[1]曾崎,王少阳,张英波,等.基于机器学习的高强Mg-Zn-Y-Zr合金半固态工艺研究[J].中国材料进展,2026,45(08):030-39.
　Zeng Qi,Wang Shaoyang,Zhang Yingbo,et al.Rsearch on Semi-solid Process of High-strength Mg-Zn-Y-Zr Alloy Based on Machine Learning[J].MATERIALS CHINA,2026,45(08):030-39.
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基于机器学习的高强Mg-Zn-Y-Zr合金半固态工艺研究()

中国材料进展[ISSN:1674-3962/CN:61-1473/TG]

卷:
45
期数:
2026年08
页码:
030-39
栏目:
出版日期:
2026-07-31

文章信息/Info

Title:
Rsearch on Semi-solid Process of High-strength Mg-Zn-Y-Zr Alloy Based on Machine Learning
作者:
曾崎; 王少阳; 张英波; 朱凯; 胡云峰
1航空工业成都飞机工业（集团）有限责任公司，四川 成都 610092 2西南交通大学材料科学与工程学院，四川 成都 610031
Author(s):
Zeng Qi; Wang Shaoyang; Zhang Yingbo;  Zhu Kai; Hu Yunfeng Author
1. Chengdu Aircraft Industry(Group) Co. Ltd., Chengdu 610092, China 2. School of Materials Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, Chin
关键词:
Mg-Zn-Y-Zr合金; 机器学习; 半固态工艺; 准晶; 热挤压
Keywords:
Mg-Zn-Y-Zr alloy;  machine learning;  semi-solid isothermal treatment; quasicrystal; hot extrusion
文献标志码:
A
摘要:
本文采用一种基于Kriging模型的改进的高效全局优化(Efficient Global Optimization, EGO)算法, 并将其应用于含准晶的Mg-Zn-Y-Zr合金的半固态工艺参数优化。首先铸造和一次热挤压设计与制备了含准晶相的Mg-1.2Zn-0.2Y-0.15Zr合金；然后建立合金半固态工艺参数与力学性能之间的样本数据集和Kriging代理模型；最后结合机器学习和半固态+热挤压复合加工技术实现了合金综合力学性能的显著优化。总共通过四次迭代优化，获得了最大抗拉强度为427.4±2.4 MPa，其半固态工艺参数为574 ℃+60min。与其一次挤压态（285±1.5 MPa）相比，其抗拉强度增加50%。证明了机器学习方法适用于优化半固态工艺参数并提升Mg-Zn-Y-Zr合金强度的可行性和高效性。
Abstract:
In this paper, an improved Efficient Global Optimization (EGO) algorithm based on the Kriging model is adopted and applied to optimize the semi-solid isothermal treatment parameters of Mg-Zn-Y-Zr alloys containing quasicrystals. Firstly, the Mg-1.2Zn-0.2Y-0.15Zr alloy containing quasicrystalline phases was designed, cast and subjected to primary hot extrusion; then the sample data set and Kriging surrogate model between the semi-solid isothermal treatment parameters of the alloy and its mechanical properties were established; finally, by combining machine learning and the semi-solid isothermal treatment + hot extrusion composite processing technology, a significant optimization of the comprehensive mechanical properties of the alloy was achieved. Through a total of four iterative optimizations, the maximum tensile strength of 427.4 ± 2.4 MPa was obtained, and its semi-solid process parameters were 574 ℃ + 60 min. Compared with its primary extrusion state (285 ± 1.5 MPa), its tensile strength increased by 50%. It is proved that the machine learning method is feasible and efficient for optimizing the semi-solid isothermal treatment parameters and enhancing the strength of Mg-Zn-Y-Zr alloys.
更新日期/Last Update: 2026-06-30