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Predicting the optimum compositions of high-performance Cu–Zn alloys via machine learning

Published online by Cambridge University Press:  21 September 2020

Baobin Xie ,
Qihong Fang ,
Jia Li Open the ORCID record for Jia Li [Opens in a new window]  and
Peter K. Liaw
Baobin Xie
Affiliation:
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha410082, PR China
Qihong Fang*
Affiliation:
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha410082, PR China
Jia Li*
Affiliation:
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha410082, PR China
Peter K. Liaw
Affiliation:
Department of Materials Science and Engineering, The University of Tennessee, Knoxville, Tennessee37996, USA
*
a)Address all correspondence to these authors. e-mail: [email protected]
b)e-mail: [email protected]
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Abstract

In the alloy materials, their mechanical properties mightly rely on the compositions and concentrations of chemical elements. Therefore, looking for the optimum elemental concentration and composition is still a critical issue to design high-performance alloy materials. Traditional alloy designing method via “trial and error” or domain experts’ experiences is barely possible to solve the issue. Here, we propose a “composition-oriented” method combined machine learning to design the Cu–Zn alloys with the high strengths, high ductility, and low friction coefficient. The method of separate training for each attribute label is used to study the effects of elemental concentrations on the mechanical properties of Cu–Zn alloys. Moreover, the elemental concentrations of new Cu–Zn alloys with the good mechanical properties are predicted by machine learning. The current results reveal the vital importance of the “composition-oriented” design method via machine learning for the development of high-performance alloys in a broad range of elemental compositions.

Keywords

copper alloyoptimum elemental compositionbest mechanical propertiesmachine learning
Type
Article
Information
Journal of Materials Research , Volume 35 , Issue 20 , 28 October 2020 , pp. 2709 - 2717
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press on behalf of Materials Research Society

