Understanding microstructure-property relationships of HPDC Al-Si alloy based on machine learning and crystal plasticity simulation  

作　　者：Qiang-Qiang Zhai Zhao Liu Ping Zhu 

机构地区：[1]State Key Laboratory of Mechanical System and Vibration,Shanghai Jiao Tong University,Shanghai,200240,People’s Republic of China [2]National Engineering Research Center of Automotive Power and Intelligent Control,Shanghai Jiao Tong University,Shanghai,200240,People’s Republic of China [3]School of Design,Shanghai Jiao Tong University,Shanghai,200240,People’s Republic of China

出　　处：《Advances in Manufacturing》2024年第3期497-511,共15页先进制造进展（英文版）

基　　金：support from the National Natural Science Foundation of China(Grant No.52375256);the Natural Science Foundation of Shanghai(Grant Nos.21ZR1431500,23ZR1431600).

摘　　要：Al-Si alloys manufactured via high-pressure die casting(HPDC)are suitable for a wide range of applications.However,the heterogeneous microstructure and unpredictable pore distribution of Al-Si high-pressure die castings result in significant variations in the mechanical properties,thus leading to a complicated microstructure-property relationship that is difficult to capture.Hence,a computational framework incorporating machine learning and crystal plasticity method is proposed.This framework aims to provide a systematic and comprehensive understanding of this relationship and enable the rapid prediction of macroscopic mechanical properties based on the microstructure.Firstly,we select eight variables that can effectively characterize the microstructural features and then obtain their statistical information.Subsequently,based on 160 samples obtained via the Latin hypercube sampling method,representative volume elements are constructed,and the crystal plasticity fast Fourier transformation method is executed to obtain the macroscopic mechanical properties.Next,the yield strength,elastic modulus,strength coefficient,and strain-hardening exponent are used to characterize the stress-strain curve,and Gaussian process regression models and microstructural variables are developed.Finally,sensitivity and univariate analyses based on these machine-learning models are performed to obtain insights into the microstructure-property relationships of the HPDC Al-Si alloy.The results show that the Gaussian process regression models exhibit high accuracy(R^(2) greater than 0.84),thus confirming the viability of the proposed method.The results of sensitivity analysis indicate that the pore size exerts the most significant effect on the mechanical properties.Furthermore,the proposed framework can not only be transferred to other alloys but also be employed for material design.

关 键 词：High-pressure die casting(HPDC) Machine learning Crystal plasticity Aluminum alloys 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]