Improving mechanical and electrical properties of Cu-Ni-Si alloy via machine learning assisted optimization of two-stage aging processing

doi:10.1016/j.jmst.2024.09.039

J. Mater. Sci. Technol. ›› 2025, Vol. 221: 155-167.DOI: 10.1016/j.jmst.2024.09.039

• Research Article • Previous Articles Next Articles

Improving mechanical and electrical properties of Cu-Ni-Si alloy via machine learning assisted optimization of two-stage aging processing

Jinyu Liang^a,b, Fan Zhao^a,c*, Guoliang Xie^d, Rui Wang^a,b, Xiao Liu^a,b, Wenli Xue^a,b, Xinhua Liu^a,e,f,*

^aKey Laboratory for Advanced Materials Processing (MOE), Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China;
^bBeijing Laboratory of Metallic Materials and Processing for Modern Transportation, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China;
^cInstitute of Materials Intelligent Technology, Liaoning Academy of Materials, Shenyang 110167, China;
^dState Key Laboratory for Advanced Metals and Materials, University of Science and Technology Beijing, Beijing 100083, China;
^eBeijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China;
^fInstitute of Materials Genome Engineering, Henan Academy of Sciences, Zhengzhou 450046, China

Received:2024-07-18 Revised:2024-09-02 Accepted:2024-09-19 Published:2024-10-18 Online:2024-10-18
Contact: ^*E-mail addresses: zhaofan@ustb.edu.cn (F. Zhao), liuxinhua18@163.com (X. Liu)

Abstract

Abstract: Recent studies have shown that synergistic precipitation of continuous precipitates (CPs) and discontinuous precipitates (DPs) is a promising method to simultaneously improve the strength and electrical conductivity of Cu-Ni-Si alloy. However, the complex relationship between precipitates and two-stage aging process presents a significant challenge for the optimization of process parameters. In this study, machine learning models were established based on orthogonal experiment to mine the relationship between two-stage aging parameters and properties of Cu-5.3Ni-1.3Si-0.12Nb alloy with preferred formation of DPs. Two-stage aging parameters of 400 °C/75 min + 400 °C/30 min were then obtained by multi-objective optimization combined with an experimental iteration strategy, resulting in a tensile strength of 875 MPa and a conductivity of 41.43 %IACS, respectively. Such an excellent comprehensive performance of the alloy is attributed to the combined precipitation of DPs and CPs (with a total volume fraction of 5.4 % and a volume ratio of CPs to DPs of 6.7). This study could provide a new approach and insight for improving the comprehensive properties of the Cu-Ni-Si alloys.

Key words: Cu-Ni-Si alloy, Machine learning, Strength, Electrical conductivity, Discontinuous precipitates

Jinyu Liang, Fan Zhao, Guoliang Xie, Rui Wang, Xiao Liu, Wenli Xue, Xinhua Liu. Improving mechanical and electrical properties of Cu-Ni-Si alloy via machine learning assisted optimization of two-stage aging processing[J]. J. Mater. Sci. Technol., 2025, 221: 155-167.

