Multi-scale Computational Analysis of Metal Matrix Nanocomposites

Abstract

Metal matrix nanocomposites (MMNCs) have been used in automotive and strategic industries due to their inherent characteristics like high specific strength and favorable thermal behavior. MMNCs are subjected to different environmental conditions and thus their behavior changes with time. However, the reinforcements tailor their overall properties but a study before their practical use is necessary. Thus, an efficient and robust technique is needed to analyze the behavior of MMNCs under their service conditions. Most computational analyses have been concentrated on the determination of the mechanical, electrical, and thermal properties of MMNTs using the molecular dynamics approach and some research on the bending-buckling of MMNTs. The main objective of this chapter is to summarize the multi-scale computational techniques to study the elastic, elastoplastic, and thermal behavior of MMNTs. The elastic and elastoplastic behavior of MMNTs varies with their thermo-mechanical service environment. Also, the micro-scale behavior of a material is different from the macro-scale behavior. Therefore, this chapter focuses on the multi-scale investigation of MMNCs for various engineering applications. Metal matrix nanocomposites are emerging materials for structural applications. Apart from their remarkable mechanical, electrical, and thermal properties, their use is questionable due to difficulties in processing. The nanotube-reinforced composites are especially featured in research due to control of the spatial distribution in the matrix. Also, reducing agglomeration or clustering is a hot topic while manufacturing the MMNCs. Clustering may also produce hot spots, stress concentration, and faster growth in a crack. The effect of clustering has been studied theoretically and has shown an appreciable decrease in the material properties. But controlling the clustering during manufacturing is a challenging task. On the positive side, these nanocomposites have gained popularity in the applications of high speed, high thermal, and severe working environments. This has become possible only due to the study of these composites at a micro-scale. With the advancement in computational technology, the exact non-linear behavior of nanocomposites can be studied. Not only elastic but elasto-plastic, visco-elastic, visco-elasto-plastic, thermo-elastic, and many other responses can be predicted. The integration between micro and macro scale using the multi-scale approaches has enabled faster and more efficient research. Therefore, we have provided information on the multi-scale analysis of MMNCs in this chapter. The popular and efficient techniques, such as Mori–Tanaka and finite element method (FEM), have been summarized. The effect of a few crucial parameters on the overall or effective material properties was also outlined. Hybrid metal matrix nanocomposites have also gained popularity because of in-situ, ex-situ, and additive manufacturing processes. The second phase added to MMNCs enhanced the mechanical tribological and thermal properties. Many hybrid nanocomposites have been developed for some specific applications, but still novel manufacturing techniques are yet to be explored. Therefore, critical computational analysis was the requirement to study these hybrid composites. The representative volume element (RVE)-based micro-mechanical modeling has been extensively used to predict the overall effective properties of hybrid nanocomposites. The effect of different parameters like shape, size, spatial distribution, aggregation, and volume fraction are vital to effectively predict the material response. The computational study of hybrid nanocomposites before manufacturing can save a lot of research time and cost. Therefore, the development of more prediction models is the need of the research society.