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

Turn off MathJax

图(8) / 表(5)

数据统计

计量

文章访问数: 324
HTML全文浏览量: 159
PDF下载量: 43
被引次数: 0

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

Cite this article as:

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

Citation:

PDF( 1728 KB)

引用本文 PDF XML SpringerLink

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

FullText(HTML)

Supplements(0)

References(51)

References

[1]	J. Sun, X. Zhang, Z.Z. Shi, et al., Development of a high-strength Zn–Mn–Mg alloy for ligament reconstruction fixation, Acta Biomater., 119(2021), p. 485. doi: 10.1016/j.actbio.2020.10.032
[2]	B. Jia, H.T. Yang, Y. Han, et al. , In vitro and in vivo studies of Zn–Mn biodegradable metals designed for orthopedic applications, Acta Biomater., 108(2020), p. 358. doi: 10.1016/j.actbio.2020.03.009
[3]	Z.Z. Shi, X.M. Li, S.L. Yao, et al., 300 MPa grade biodegradable high-strength ductile low-alloy (BHSDLA) Zn–Mn–Mg alloys: An in vitro study, J. Mater. Sci. Technol., 138(2023), p. 233. doi: 10.1016/j.jmst.2022.08.015
[4]	J. Venezuela and M.S. Dargusch, The influence of alloying and fabrication techniques on the mechanical properties, biodegradability and biocompatibility of zinc: A comprehensive review, Acta Biomater., 87(2019), p. 1. doi: 10.1016/j.actbio.2019.01.035
[5]	T. Wang, Z.Z. Shi, H.Y. Zhong, et al. , In vitro performance of a biodegradable zinc alloy adjustable-loop cortical suspension fixation for anterior cruciate ligament reconstruction, Int. J. Miner. Metall. Mater., 31(2024), No. 5, p. 887. doi: 10.1007/s12613-024-2889-5
[6]	C. Zhou, H.F. Li, Y.X. Yin, et al., Long-term in vivo study of biodegradable Zn–Cu stent: A 2-year implantation evaluation in porcine coronary artery, Acta Biomater., 97(2019), p. 657. doi: 10.1016/j.actbio.2019.08.012
[7]	M. Saini, Y. Singh, P. Arora, V. Arora, and K. Jain, Implant biomaterials: A comprehensive review, World J. Clin. Cases, 3(2015), No. 1, p. 52. doi: 10.12998/wjcc.v3.i1.52
[8]	Z.Z. Shi, J. Yu, X.F. Liu, et al., Effects of Ag, Cu or Ca addition on microstructure and comprehensive properties of biodegradable Zn–0.8Mn alloy, Mater. Sci. Eng. C, 99(2019), p. 969. doi: 10.1016/j.msec.2019.02.044
[9]	Z.Z. Shi, J. Yu, and X.F. Liu, Microalloyed Zn–Mn alloys: From extremely brittle to extraordinarily ductile at room temperature, Mater. Des., 144(2018), p. 343. doi: 10.1016/j.matdes.2018.02.049
[10]	J.G. Kuang and Z.L. Long, Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms, Int. J. Miner. Metall. Mater., 31(2024), No. 2, p. 337. doi: 10.1007/s12613-023-2679-5
[11]	B.F. Shi, T. Lookman, and D.Z. Xue, Multi-objective optimization and its application in materials science, Mater. Genome Eng. Adv., 1(2023), No. 2, art. No. e14. doi: 10.1002/mgea.14
[12]	H.D. Fu, H.T. Zhang, C.S. Wang, W. Yong, and J.X. Xie, Recent progress in the machine learning-assisted rational design of alloys, Int. J. Miner. Metall. Mater., 29(2022), No. 4, p. 635. doi: 10.1007/s12613-022-2458-8