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References

Tang, M., Zhang, Y., Jiang, S., Yu, J., Yan, B., Zhao, C., and Yan, B.: Microstructural evolution and related mechanisms in NiTiCu shape memory alloy subjected local canning compression. Intermetallics 118, 106700 (2020).CrossRefGoogle Scholar
An, X.H., Lin, Q.Y., Wu, S.D., Zhang, Z.F., Figueiredo, R.B., Gao, N., and Langdon, T.G.: The influence of stacking fault energy on the mechanical properties of nanostructured Cu and Cu–Al alloys processed by high-pressure torsion. Scr. Mater. 64, 954 (2011).CrossRefGoogle Scholar
Zhao, Y.H., Liao, X.Z., Horita, Z., Langdon, T.G., and Zhu, Y.T.: Determining the optimal stacking fault energy for achieving high ductility in ultrafine-grained Cu–Zn alloys. Mater. Sci. Eng. A 493, 123 (2008).CrossRefGoogle Scholar
Jiang, Y., Li, Z., Xiao, Z., Xing, Y., Zhang, Y., and Fang, M.: Microstructure and properties of a Cu-Ni-Sn alloy treated by two-stage thermomechanical processing. JOM 71, 2734 (2019).CrossRefGoogle Scholar
Luo, B., Li, D., Zhao, C., Wang, Z., Luo, Z., and Zhang, W.: A low Sn content Cu-Ni-Sn alloy with high strength and good ductility. Mater. Sci. Eng. A 746, 154 (2019).CrossRefGoogle Scholar
Inoue, A., Zhang, W., Zhang, T., and Kurosaka, K.: High-strength Cu-based bulk glassy alloys in Cu–Zr–Ti and Cu–Hf–Ti ternary systems. Acta Mater. 49, 2645 (2001).CrossRefGoogle Scholar
Sjölander, E. and Seifeddine, S.: The heat treatment of Al–Si–Cu–Mg casting alloys. J. Mater. Process. Technol. 210, 1249 (2010).CrossRefGoogle Scholar
Kim, K.S., Huh, S.H., and Suganuma, K.: Effects of cooling speed on microstructure and tensile properties of Sn–Ag–Cu alloys. Mater. Sci. Eng. A 333, 106 (2002).CrossRefGoogle Scholar
Tang, D., Wang, L., Li, J., Wang, Z., Kong, C., and Yu, H.: Microstructure, element distribution, and mechanical property of Cu9Ni6Sn alloys by conventional casting and twin-roll casting. Metall. Mater. Trans. A 51, 1469 (2020).Google Scholar
Basak, C.B. and Poswal, A.K.: Compositional partitioning during the spinodal decomposition in Cu–Ni–Sn alloy. Philos. Mag. 98, 1204 (2018).CrossRefGoogle Scholar
Schaffer, G.B., Huo, S.H., Drennan, J., and Auchterlonie, G.J.: The effect of trace elements on the sintering of an Al–Zn–Mg–Cu Alloy. Acta Mater. 49, 2671 (2001).CrossRefGoogle Scholar
Kuehmann, C. and Olson, G.B.: Computational materials design and engineering. Mater. Sci. Eng. A 25, 472 (2009).Google Scholar
Reed, R.C., Tao, T., and Warnken, N.: Alloys-by-design: Application to nickel-based single crystal superalloys. Acta Mater. 57, 5898 (2009).CrossRefGoogle Scholar
Kim, G., Diao, H., Lee, C., Samaei, A.T., Phan, T., De Jong, M., and Chen, W.: First-principles and machine learning predictions of elasticity in severely lattice-distorted high-entropy alloys with experimental validation. Acta Mater. 181, 124 (2019).CrossRefGoogle Scholar
Datta, S. and Banerjee, M.K.: Mapping the input–output relationship in HSLA steels through expert neural network. Mater. Sci. Eng. A 420, 254 (2006).CrossRefGoogle Scholar
Bhadeshia, H.K.D.H., MacKay, D.J.C., and Svensson, L.E.: Impact toughness of C-Mn steel arc welds - Bayesian neural network analysis. Mater. Sci. Technol. 11, 1046 (1995).CrossRefGoogle Scholar
Pak, J.H., Jang, J.H., Bhadeshia, H.K.D.H., and Karlsson, L.: Optimization of neural network for Charpy toughness of steel welds. Mater. Manuf. Process. 24, 16 (2008).CrossRefGoogle Scholar
Wu, C.T., Chang, H.T., Wu, C.Y., Chen, S.W., Huang, S.Y., Huang, M.X., Pan, Y.T., Bradbury, P., Chou, J., and Yen, H.W.: Machine learning recommends affordable new Ti alloy with bone-like modulus. Mater. Today 34, 41 (2020).CrossRefGoogle Scholar
Li, J., Xie, B.B., Fang, Q.H., Liu, B., Liu, Y., and Liaw, P.K.: High-throughput simulation combined machine learning search for optimum elemental composition in medium entropy alloy. J. Mater. Sci. Technol. doi: https://doi.org/10.1016/j.jmst.2020.08.008. Published online 9 August 2020.Google Scholar
Reddy, N.S., Krishnaiah, J., Young, H.B., and Lee, J.S.: Design of medium carbon steels by computational intelligence techniques. Comput. Mater. Sci. 101, 120 (2015).CrossRefGoogle Scholar
Ozerdem, M.S. and Kolukisa, S.: Artificial neural network approach to predict the mechanical properties of Cu-Sn-Pb-Zn-Ni cast alloys. Mater. Des. 30, 764–769 (2009).CrossRefGoogle Scholar
Akutsu, H. and Saitama, S.: Synchronizerring in speed varator made of wear-resistant copper alloy having high strength and toughness. U.S. Patent, 4874439, 1989.Google Scholar
Kuhn, M.: Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).CrossRefGoogle Scholar
Li, J., Li, L., Jiang, C., Fang, Q., Liu, F., Liu, Y., and Liaw, P.K.: Probing deformation mechanisms of gradient nanostructured CrCoNi medium entropy alloy. J. Mater. Sci. Technol. 57, 85–91 (2020).CrossRefGoogle Scholar
