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Simulating the Effect of Temperature Gradient on Grain Growth of 6061-T6 Aluminum Alloy via Monte Carlo Potts Algorithm

Qi Wu*, Jianan Li, Lianchun Long, Linao Liu

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

* Corresponding Author: Qi Wu. Email: email

Computer Modeling in Engineering & Sciences 2021, 129(1), 99-116. https://doi.org/10.32604/cmes.2021.015669

Abstract

During heat treatment or mechanical processing, most polycrystalline materials experience grain growth, which significantly affects their mechanical properties. Microstructure simulation on a mesoscopic scale is an important way of studying grain growth. A key research focus of this type of method has long been how to efficiently and accurately simulate the grain growth caused by a non-uniform temperature field with temperature gradients. In this work, we propose an improved 3D Monte Carlo Potts (MCP) method to quantitatively study the relationship between non-uniform temperature fields and final grain morphologies. Properties of the aluminum alloy AA6061-T6 are used to establish a trial calculation model and to verify the algorithms with existing experimental results in literature. The detailed grain growth process of the 6061-T6 aluminum alloy under different temperature fields is then obtained, and grain morphologies at various positions are analyzed. Results indicate that while absolute temperature and duration time are the primary factors determining the final grain size, the temperature gradient also has strong influence on the grain morphologies. The relationships between temperatures, temperature gradients and grain growth process have been established. The proposed MCP algorithm can be applied to different types of materials when the proper parameters are used.

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Wu, Q., Li, J., Long, L., Liu, L. (2021). Simulating the Effect of Temperature Gradient on Grain Growth of 6061-T6 Aluminum Alloy via Monte Carlo Potts Algorithm. CMES-Computer Modeling in Engineering & Sciences, 129(1), 99–116. https://doi.org/10.32604/cmes.2021.015669

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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