[13]	Y. Shang, Z.Y. Xiong, K. An, J.A. Hauch, C.J. Brabec, and N. Li, Materials genome engineering accelerates the research and development of organic and perovskite photovoltaics, Mater. Genome Eng. Adv., 2(2024), No. 1, art. No. e28. doi: 10.1002/mgea.28
[14]	J.Q. Li, H.T. Zhang, J.T. Sun, H.D. Fu, and J.X. Xie, Design of low-alloying and high-performance solid solution-strengthened copper alloys with element substitution for sustainable development, Int. J. Miner. Metall. Mater., 31(2024), No. 5, p. 826. doi: 10.1007/s12613-024-2870-3
[15]	H.T. Zhang, H.D. Fu, X.Q. He, et al., Dramatically enhanced combination of ultimate tensile strength and electric conductivity of alloys via machine learning screening, Acta Mater., 200(2020), p. 803. doi: 10.1016/j.actamat.2020.09.068
[16]	X.M. Feng, Z.L. Wang, L. Jiang, F. Zhao, and Z.H. Zhang, Simultaneous enhancement in mechanical and corrosion properties of Al–Mg–Si alloys using machine learning, J. Mater. Sci. Technol., 167(2023), p. 1. doi: 10.1016/j.jmst.2023.04.072
[17]	Y.F. Juan, G.S. Niu, Y. Yang, et al., Accelerated design of Al−Zn−Mg−Cu alloys via machine learning, Trans. Nonferrous Met. Soc. China, 34(2024), No. 3, p. 709. doi: 10.1016/S1003-6326(23)66429-5
[18]	H.Y. Li, X.W. Li, Y.N. Li, et al., Machine learning assisted design of aluminum-lithium alloy with high specific modulus and specific strength, Mater. Des., 225(2023), art. No. 111483. doi: 10.1016/j.matdes.2022.111483
[19]	H.Y. Gong, J. He, X.T. Zhang, et al., Wan, A repository for the publication and sharing of heterogeneous materials data, Sci. Data, 9(2022), No. 1, art. No. 787. doi: 10.1038/s41597-022-01897-z
[20]	S.L. Liu, Y.J. Su, H.Q. Yin, et al., An infrastructure with user-centered presentation data model for integrated management of materials data and services, NPJ Comput. Mater., 7(2021), No. 1, art. No. 88. doi: 10.1038/s41524-021-00557-x
[21]	H.T. Zhang, H.D. Fu, S.C. Zhu, W. Yong, and J.X. Xie, Machine learning assisted composition effective design for precipitation strengthened copper alloys, Acta Mater., 215(2021), art. No. 117118. doi: 10.1016/j.actamat.2021.117118
[22]	Z.Z. Shi, J. Yu, X.F. Liu, and L.N. Wang, Fabrication and characterization of novel biodegradable Zn–Mn–Cu alloys, J. Mater. Sci. Technol., 34(2018), No. 6, p. 1008. doi: 10.1016/j.jmst.2017.11.026
[23]	Z. Li, Z.Z. Shi, Y. Yan, et al., Suppression mechanism of initial pitting corrosion of pure Zn by Li alloying, Corros. Sci., 189(2021), art. No. 109564. doi: 10.1016/j.corsci.2021.109564
[24]	H.T. Yang, B. Jia, Z.C. Zhang, et al., Alloying design of biodegradable zinc as promising bone implants for load-bearing applications, Nat. Commun., 11(2020), No. 1, art. No. 401. doi: 10.1038/s41467-019-14153-7
[25]	S.N. Sun, Y.P. Ren, L.Q. Wang, B. Yang, H.X. Li, and G.W. Qin, Abnormal effect of Mn addition on the mechanical properties of as-extruded Zn alloys, Mater. Sci. Eng. A, 701(2017), p. 129. doi: 10.1016/j.msea.2017.06.037
