Machine Learning Aided Rapid Discovery of High Perfor-mance Silver Alloy Electrical Contact Materials

doi:10.11900/0412.1961.2021.00002

Acta Metall Sin

2022, Vol. 58

Issue (6): 816-826 DOI: 10.11900/0412.1961.2021.00002

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Machine Learning Aided Rapid Discovery of High Perfor-mance Silver Alloy Electrical Contact Materials

HE Xingqun¹^,²^,³, FU Huadong¹^,²^,³(

), ZHANG Hongtao¹^,²^,³, FANG Jiheng⁴, XIE Ming⁴, XIE Jianxin¹^,²^,³(

)

^1.Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
^2.Beijing Laboratory of Metallic Materials and Processing for Modern Transportation, University of Science and Technology Beijing, Beijing 100083, China
^3.Key Laboratory for Advanced Materials Processing (Ministry of Education), University of Science and Technology Beijing, Beijing 100083, China
^4.State Key Laboratory of Advanced Technologies for Comprehensive Utilization of Platinum Metals, Kunming Institute of Precious Metals, Kunming 650106, China

Cite this article:

HE Xingqun, FU Huadong, ZHANG Hongtao, FANG Jiheng, XIE Ming, XIE Jianxin. Machine Learning Aided Rapid Discovery of High Perfor-mance Silver Alloy Electrical Contact Materials. Acta Metall Sin, 2022, 58(6): 816-826.

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Abstract

Thirty-two groups of data of composition and performance of silver alloy electrical contact materials prepared via casting were collected from the literature to quickly find high-performance silver alloy electrical contact materials. The key alloy factors affecting the alloy properties were identified using the feature selection method. The prediction model of alloy electrical conductivity and hardness was established using a support vector machine (SVM) algorithm, which achieved the rapid design of alloy composition. Three composition designs of Ag-19.53Cu-1.36Ni, Ag-10.20Cu-0.20Ni-0.05Ce, and Ag-11.43Cu-0.66Ni-0.05Ce (mass fraction, %) with excellent predictive performance were selected for experimental validation under industrial production conditions. The error between the performance prediction and experimental results is less than 10%, the electrical conductivity of the three alloys designed is greater than 79%IACS, and the Vickers hardness is greater than 87 HV. Both the electrical conductivity and hardness are better than those of previous silver alloy electrical contact materials prepared via casting. The above results show that the machine learning composition design method established in this study has good reliability, helps improve the efficiency of alloy composition design, and quickly finds silver alloy electrical contact materials with excellent comprehensive properties.

Key words: machine learning silver alloy electrical contact material composition design

Received: 15 January 2021

ZTFLH:

TG146.3

Fund: National Natural Science Foundation of China(U1602271);National Natural Science Foundation of China(51974028);Project of Beijing Municipal Science & Technology Commission(Z191100001119125);Fundamental Research Funds for the Central Universities(FRF-IDRY-19-019)

About author: FU Huadong, professor, Tel: (010)62333999, E-mail: hdfu@ustb.edu.cnXIE Jianxin, professor, Tel: (010)62332254, E-mail: jxxie@mater.ustb.edu.cn

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	Xingqun HE
	Huadong FU
	Hongtao ZHANG
	Jiheng FANG
	Ming XIE
	Jianxin XIE

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https://www.ams.org.cn/EN/10.11900/0412.1961.2021.00002 OR https://www.ams.org.cn/EN/Y2022/V58/I6/816

Fig.1 Strategy of silver alloy composition design aided by machine-learning (ML) (g is the performance to be predicted and f(x) is its corresponding model in step III. EC and HV in step IV represent the electrical conductivity and Vickers hardness of the alloys, respectively)

Table 1 Distribution range of sample dataset

Fig.2 Linear correlation performances of alloy factors for EC model (a) and hardness model (b) of silver alloy electrical contact material after linear correlation screening (The color bars in Figs.2a and b indicate the magnitude of the correlation coefficient values between the two features of electrical conductivity and hardness, respectively)

Fig.3 Results of backward recursive screening of alloy factors for EC model (a) and hardness model (b) of silver alloy electrical contact materials (MAPE—mean absolute percentage error)

Fig.4 Results of exhaustive screening of key alloy factors for EC model (a) and hardness model (b) of silver alloy electrical contact material

