Understanding solidification of near eutectic alloy using Cellular Automata (CA)

Roy, Indranil; Rolchigo, Matt; Coleman, John; Chen, Shuanglin; Plotkowski, Alex; Yang, Ying

doi:10.1016/j.commatsci.2024.112835

Title: Understanding solidification of near eutectic alloy using Cellular Automata (CA)

Journal Article · 08 February 2024 · Computational Materials Science

DOI: https://doi.org/10.1016/j.commatsci.2024.112835 · OSTI ID:2317774

^[1];

^[1]; Coleman, John ^[1]; Chen, Shuanglin ^[2]; Plotkowski, Alex ^[1]; Yang, Ying ^[1]

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Computherm LLC, Madison, WI (United States)

The solidification microstructure of an alloy strongly influences the mechanical properties. Dendritic and eutectic solidification are two important pathways of alloy solidification. The formation of phases, and their size and distribution depend on which pathway to take. In contrast to the Scheil model that has been widely used in describing the solidification pathways of alloys manufactured from conventional casting approach, more sophisticated solidification models are required to describe the state-of-the art manufacturing processes, such as additive manufacturing where distinctly different phase morphology, size and distribution than conventional casting can form, due to the vast difference in cooling conditions. Therefore, this work is to develop a computational Cellular Automata framework which includes the modeling of nucleation and growth of dendritic and eutectic solidification, as well as their competition as a function of alloy composition, undercooling and cooling rate. Further, after individual solidification models were validated against analytical solutions, the models were then combined to predict the competition between dendritic and eutectic solidification in alloys with off-eutectic compositions. This work predicts increased cooling rates suppress the dendritic solidification and promote eutectic solidification. Although quantitative validation is needed, this finding is consistent with qualitative observation in literature.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2317774

Journal Information:: Computational Materials Science, Vol. 236, Issue 1; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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