[26]	P.S. Guo, F.X. Li, L.J. Yang, et al., Ultra-fine-grained Zn–0.5Mn alloy processed by multi-pass hot extrusion: Grain refinement mechanism and room-temperature superplasticity, Mater. Sci. Eng. A, 748(2019), p. 262. doi: 10.1016/j.msea.2019.01.089
[27]	Z.Z. Shi, H.Y. Li, J.Y. Xu, X.X. Gao, and X.F. Liu, Microstructure evolution of a high-strength low-alloy Zn–Mn–Ca alloy through casting, hot extrusion and warm caliber rolling, Mater. Sci. Eng. A, 771(2020), art. No. 138626. doi: 10.1016/j.msea.2019.138626
[28]	S.Y. Liu, D. Kent, N. Doan, M. Dargusch, and G. Wang, Effects of deformation twinning on the mechanical properties of biodegradable Zn–Mg alloys, Bioact. Mater., 4(2019), p. 8. doi: 10.1016/j.bioactmat.2018.11.001
[29]	W. Bednarczyk, M. Wątroba, J. Kawałko, and P. Bała, Can zinc alloys be strengthened by grain refinement? A critical evaluation of the processing of low-alloyed binary zinc alloys using ECAP, Mater. Sci. Eng. A, 748(2019), p. 357. doi: 10.1016/j.msea.2019.01.117
[30]	L.F. Ye, H. Liu, C. Sun, et al., Achieving high strength, excellent ductility, and suitable biodegradability in a Zn-0.1Mg alloy using room-temperature ECAP, J. Alloys Compd., 926(2022), art. No. 166906. doi: 10.1016/j.jallcom.2022.166906
[31]	M. Wątroba, W. Bednarczyk, J. Kawałko, and P. Bała, Effect of zirconium microaddition on the microstructure and mechanical properties of Zn–Zr alloys, Mater. Charact., 142(2018), p. 187. doi: 10.1016/j.matchar.2018.05.055
[32]	L.Q. Wang, Y.P. Ren, S.N. Sun, H. Zhao, S. Li, and G.W. Qin, Microstructure, mechanical properties and fracture behavior of as-extruded Zn–Mg binary alloys, Acta Metall. Sin. Engl. Lett., 30(2017), No. 10, p. 931. doi: 10.1007/s40195-017-0585-4
[33]	L.Q. Wang, Y.F. He, H. Zhao, et al., Effect of cumulative strain on the microstructural and mechanical properties of Zn–0.02wt%Mg alloy wires during room-temperature drawing process, J. Alloys Compd., 740(2018), p. 949. doi: 10.1016/j.jallcom.2018.01.059
[34]	Z.Z. Shi, X.X. Gao, H.J. Zhang, et al., Design biodegradable Zn alloys: Second phases and their significant influences on alloy properties, Bioact. Mater., 5(2020), No. 2, p. 210. doi: 10.1016/j.bioactmat.2020.02.010
[35]	Y.P. Diao, L.C. Yan, and K.W. Gao, A strategy assisted machine learning to process multi-objective optimization for improving mechanical properties of carbon steels, J. Mater. Sci. Technol., 109(2022), p. 86. doi: 10.1016/j.jmst.2021.09.004
[36]	B. Xu, H.Q. Yin, X. Jiang, et al., Data-driven design of Ni-based turbine disc superalloys to improve yield strength, J. Mater. Sci. Technol., 155(2023), p. 175. doi: 10.1016/j.jmst.2023.01.032
[37]	M.Y. Jiang, G. Monnet, and B. Devincre, On the origin of the Hall–Petch law: A 3D-dislocation dynamics simulation investigation, Acta Mater., 209(2021), art. No. 116783. doi: 10.1016/j.actamat.2021.116783
[38]	M.A. Meyers and K.K. Chawla, Mechanical Behavior of Materials, Cambridge University Press, Cambridge, 2008.