Table 2 Searching results of key alloy factor characteristics for EC model

Table 3 Searching results of key alloy factor characteristics for hardness model

Fig.5 Machine learning prediction model results based on support vector machine for EC model (a) and hardness model (b) (RMSE—root mean square error, R—correlation coefficient)

Table 4 Nominal compositions and actual compositions of the designed alloy

Table 5 Comparisons of predicted and measured properties of the designed alloys

Fig.6 EC (a) and hardness (b) changes during processing and heat treatment of the designed alloys (CA—as cast, HG—after homogenization, DH—drawing to diameter 2 mm at hard state, AN—after drawing to diameter 2 mm and annealing)

Fig.7 Tensile strength and elongation of the designed alloy after drawing to diameter 2 mm and annealing (a) and comparisons of properties between designed alloy and the alloys reported in literatures [21,22,38-40] (b)

Fig.8 SEM images of designed silver alloy electrical contact materials
(a-c) as-cast microstructures of alloys 1, 2, and 3, respectively (Insets show the magnified images) (d-f) cross section microstructures of alloys 1, 2, and 3 after drawing to diameter 2 mm and annealing, respectively (g-i) longitudinal section microstructures of alloys 1, 2, and 3 after drawing to diameter 2 mm and annealing, respectively

Fig.9 TEM images of the designed alloys consisting of α-Ag phase and β-Cu(Ag, Ni) phase after annealing at 550^oC
(a) alloy 1 (b) alloy 2 (c) alloy3