Li, L., Chen, H., Fang, Q., Li, J., Liu, F., Liu, Y., and Liaw, P.K.: Effects of temperature and strain rate on plastic deformation mechanisms of nanocrystalline high-entropy alloys. Intermetallics 120, 106741 (2020).CrossRefGoogle Scholar
Zhang, Y., Jin, S., Trimby, P.W., Liao, X., Murashkin, M.Y., Valiev, R.Z., Liu, J., Cairney, J.M., Ringer, S.P., and Sha, G.: Dynamic precipitation, segregation and strengthening of an Al-Zn-Mg-Cu alloy (AA7075) processed by high-pressure torsion. Acta Mater. 162, 19–32 (2019).CrossRefGoogle Scholar
He, J.Y., Wang, H., Huang, H.L., Xu, X.D., Chen, M.W., Wu, Y., Liu, X.J., Nieh, T.G., An, K., and Lu, Z.P.: A precipitation-hardened high-entropy alloy with outstanding tensile properties. Acta Mater. 102, 187–196 (2016).CrossRefGoogle Scholar
Gao, Y.H., Cao, L.F., Yang, C., Zhang, J.Y., Liu, G., and Sun, J.: Co-stabilization of θ′-Al2Cu and Al3Sc precipitates in Sc-microalloyed Al-Cu alloy with enhanced creep resistance. Mater. Today 6, 100035 (2019).Google Scholar
Yi, J., Jia, Y.L., Zhao, Y.Y., Xiao, Z., He, K.J., Wang, Q., Wang, M.P., and Li, Z.: Precipitation behavior of Cu-3.0Ni-0.72Si alloy. Acta Mater. 166, 261–270 (2019).CrossRefGoogle Scholar
Wen, H.M., Topping, T.D., Isheim, D., Seidman, D.N., and Lavernia, E.J.: Strengthening mechanisms in a high-strength bulk nanostructured Cu-Zn-Al alloy processed via cryomilling and spark plasma sintering. Acta Mater. 61, 2769–2782 (2013).CrossRefGoogle Scholar
Lei, Q., Li, Z., Xiao, T., Pang, Y., Xiang, Z.Q., Qiu, W.T., and Xiao, Z.: A new ultrahigh strength Cu-Ni-Si alloys. Intermetallics 42, 77–84 (2013).CrossRefGoogle Scholar
Elhadari, H.A., Patel, H.A., Chen, D.L., and Kasprzak, W.: Tensile and fatigue properties of a cast aluminum alloy with Ti, Zr and V additions. Mater. Sci. Eng. A 528, 8128–8138 (2011).CrossRefGoogle Scholar
Hu, T., Chen, J.H., Liu, J.Z., Liu, Z.R., and Wu, C.L.: The crystallographic and morphological evolution of the strengthening precipitates in Cu-Ni-Si alloys. Acta Mater. 61, 1210–1219 (2013).CrossRefGoogle Scholar
Li, J., Huang, G.J., Mi, X.J., Peng, L.J., Xie, H.F., and Kang, Y.L.: Effect of Ni/Si mass ratio and thermomechanical treatment on the microstructure and properties of Cu-Ni-Si alloys. Materials 12, 2076 (2019).CrossRefGoogle ScholarPubMed
Turhana, H., Aksoyb, M., Kuzucuc, V., and Yildirim, M.M.: The effect of manganese on the microstructure and mechanical properties of leaded-tin bronze. J. Mater. Process. Technol. 114, 207–211 (2001).CrossRefGoogle Scholar
Men, H., Kim, W.T., and Kim, D.H.: Effect of titanium on glass-forming ability of Cu-Zr-Al alloys. Mater. Trans. 44, 1647–1650 (2003).CrossRefGoogle Scholar
Aksoy, M., Kuzucu, V., and Turhan, H.: A note on the effect of phosphorus on the microstructure and mechanical properties of leaded-tin bronze. J. Mater. Process. Technol. 124, 113–119 (2002).CrossRefGoogle Scholar
Ma, X., Chen, J., Liu, Y., Wang, X., and Huang, S.: Effect of short-range order on microstructure, texture and strain hardening of cold drawn Cu-10at.%Mn alloy. Mater. Charact. 135, 32–39 (2018).CrossRefGoogle Scholar
Liu, L., Chen, Z., Liu, C., Wu, Y., and An, B.: Micro-mechanical and fracture characteristics of Cu6Sn5 and Cu3Sn intermetallic compounds under micro-chatilever bending. Intermetallics 76, 10 (2016).CrossRefGoogle Scholar
Sandstrom, R. and Andersson, H.: The effect of phosphorus on creep in copper. J. Nucl. Mater. 372, 66 (2008).CrossRefGoogle Scholar
Dar, S.M., Liao, H.C., and Xu, A.: Effect of Cu and Mn content on solidification microstructure, T-phase formation and mechanical property of Al-Cu-Mn alloys. J. Alloys Compd. 774, 758 (2019).CrossRefGoogle Scholar
An, X.H., Wu, S.D., Wang, Z.G., and Zhang, Z.F.: Significance of stacking fault energy in bulk nanostructured materials: insights from Cu and its binary alloys as model systems. Prog. Mater. Sci. 101, 1 (2019).CrossRefGoogle Scholar
Tian, Y.Z., Zhao, L.J., Park, N., Liu, R., Zhang, P., Zhang, Z.J., Shibata, A., Zhang, Z.F., and Tsuji, N.: Revealing the deformation mechanisms of Cu–Al alloys with high strength and good ductility. Acta Mater. 110, 61 (2016).CrossRefGoogle Scholar
Krishna, S.C. and Srinath, J.: Microstructure and properties of a high-strength Cu-Ni-Si-Co-Zr alloy. J. Mater. Eng. Perform. 22, 2115 (2013).CrossRefGoogle Scholar
Orimoloye, L.O., Sung, M.C., Ma, T., and Johnson, J.E.V.: Comparing the effectiveness of deep feedforward neural networks and shallow architectures for predicting stock price indices. Expert Syst. Appl. 139, 112828 (2020).CrossRefGoogle Scholar
Nourani, V., Gökçekuş, H., and Umar, I.K.: Artificial intelligence based ensemble model for prediction of vehicular traffic noise. Environ. Res. 180, 108852 (2020).CrossRefGoogle ScholarPubMed