Viscosity Calculation for Al–Si–Mg–Fe System through CALPHAD Method

Author:

Fu Yu1,Luo Qun1ORCID,Liu Bin1,Li Qian1234ORCID

Affiliation:

1. State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai Key Laboratory of Advanced Ferrometallurgy Shanghai University Shanghai 200444 China

2. National Engineering Research Center for Magnesium Alloys Chongqing University Chongqing 400044 China

3. College of Materials Science and Engineering Chongqing University Chongqing 400044 China

4. National Key Laboratory of Advanced Casting Technologies Chongqing University Chongqing 400044 China

Abstract

Viscosity is a crucial parameter affecting the fluidity of metal melts, which directly influences the founding properties of Al alloys. However, obtaining viscosity measurement data is difficult for metal melts, and the viscosity prediction for ternary and multicomponent alloys would provide the significant data for the selection of process parameters. This study compares the applicability of the Hirai model, SDS model, and R‐K function for viscosity calculations in the sub‐binary systems of Al–Si–Mg–Fe alloys. R‐K function shows the best agreement with experimental data. However, the SDS model shows lower relative error than Hirai model, which would play an important role in those systems lacking experimental data to predict the viscosities. Ultimately, a database capable of predicting viscosity values for the entire composition and temperature range of the Al–Si–Mg–Fe system is established using the CALPHAD method. Viscosity parameters for Al–Si, Al–Mg, Al–Fe, Mg–Si, and Mg–Fe are evaluated and optimized through the R‐K derivation, corroborated with existing experimental data. Using the R‐K function, successful extrapolation and prediction of viscosity for the Al–Si–Mg–Fe ternary and quaternary systems are achieved, with a mean square error between predicted and experimental values of only 0.7%, demonstrating the successful application of the Al–Si–Mg–Fe database for viscosity prediction.

Funder

Key Technologies Research and Development Program

National Natural Science Foundation of China

Shanghai Rising-Star Program

Fundamental Research Funds for the Central Universities

Publisher

Wiley

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