[39]	J.B. Yang, Y. He, Y.L. Ma, et al., Theoretical model of the temperature-dependent ultimate tensile strength from the viewpoint of dislocation kinetics approach for FCC metals, Eur. J. Mech. A/solids, 103(2024), art. No. 105160. doi: 10.1016/j.euromechsol.2023.105160
[40]	U.F. Kocks and H. Mecking, Physics and phenomenology of strain hardening: The FCC case, Prog. Mater. Sci., 48(2003), No. 3, p. 171. doi: 10.1016/S0079-6425(02)00003-8
[41]	W.G. Li, H.B. Kou, X.Y. Zhang, et al., Temperature-dependent elastic modulus model for metallic bulk materials, Mech. Mater., 139(2019), art. No. 103194. doi: 10.1016/j.mechmat.2019.103194
[42]	X.M. Xiao, B. Wang, E.Y. Liu, et al., Investigation of zinc-silver alloys as biodegradable metals for orthopedic applications, J. Mater. Res. Technol., 26(2023), p. 6287. doi: 10.1016/j.jmrt.2023.09.025
[43]	K. Wang, X. Tong, J.X. Lin, et al., Binary Zn–Ti alloys for orthopedic applications: Corrosion and degradation behaviors, friction and wear performance, and cytotoxicity, J. Mater. Sci. Technol., 74(2021), p. 216. doi: 10.1016/j.jmst.2020.10.031
[44]	L. Liu, T.T. Cao, Q.W. Zhang, and C.W. Cui, Organic phosphorus compounds as inhibitors of corrosion of carbon steel in circulating cooling water: Weight loss method and thermodynamic and quantum chemical studies, Adv. Mater. Sci. Eng., 2018(2018), No. 1, art. No. 1653484. doi: 10.1155/2018/1653484
[45]	J.P. Broomfield, An overview of cathodic protection criteria for steel in atmospherically exposed concrete, Corros. Eng. Sci. Technol., 55(2020), No. 4, p. 303. doi: 10.1080/1478422X.2020.1735715
[46]	R.W. Evitts and G.F. Kennell, Handbook of Environmental Degradation of Materials, 3rd ed., William Andrew Press, New York, 2018, p. 97.
[47]	M. Cao, Z. Xue, Z.Y. Lv, J.L. Sun, Z.Z. Shi, and L.N. Wang, 300 MPa grade highly ductile biodegradable Zn–2Cu–(0.2–0.8)Li alloys with novel ternary phases, J. Mater. Sci. Technol., 157(2023), p. 234. doi: 10.1016/j.jmst.2023.01.048
[48]	X.L. Zhu, P.S. Guo, L.J. Yang, et al., Comparison of the in vitro corrosion behavior of biodegradable pure Zn in SBF, 0.9% NaCl, and DMEM, Mater. Corros., 72(2021), No. 10, p. 1687. doi: 10.1002/maco.202112485
[49]	Y. Kawamura, N. Osaki, T. Kiguchi, A. Vinogradov, and S. Inoue, Advanced wrought Mg–4.5Al–2.5Ca–0.02Mn (at%) alloys with exceptional balance of high thermal conductivity, yield strength, ductility, nonflammability, and corrosion resistance, J. Alloys Compd., 978(2024), art. No. 173299. doi: 10.1016/j.jallcom.2023.173299
[50]	H.Z. Wu, X.X. Xie, J. Wang, G.Z. Ke, H. Huang, Y. Liao, and Q.Q. Kong, Biological properties of Zn–0.04Mg–2Ag: A new degradable zinc alloy scaffold for repairing large-scale bone defects, J. Mater. Res. Technol., 13(2021), p. 1779. doi: 10.1016/j.jmrt.2021.05.096
[51]	K.N. Niu, D.C. Zhang, F.G. Qi, J.G. Lin, and Y.L. Dai, The effects of Cu and Mn on the microstructure, mechanical, corrosion properties and biocompatibility of Zn–4Ag alloy, J. Mater. Res. Technol., 21(2022), p. 4969. doi: 10.1016/j.jmrt.2022.11.083