties of CNT reinforced composites. In recent years, the way in which fibers are distributed inside the matrix, in terms of randomness, has introduced another important factor in characterizing the mechanical properties of such composites. Based on this factor, composites can be classified into two types namely, aligned and randomly distributed. This research has studied the effect of random distribution of fibers in the matrix on the elastic modulus and initial yield stress of the nanocomposite. Therefore, several models of composites, with different distribution of fibers, were considered while holding the volume fractions and aspect ratio constant. As a result, the effect of randomness on the effective modulus of CNT reinforced composites was estimated. The finite element method (FEM), using the MSC.Marc software, was employed to predict the effective modulus of CNT reinforced composites and the results were successfully validated by comparison with the analytical Halpin–Tsai method. |
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