Table 6 EDS results of points 1-3 marked in Fig.9

1	Findik F, Uzun H. Microstructure, hardness and electrical properties of silver-based refractory contact materials [J]. Mater. Des., 2003, 24: 489 doi: 10.1016/S0261-3069(03)00125-0
2	Biyik S, Arslan F, Aydin M. Arc-erosion behavior of boric oxide-reinforced silver-based electrical contact materials produced by mechanical alloying [J]. J. Electron. Mater., 2015, 44: 457 doi: 10.1007/s11664-014-3399-4
3	Jung H, Nestler D, Wielage B, et al. Reinforcement of conducting silver-based materials [J]. Mater. Sci., 2014, 20: 247
4	Li H Y, Wang X H, Liu Y F, et al. Effect of strengthening phase on material transfer behavior of Ag-based contact materials under different voltages [J]. Vacuum, 2017, 135: 55 doi: 10.1016/j.vacuum.2016.10.031
5	Wang J, Tie S N, Kang Y Q, et al. Contact resistance characteristics of Ag-SnO₂ contact materials with high SnO₂ content [J]. J. Alloys Compd., 2015, 644: 438 doi: 10.1016/j.jallcom.2015.05.035
6	Ray N, Kempf B, Wiehl G, et al. Novel processing of Ag-WC electrical contact materials using spark plasma sintering [J]. Mater. Des., 2017, 121: 262 doi: 10.1016/j.matdes.2017.02.070
7	Xue D Z, Xue D Q, Yuan R H, et al. An informatics approach to transformation temperatures of NiTi-based shape memory alloys [J]. Acta Mater., 2017, 125: 532 doi: 10.1016/j.actamat.2016.12.009
8	Ren F, Ward L, Williams T, et al. Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments [J]. Sci. Adv., 2018, 4: eaaq1566 doi: 10.1126/sciadv.aaq1566
9	Shen C G, Wang C C, Wei X L, et al. Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel [J]. Acta Mater., 2019, 179: 201 doi: 10.1016/j.actamat.2019.08.033
10	Ling J, Hutchinson M, Antono E, et al. High-dimensional materials and process optimization using data-driven experimental design with well-calibrated uncertainty estimates [J]. Integr. Mater. Manuf. Innov., 2017, 6: 207 doi: 10.1007/s40192-017-0098-z
11	Liu R Q, Kumar A, Chen Z Z, et al. A predictive machine learning approach for microstructure optimization and materials design [J]. Sci. Rep., 2015, 5: 11551 doi: 10.1038/srep11551
12	Sun Y T, Bai H Y, Li M Z, et al. Machine learning approach for prediction and understanding of glass-forming ability [J]. J. Phys. Chem. Lett., 2017, 8: 3434 doi: 10.1021/acs.jpclett.7b01046 pmid: 28697303
13	Wang C S, Fu H D, Jiang L, et al. A property-oriented design strategy for high performance copper alloys via machine learning [J]. npj Comput. Mater., 2019, 5: 87 doi: 10.1038/s41524-019-0227-7
14	Zhang H T, Fu H D, He X Q, et al. Dramatically enhanced combination of ultimate tensile strength and electric conductivity of alloys via machine learning screening [J]. Acta Mater., 2020, 200: 803 doi: 10.1016/j.actamat.2020.09.068
15	Xue D Z, Balachandran P V, Hogden J, et al. Accelerated search for materials with targeted properties by adaptive design [J]. Nat. Commun., 2016, 7: 11241 doi: 10.1038/ncomms11241
16	Wen C, Zhang Y, Wang C X, et al. Machine learning assisted design of high entropy alloys with desired property [J]. Acta Mater., 2019, 170: 109 doi: 10.1016/j.actamat.2019.03.010
17	Sun Y, Zeng W D, Han Y F, et al. Optimization of chemical composition for TC11 titanium alloy based on artificial neural network and genetic algorithm [J]. Comput. Mater. Sci., 2011, 50: 1064 doi: 10.1016/j.commatsci.2010.11.002
18	GroupWriting. Precious Metal Processing Manual [M]. Beijing: Metallurgical Industry Press, 1978: 1
	《贵金属加工手册》编写组. 贵金属加工手册 [M]. 北京: 冶金工业出版社, 1978: 1
19	Ning Y T, Zhao H Z. Silver [M]. Changsha: Central South University Press, 2005: 1
	宁远涛, 赵怀志. 银 [M]. 长沙: 中南大学出版社, 2005: 1
20	Hu C Y, Liu S J. New Materials of Precious Metals [M]. Changsha: Central South University Press, 2015: 1
	胡昌义, 刘时杰. 贵金属新材料 [M]. 长沙: 中南大学出版社, 2015: 1
21	Wei M X, Liu Q, Gao Q Q, et al. Study the influence of rare earth elements on microstructure and property of AgCuNi alloy [J]. Precious Met., 2019, 40(suppl.1) : 31
	魏明霞, 柳青, 高勤琴等. 稀土元素对AgCuNi合金材料组织性能影响研究 [J]. 贵金属, 2019, 40(): 31
22	Dong P, Ren T, Qin G Y, et al. Microstructure and properties of Ag-15Cu-10Au-2Ni alloy [J]. J. Funct. Mater., 2019, 50: 3174
	董鹏, 任涛, 秦国义等. Ag-15Cu-10Au-2Ni合金的显微组织与性能 [J]. 功能材料, 2019, 50: 3174
