机器学习设计具有合适腐蚀速率的400 MPa级生物可降解Zn–Mn基合金

陈王璋; 苟伟; 李亚庚; 李相民; 李猛; 侯建新; 张晓彤; 石章智; 王鲁宁

doi:10.1007/s12613-024-2995-4

Volume 31 Issue 12

Dec. 2024

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文章导航 > 矿物冶金与材料学报（英文） > 2024 > 31(12): 2727-2736

Wangzhang Chen, Wei Gou, Yageng Li, Xiangmin Li, Meng Li, Jianxin Hou, Xiaotong Zhang, Zhangzhi Shi, and Luning Wang, Machine learning design of 400 MPa grade biodegradable Zn–Mn based alloys with appropriate corrosion rates, Int. J. Miner. Metall. Mater., 31(2024), No. 12, pp. 2727-2736. https://doi.org/10.1007/s12613-024-2995-4

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Wangzhang Chen, Wei Gou, Yageng Li, Xiangmin Li, Meng Li, Jianxin Hou, Xiaotong Zhang, Zhangzhi Shi, and Luning Wang, Machine learning design of 400 MPa grade biodegradable Zn–Mn based alloys with appropriate corrosion rates, Int. J. Miner. Metall. Mater., 31(2024), No. 12, pp. 2727-2736. https://doi.org/10.1007/s12613-024-2995-4

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研究论文

机器学习设计具有合适腐蚀速率的400 MPa级生物可降解Zn–Mn基合金

1)
北京科技大学材料科学与工程学院, 新金属材料国家重点实验室, 北京材料基因工程高精尖创新中心, 北京100083, 中国
2)
辽宁材料实验室材料智能技术研究所, 沈阳110004, 中国
3)
北京科技大学顺德创新学院计算机与通信工程学院, 顺德528399, 中国

* 共同第一作者

通讯作者:
石章智 E-mail: ryansterne@163.com

王鲁宁 E-mail: luning.wang@ustb.edu.cn

文章亮点

(1) 首次通过机器学习对锌合金的抗拉强度和腐蚀速率进行了预测，最终误差在10%以下。

(2) 成功设计了一种代表性的400 MPa级Zn–Mn合金。

(3) 通过实验结果和既有定律对预测模型进行了可解释分析。

中文摘要

生物可降解锌合金常用的试错法成本高且具有盲目性。本研究基于在国家数据管理与服务平台上自建的生物可降解锌合金数据库，通过皮尔逊相关筛选、递归筛选与穷尽筛选方法，首次建立了两种机器学习模型来预测生物可降解锌合金的极限抗拉强度(UTS)和浸泡腐蚀速率(CR)。基于两种预测模型建立了Zn–Mn基合金设计的实时可视化界面；可根据输入特征量实时预测目标量并展示影响规律。最终设计出具有代表性的Zn–0.4Mn–0.4Li–0.05Mg合金，通过拉伸力学性能和浸泡腐蚀速率测试，其UTS达到420 MPa，预测误差仅为0.95%。CR为73 μm/a，预测误差为5.5%。将常用挤压态锌合金的UTS提高了50 MPa等级，并且作为植入器械时具有理想的腐蚀速率。最后，详细讨论了所筛选特征对UTS和CR的影响规律。UTS和CR模型的联合应用为协同调控生物可降解锌合金的综合性能提供了新的策略。

关键词:

Research Article

Machine learning design of 400 MPa grade biodegradable Zn–Mn based alloys with appropriate corrosion rates

+ Author Affiliations

1)
Beijing Advanced Innovation Center for Materials Genome Engineering, State Key Laboratory for Advanced Metals and Materials, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China
2)
Institute of Materials Intelligent Technology, Liaoning Academy of Materials, Shenyang 110004, China
3)
School of Computer and Communication Engineering, Shunde Innovation School, University of Science and Technology Beijing, Shunde 528399, China

Luning Wang is a vice editor-in-chief of this journal and was not involved in the editorial review or the decision to publish this article. All authors declare that they have no financial interests or personal relationships that could have appeared to influence the work reported in this paper.
* These authors contributed equally to this work.

Corresponding authors:
Zhangzhi Shi E-mail: ryansterne@163.com

Luning Wang E-mail: luning.wang@ustb.edu.cn
Received: 15 July 2024; Revised: 16 August 2024; Accepted: 24 August 2024; Available online: 26 August 2024

Abstract

Abstract

The commonly used trial-and-error method of biodegradable Zn alloys is costly and blindness. In this study, based on the self-built database of biodegradable Zn alloys, two machine learning models are established by the first time to predict the ultimate tensile strength (UTS) and immersion corrosion rate (CR) of biodegradable Zn alloys. A real-time visualization interface has been established to design Zn–Mn based alloys; a representative alloy is Zn–0.4Mn–0.4Li–0.05Mg. Through tensile mechanical properties and immersion corrosion rate tests, its UTS reaches 420 MPa, and the prediction error is only 0.95%. CR is 73 μm/a and the prediction error is 5.5%, which elevates 50 MPa grade of UTS and owns appropriate corrosion rate. Finally, influences of the selected features on UTS and CR are discussed in detail. The combined application of UTS and CR model provides a new strategy for synergistically regulating comprehensive properties of biodegradable Zn alloys.
- Zn alloys;
- machine learning;
- alloy design;
- performance prediction

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