![]() However, PF simulations require more computation resources, which limits its industrial applications where large computation domains are needed 23.Ĭompared to the PF method, CA provides strong morphological resolutions but with a reasonable demand for computation resources 24, 25. 22 studied eutectic solidification using an atomistic simulation method. 21 established two different PF models to study the morphological evolution of binary eutectics. 20 performed PF simulations during directional solidification of a binary alloy and conducted a parallel computation to increase the computational speed. ![]() Numerical models have been developed for predicting solidification dendrites using different techniques such as phase-field (PF) 17, 18 and cellular automaton (CA) 19. Numerical simulation is an alternative method to study the dynamic process of solidification with the recent advancements in materials science and computer technology 16. Accurate prediction of the evolution of dendrite and eutectic microstructure is essential to improving the properties and performance of solidification products. However, it is still difficult to understand the dynamic evolution of dendrite and eutectic formation during solidification processing 15. 14 identified the eutectic transformation by X-ray absorption contrast difference. Khajeh and Maijer 13 applied X-ray microtomography to obtain three-dimensional geometries of eutectic/primary phases. The solidification microstructure of aluminum alloys has been extensively investigated using 2-D optical metallography 6, 10 and 3-D X-ray tomography 11, 12. It is, thus, important to understand the mechanisms of both dendrite and eutectic phase formation during solidification processing, in order to accurately simulate and optimize the solidification microstructure for ICME (integrated computational materials engineering) based design and manufacturing of solidification products 9. ![]() Additionally, the formation of eutectics which occurs at the final stage of solidification is critical to the castability and the final properties of the alloys 8. However, eutectic (α-Al + Si) solidification microstructure in interdendritic regions of Al-Si products can significantly affect their mechanical properties 6, 7. Therefore, extensive experimental and simulation studies 4, 5 have been reported on the solidification of dendrite microstructure including dendrite morphology, dendrite arm spacing, solute concentration distribution, etc. It is well known that the α-Al grain/dendrite microstructure of Al-Si alloys influences the mechanical and corrosion properties of solidification products including castings, welds, and additively manufactured components 3. Similar content being viewed by othersĪl-Si-based cast alloys have been widely used in many industrial applications due to their lightweight, excellent castability, comprehensive mechanical properties, and corrosion resistance 1, 2. This 3-D CA model is useful for predicting and optimizing the solidification microstructure including eutectic transformation during solidification processing such as casting, potentially welding, and additive manufacturing. The simulation results show a good agreement with the experimental observations and calculations by the Scheil model and lever rule. The quantitative results of the Si phase in the eutectic microstructure were experimentally validated using scanning electron microscopy and deep etching techniques. In this study, our previous three-dimensional (3-D) cellular automaton (CA) model for α-Al dendritic growth was extended to include eutectic (α-Al + Si) transformation in multi-dendrite domains, providing a complete solidification simulation of critically important Al-Si based alloys. Simulating eutectic Si phase formation has been a challenge in ICME (integrated computational materials engineering) based design and manufacturing of solidification products of Al-Si-based alloys. The morphology of eutectic silicon in solidification microstructure is critical to the performance of Al-Si-based alloys.
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