Npj Comput. Mater.: 新合金设计—样本数据迁移机器学习方
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Al-Zn-Mg-Cu系(7xxx系)合金具有比强度高、耐蚀性好、成本低等优点,被广泛应用于航空航天、现代交通等领域。随着飞机、高速铁路列车等运输工具不断向高速化、轻量化方向发展,对Al-Zn-Mg-Cu合金的综合性能提出了更高的要求,导致合金中元素种类和含量越来越多,合金成分优化面临计算设计困难、实验试错优化工作量巨大等问题。经典机器学习方法可加速材料设计,但常常面临数据缺乏导致的模型泛化能力差、精度不足的问题。迁移学习是机器学习的前沿技术之一,它应用相关领域的知识(数据)可解决所研究的对象由于数据稀缺,采用经典机器学习方法难以建立泛化能力强、精度高的预测模型的问题。
来自北京科技大学北京材料基因工程高精尖创新中心的谢建新院士团队,采用样本数据迁移机器学习方法,在极少量数据的情况下,开发了适用于新型超强高韧铝合金E2的时效工艺制度(T66R工艺),实现了E2合金强塑性的同步提升。与采用经典的T6处理工艺相比,T66R工艺可以同时提高E2合金试样的抗拉强度和断后伸长率,分别从715±6 MPa和8.4±0.4%提升到767±6 MPa和13.4±0.5%。组织表征结果表明,T66R优化工艺处理后的E2合金试样中微米级未溶相降低了一个数量级,且析出相更加细小弥散、更接近球形,数密度提升了50%以上。细小的球形析出相使得合金中位错的主要运动方式,由Orowan绕过机制转变为切过机制,合金的强度和塑性同步提升。研究表明,迁移现有合金数据与知识用于设计新合金是一种有效的方法,所开发合金对飞机、高速铁路列车等高端装备的轻量化具有重要意义。
A rapid and effective method for alloy materials design via sample data transfer machine learning
Jiang Lei, Zhang Zhihao, Hu Hao, He Xingqun, Fu Huadong & Xie Jianxin
One of the challenges in material design is to rapidly develop new materials or improve the performance of materials by utilizing the data and knowledge of existing materials. Here, a rapid and effective method of alloy material design via data transfer learning is proposed to efficiently design new alloys using existing data. A new type of aluminum alloy (E2 alloy) with ultra strength and high toughness previously developed by the authors is used as an example. An optimal three-stage solution-aging treatment process (T66R) was efficiently designed transferring 1053 pieces of process-property relationship data of existing AA7xxx commercial aluminum alloys. It realizes the substantial improvement of strength and plasticity of E2 alloy simultaneously, which is of great significance for lightweight of high-end equipment. Meanwhile, the microstructure analysis clarifies the mechanism of alloy performance improvement. This study shows that transferring the existing alloy data is an effective method to design new alloys.
Fig. 3 Material discovery through C1, R2, and R7 models established by AlphaMat.
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