摘要
A356 alloys are widely used in industries due to their excellent comprehensive performance.Sr is usually added in A356 alloys to improve their mechanical properties.There have been various experimental reports on the optimal additional amount of Sr in A356 alloys,but their results are inevitably inconsistent.In this paper,a combination of computational thermodynamic and machine learning approaches was employed to determine the optimal Sr content in A356 alloys with the best mechanical properties.First,a self-consistent thermodynamic database of quaternary Al-Si-Mg-Sr system was established by means of the Calculation of PHAse Diagram technique supported by key experiments.Second,the fractions for solidified phase/structures of A356-xSr alloys predicted by Scheil simulation,together with the measured mechanical properties were set as the input dataset in the machine learning model to train the relation of“composition-microstructure-properties”.The optimal addition of Sr in A356 alloy was designed as 0.005 wt.%and validated by key experiments.Furthermore,such a combinatorial approach can help to understand the strengthening/toughening mechanisms of Sr-modified A356 alloys.It is also anticipated that the present approach may provide a feasible means for efficient and accurate design of various casting alloys and understanding the alloy strengthening/toughening mechanisms.
基金
supported by the National Key Research and Development Program of China(Grant No.2019YFB2006500)
the Youth Talent Project of Innovation-driven Plan at Central South University(Grant No.2282019SYLB026)
the financial support from the Fundamental Research Funds for the Central Universities of Central South University(Grant No.2021zzts0094)
the financial support from the Natural Science Foundation of China(Grant No.52061007)
the Guangxi Natural Science Foundation(Grant No.2019GXNSFAA245003)。