References

[1] Y. Geng, Y. Ban, B. Wang, X. Li, K. Song, Y. Zhang, Y. Jia, B. Tian, Y. Liu, A .A. Volinsky, J.Mater. Res. Technol. 9(2020) 11918-11934.
[2] W. Liao, H. Yang, C. Yi, J. Zheng, Mater. Sci. Eng. A 833 (2022) 142577.
[3] K. Yang, Y. Wang, M. Guo, H. Wang, Y. Mo, X. Dong, H. Lou, Prog. Mater. Sci. 138(2023) 101141.
[4] Q. Lei, S. Li, J. Zhu, Z. Xiao, F. Zhang, Z. Li, Mater. Charact. 147(2019) 315-323.
[5] C. Bellini, A. Brotzu, V.D. Cocco, F. Felli, F. Iacoviello, D. Pilone, Proc. Struct. Integr. 26(2020) 330-335.
[6] K. Fukamachi, M. Kimura, Mater. Sci. Eng. A 831 (2022) 142220.
[7] W. Liao, Y. Hu, Q. Liu, Mater. Sci. Eng. A 846 (2022) 143283.
[8] S. Tao, Z. Lu, H. Xie, J. Zhang, X. Wei, Mater. Lett. 315(2022) 131790.
[9] S. Semboshi, S. Sato, A. Iwase, T. Takasugi, Mater. Charact. 115(2016) 39-45.
[10] M. Goto, T. Yamamoto, S.Z. Han, T. Utsunomiya, S. Kim, J. Kitamura, J.H. Ahn, S.H. Lim, J. Lee, Mater. Lett. 288(2021) 129353.
[11] B. Krupi ńska, W. Borek, M. Krupi ński, T. Karkoszka, Materials 13 (2020) 3416.
[12] J.Y. Cheng, B.B. Tang, F.X. Yu, B. Shen, J. Alloy. Compd. 614(2014) 189-195.
[13] Y. Wu, Y. Li, J. Lu, S. Tan, F. Jiang, J. Sun, Mater. Sci. Eng. A 731 (2018) 403-412.
[14] W. Wang, J. Wang, S. Li, C. Wang, J. Zhou, J. Zeng, W. Tan, B. Wang, Mater. Charact. 194(2022) 112451.
[15] L. Jiang, H. Fu, C. Wang, W. Li, J. Xie, Metall. Mater. Trans. A 51 (2020) 331-341.
[16] J.H. Ahn, S.Z. Han, E. Choi, H. Lee, S.H. Lim, J. Lee, K. Kim, N.M. Hwang, H.N. Han, Mater. Charact. 170(2020) 110642.
[17] H. Kim, J.H. Ahn, S.Z. Han, J. Jo, H. Baik, M. Kim, H.N. Han, J. Alloy. Compd. 832(2020) 155059.
[18] J.H. Ahn, S.Z. Han, E. Choi, G.Y. Lee, K. Kim, J. Lee, H.N. Han, Mater. Charact. 184(2022) 111605.
[19] Y. Cao, S.Z. Han, E. Choi, J.H. Ahn, X. Mi, G. Huang, S. Lee, H. Shin, S. Kim, J. Lee, Phil. Mag. Lett. 101(2021) 51-59.
[20] X. Meng, G. Xie, W. Xue, Y. Fu, R. Wang, X. Liu, Materials 15 (2022) 4521.
[21] Y. Shan, Y. Zhang, C. Zhang, J. Feng, B. Huang, S. Zhao, K. Song, Mater. Sci. Eng. A 908 (2024) 146946.
[22] Y. Chen, Y. Tian, Y. Zhou, D. Fang, X. Ding, J. Sun, D. Xue, J. Alloy. Compd. 844(2020) 156159.
[23] H. Zhang, H. Fu, X. He, C. Wang, L. Jiang, L. Chen, J. Xie, Acta Mater. 200(2020) 803-810.
[24] C. Wen, C. Wang, Y. Zhang, S. Antonov, D. Xue, T. Lookman, Y. Su, Acta Mater. 212(2021) 116917.
[25] K. Guo, H. Lu, Z. Zhao, F. Tang, H. Wang, X. Song, Comput. Mater. Sci. 205(2022) 111232.
[26] H. Zhang, H. Fu, Y. Shen, J. Xie, Int. J. Min. Met. Mater. 29(2022) 1197-1205.
[27] Y. Chen, S. Wang, J. Xiong, G. Wu, J. Gao, Y. Wu, G. Ma, H. Wu, X. Mao, J. Mater. Sci.Technol. 132(2023) 213-222.
[28] L. Jiang, Z. Zhang, H. Hu, X. He, H. Fu, J. Xie, npj Comput.Mater. 9(2023) 12-26.
[29] Y. Liu, Z. Wang, Y. Wang, Y. Li, F. Zhao, Z. Zhang, X. Liu, Mater. Des. 242(2024) 113011.
[30] H. Ji, Y. Lyu, W. Ying, J. Liu, H. Ye, J. Build. Eng. 91(2024) 109710.
[31] J. Wei, C. Liu, Y. Wan, J. Shao, X. Han, G. Zhang, Mater. Charact. 167(2020) 110523.
[32] S.Y. Wang, F.C. Lang, Y.M. Xing, Compos. Sci. Technol. 242(2023) 110215.
[33] J. Zhu, Z. Li, S.T. Yang, Y.R. Zhao, F.C. Lang, Y.M. Xing, Opt. Laser Eng. 163(2023) 107457.
[34] J. Shen, Z. Zhang, J. Xie, Mater. Sci. Eng. A 897 (2024) 146338.
[35] F. Liu, G. Xie, S. Wang, J. Yang, C. Chen, X. Liu, Mater. Sci. Eng. A 867 (2023) 144689.
[36] H. Feng, L. Wang, S. Cui, N. Hansen, F. Fang, X. Zhang, Scr. Mater. 200(2021) 113906.
[37] H. Zhang, Y. Jiang, J. Xie, Y. Li, L. Yue, J. Alloy. Compd. 773(2019) 1121-1130.
[38] C. Wang, H. Fu, H. Zhang, X. He, J. Xie, Mater. Sci. Eng. A 838 (2022) 142815.
[39] W. Liao, C. Zhang, H. Qiang, W. Song, Y. Hu, J. Alloy. Compd. 936(2023) 168281.
[40] P. Stavroulakis, A. Toulfatzis, A. Vazdirvanidis, G. Pantazopoulos, S. Pa- paefthymiou, Metallogr.Microstruc. 8(2019) 167-181.
[41] E. Lee, S. Han, K. Euh, S. Lim, S. Kim, Met. Mater. Int. 17(2011) 569-576.
[42] E. Lee, K. Euh, S.Z. Han, S. Lim, J. Lee, S. Kim, Met. Mater. Int. 19(2013) 183-188.
[43] W. Wang, H. Kang, Z. Chen, Z. Chen, C. Zou, R. Li, G. Yin, T. Wang, Mater. Sci. Eng. A 673 (2016) 378-390.

