机器学习辅助具有特定相变温度的Cu基SMA高效设计

吴梦玮; 雍维; 付存琴; 马春梅; 刘瑞平

doi:10.1007/s12613-023-2767-6

Volume 31 Issue 4

Apr. 2024

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文章导航 > 矿物冶金与材料学报（英文） > 2024 > 31(4): 773-785

Mengwei Wu, Wei Yong, Cunqin Fu, Chunmei Ma, and Ruiping Liu, Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature, Int. J. Miner. Metall. Mater., 31(2024), No. 4, pp. 773-785. https://doi.org/10.1007/s12613-023-2767-6

Cite this article as:

Mengwei Wu, Wei Yong, Cunqin Fu, Chunmei Ma, and Ruiping Liu, Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature, Int. J. Miner. Metall. Mater., 31(2024), No. 4, pp. 773-785. https://doi.org/10.1007/s12613-023-2767-6

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研究论文

机器学习辅助具有特定相变温度的Cu基SMA高效设计

1)
中国矿业大学(北京)材料科学与工程系，北京 100083，中国
2)
北京科技大学先进材料与技术研究所，北京 100083，中国

* 共同第一作者

通讯作者:
马春梅 E-mail: haomerry@ustb.edu.cn

刘瑞平 E-mail: lrp@cumtb.edu.cn

文章亮点

(1) 成功地预测了Cu基形状记忆合金的相变温度

(2) 系统对比了成分建模和元素特征建模方法的优缺点

(3) 分析了元素关键特征对Cu基形状记忆合金相变温度的影响机理

(4) 实现了具有特定相变温度的Cu基形状记忆合金的高效设计

中文摘要

马氏体相变温度是形状记忆合金的应用基础，快速准确预测形状记忆合金转变温度具有非常重要的实际意义。本文利用机器学习方法以加速搜索具有特定目标特性（相变温度）的形状记忆合金。采用直接建模和特征建模方法对形状记忆合金相变温度进行预测。从大量未探索的数据中选择一组数据，采用反向设计方法设计了形状记忆合金。获取了该形状记忆合金的实验结果，验证了支持向量回归模型的有效性。结果显示机器学习模型可以更高效、更有针对性地获得目标材料，实现了特定目标相变温度形状记忆合金的准确快速设计。在此基础上，分析了相变温度与材料描述符之间的关系，证明了影响形状记忆合金相变温度的关键因素是基于原子间结合能的强度。本工作为Cu基形状记忆合金的可控设计和性能优化提供新的思路。

关键词:

Research Article

Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature

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Abstract

Abstract

The martensitic transformation temperature is the basis for the application of shape memory alloys (SMAs), and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance. In this work, machine learning (ML) methods were utilized to accelerate the search for shape memory alloys with targeted properties (phase transition temperature). A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data. Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys. The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression (SVR) model. The results show that the machine learning model can obtain target materials more efficiently and pertinently, and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature. On this basis, the relationship between phase transition temperature and material descriptors is analyzed, and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms. This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.
- machine learning;
- support vector regression;
- shape memory alloys;
- martensitic transformation temperature

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