23	Chen Y T, Wang S, Xie M, et al. Research progress in silver based sliding electrical contact material [J]. Precious Met., 2015, 36(1): 68
	陈永泰, 王松, 谢明等. 银基滑动电接触材料的研究进展 [J]. 贵金属, 2015, 36(1): 68
24	Li S Z, Zhang H R, Dai D B, et al. Study on the factors affecting solid solubility in binary alloys: An exploration by machine learning [J]. J. Alloys Compd., 2019, 782: 110 doi: 10.1016/j.jallcom.2018.12.136
25	Wojtasik K, Missol W. PM helps develop cadmium-free electrical contacts [J]. Met. Powd. Rep., 2004, 59: 34.
26	Huang F X, Li M, Ying P, et al. Effect of trace cerium on the as-cast microstructure of Ag-Cu-Ni alloy [J]. Mater. Sci. Forum, 2011, 687: 44 doi: 10.4028/www.scientific.net/MSF.687.44
27	Ferro R, Delfino S. Comments on the properties of Ag-rich alloys in the silver-rare earth systems [J]. J. Less Common Met., 1979, 68: 23 doi: 10.1016/0022-5088(79)90269-8
28	Hsueh H W, Hung F Y, Lui T S. Recrystallization of Ag and Ag-La alloy wire in wire bonding process [J]. Adv. Mater. Res., 2013, 804: 151
29	Louw N, Steel S J. Variable selection in kernel fisher discriminant analysis by means of recursive feature elimination [J]. Comput. Stat. Data An., 2006, 51: 2043 doi: 10.1016/j.csda.2005.12.018
30	Nagata K, Kitazono J, Nakajima S, et al. An exhaustive search and stability of sparse estimation for feature selection problem [J]. IPSJ Online Trans., 2015, 8: 25
31	Luo H T, Chen S W. Phase equilibria of the ternary Ag-Cu-Ni system and the interfacial reactions in the Ag-Cu/Ni couples [J]. J. Mater. Sci., 1996, 31: 5059 doi: 10.1007/BF00355906
32	Wloch G, Sokolowski K, Ostachowski P, et al. Decomposition of supersaturated solid solution during non-isothermal aging and its effect on the physical properties and microstructure of the Ag-Cu7.5 Alloy [J]. J. Mater. Eng. Perform., 2020, 29: 1488 doi: 10.1007/s11665-019-04517-x
33	Ghosh G, Miyake J, Fine M E, et al. The systems-based design of high-strength, high-conductivity alloys [J]. JOM, 1997, 49(3): 56
34	Zhan C G, Nichols J A, Dixon D A. Ionization potential, electron affinity, electronegativity, hardness, and electron excitation energy: Molecular properties from density functional theory orbital energies [J]. J. Phys. Chem., 2003, 107A: 4184
35	Li M, Lu P, Zhang H Q, et al. Electrochemical determination of the ionization potential and electron affinity of PF derivatives [J]. Chin. J. Lumin., 2006, 27: 80
	李茂, 路萍, 张海全等. 含芴聚合物电子亲合能和电离能的确定 [J]. 发光学报, 2006, 27: 80
36	Demir D, Turşucu A, Önülüer T. Studies on mass attenuation coefficient, effective atomic number and electron density of some vitamins [J]. Radiat. Environ. Biophys., 2012, 51: 469 doi: 10.1007/s00411-012-0427-8 pmid: 22733080
37	Owen P E A, Roberts E W. XXIX. Factors affecting the limit of solubility of elements in copper and silver [J]. Lond. Edinb. Dubl. Phil. Mag. J. Sci., 1939, 27: 294
38	Li J, Yang F T, Zhou S P, et al. Influence of trace amounts of cerium on properties of AgCuNi alloy [J]. Chin. J. Rare Met., 2007(suppl. 1) : 1
	李季, 杨富陶, 周世平等. 微量稀土元素铈对AgCuNi合金的性能影响 [J]. 稀有金属, 2007(): 1
39	Lu S P, Yang H M, Yang F T, et al. The effect of Zn on the properties of AgCuNi4-0.5 alloy [J]. Precious Met., 2013, 34(1): 21
	卢绍平, 杨红梅, 杨富陶等. Zn对AgCuNi4-0.5合金性能的影响 [J]. 贵金属, 2013, 34(1): 21
40	He X Y, Zhou S P, Wang J, et al. Effect of Cu on mechanical properties and recrystallization temperature of AgCe alloy [J]. Precious Met., 2008, 29(2): 11
	贺晓燕, 周世平, 王健等. Cu对AgCe合金机械性能及再结晶温度的影响 [J]. 贵金属, 2008, 29(2): 11
41	Hou J T, Hao Q L, Zhang L, et al. Study on the evolution of microstructure and properties during recrystallization of Ag-Cu alloy [J]. Precious Met., 2019, 40(suppl.1) : 40
	侯江涛, 郝庆乐, 张雷等. 银铜合金再结晶过程组织性能演变研究 [J]. 贵金属, 2019, 40(): 40
42	Dong P. Effects of Au(In、Sn) on microstructure and properties of Ag-20Cu-2Ni alloy [D]. Kunming: Yunnan University, 2019
	董鹏. Au(In、Sn)对Ag-20Cu-2Ni合金显微组织与性能的影响 [D]. 昆明: 云南大学, 2019
43	Xie J X, Su Y J, Xue D Z, et al. Machine learning for materials research and development [J]. Acta Metall. Sin., 2021, 57: 1343
	谢建新, 宿彦京, 薛德祯等. 机器学习在材料研发中的应用 [J]. 金属学报, 2021, 57: 1343 doi: 10.11900/0412.1961.2021.00357

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