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Numerical study on grain evolution of gradient-structured aluminum matrix composites induced by graphene nanoplatelets

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Abstract

Gradient composite materials are widely used because of their excellent combination of strength and toughness. Nano-reinforced Al-matrix composite material is one of the most applied structures. The primary goal of this work is to study the effect of graphene nanoplatelets (GNPs) on the grain evolution of gradient-structured Al-matrix composites via simulation. The grain growth is studied using an improved and verified Monte Carlo algorithm. The effects of three distribution forms of GNPs are designed and investigated: linear, concave and discontinuous, respectively. The influence of gradient-distributed GNPs on the dynamic process of grain morphology evolution in composites has been investigated numerically for the first time. Gradient grain sizes in the 90–470 nm range are obtained, and the maximum gradient value reaches 1.833 nm/nm. The connections between grain morphologies, grain sizes, and the spatial distributions of GNPs have been studied in detail. The simulated results show that the grain evolution process is significantly different under the three conditions. The grain size variation of composites g with GNPs concentration vG is nonlinear, and they satisfy the predicted relation as g = 305.5e(−vG/2.77) + 90.54 under the parameters studied in this model. The quantitative relationship between grain size gradient and GNPs distribution is also found. This work provides a theoretical method for gradient microstructure design of GNPs reinforced Al-matrix composites. For preparation requirements with specific grain size and gradient values, the reinforcement distribution can be determined quantitatively. Moreover, this model can be extended to grain evolution for a wider range of matrix and nano-reinforcements.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (12102016) and the National Key Research and Development Program of China (2018YFB0703500).

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  1. Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing, 100124, China

    Qi Wu & Lianchun Long

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Wu, Q., Long, L. Numerical study on grain evolution of gradient-structured aluminum matrix composites induced by graphene nanoplatelets. Appl. Phys. A 128, 1116 (2022). https://doi.org/10.1007/s00339-022-06274-6

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