Improving mechanical and electrical properties of Cu-Ni-Si alloy via machine learning assisted optimization of two-stage aging processing

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Zijia Liao, Hesamoddin Rabiee, Lei Ge, Xiaogang Li, Zhaozhong Yang, Qi Xue, Chao Shen, Hao Wang. Revealling pore microstructure impacts on the compressive strength of porous proppant based on finite and discrete element method [J]. J. Mater. Sci. Technol., 2025, 211(0): 72-81.
[2]	Nan Jiang, Hong Bian, Xiaoguo Song, Hyeonseok Kwon, Xin Xi, Danyang Lin, Bo Chen, Weimin Long, Hyoung Seop Kim, Lianhui Jia. Higher entropy-induced strengthening in mechanical property of Cantor alloys/Zr-3 joints by laser in-situ eutectic high-entropy transformation [J]. J. Mater. Sci. Technol., 2025, 211(0): 110-122.
[3]	Yuxiang Chen, Mingyang Li, Ningyu Li, Yijie Wang, Kang Liu, Yongqin Chang. Microstructure and mechanical properties of TiNbV_0.5Ta_0.5Cr_x (x=0, 0.1, 0.2, 0.5) refractory high-entropy alloys [J]. J. Mater. Sci. Technol., 2025, 211(0): 254-266.
[4]	Boshen Zhao, Zhihao Ren, Hui Chang, Zhengfei Zhou, Yi Ding. Role of Cr addition in Ni-based interlayer on strengthening titanium and steel dissimilar bimetallic structures enabled by directed energy deposition with laser beam [J]. J. Mater. Sci. Technol., 2025, 209(0): 27-42.
[5]	H. Yu, S.J. Niu, C. Liu, L.P. Tian, L.W. Quan, Z.K. Liu, Y.L. Xu, L.X. Huang, K.S. Shin, W. Yu, B.A. Jiang. Influence of pre-stretching and annealing on microstructure and mechanical properties evolution of twin-roll cast Mg-2Al-1Zn-1Ca (wt.%) plates [J]. J. Mater. Sci. Technol., 2025, 209(0): 171-184.
[6]	Youhong Peng, Li Wang, Chenglu Liu, Chao Xu, Lin Geng, Guohua Fan. Revealing the exceptional cryogenic strength-ductility synergy of a solid solution 6063 alloy by in-situ EBSD experiments [J]. J. Mater. Sci. Technol., 2025, 208(0): 313-322.
[7]	Maoyin Li, Stevan Cokic, Bart Van Meerbeek, Jef Vleugels, Fei Zhang. Novel zirconia ceramics for dental implant materials [J]. J. Mater. Sci. Technol., 2025, 210(0): 97-108.
[8]	Yuliang Zhao, Weixiang He, Feiyu Zhao, Chenghao Song, Weiwen Zhang, Dongfu Song, Yue Tang, Zhenzhong Sun, Wen Yin, Yanling Xue, Runxia Li, Ricardo Fernández. Insight into the Fe-rich phases strengthening mechanisms of non-heat-treatable Al-Mg-Mn-Fe-Cu alloys [J]. J. Mater. Sci. Technol., 2025, 205(0): 232-246.
[9]	Xin Wu, Peiyuan Kang, Yinghan Zhang, Haocheng Guo, Shuoying Yang, Qi Zheng, Lianjun Wang, Wan Jiang. Highly electrically conductive MOF/conducting polymer nanocomposites toward tunable electromagnetic wave absorption [J]. J. Mater. Sci. Technol., 2025, 205(0): 258-269.
[10]	Xin Chen, Lujun Huang, Shuo Ma, Fengbo Sun, Shuai Wang, Lin Geng. Multi-scale dispersion strengthening for high-temperature titanium alloys: Strength preservation and softening mechanisms [J]. J. Mater. Sci. Technol., 2025, 206(0): 1-14.
[11]	Xiuwen Ren, Zhongjin Wang, Ruidong An. A promising approach to enhance fatigue life of TC11 titanium alloy: Low dislocation density and surface grain refinement induced by electropulsing [J]. J. Mater. Sci. Technol., 2025, 204(0): 60-70.
[12]	Akash A. Deshmukh, Raghavan Ranganathan. Recent advances in modelling structure-property correlations in high-entropy alloys [J]. J. Mater. Sci. Technol., 2025, 204(0): 127-151.
[13]	Yuan Yuan, Yong Han, Kai Xu, Sisi Tang, Yaohua Zhang, Yaozha Lv, Yihan Yang, Xue Jiang, Keke Chang. Revealing the solidification microstructure evolution and strengthening mechanisms of additive-manufactured W-FeCrCoNi alloy: Experiment and simulation [J]. J. Mater. Sci. Technol., 2025, 204(0): 302-313.
[14]	Kang Tu, Bo Li, Zonglin Li,Kaisheng Ming, Shijian Zheng. Dual heterogeneous structure enabled ultrahigh strength and ductility across a broad temperature range in CrCoNi-based medium-entropy alloy [J]. J. Mater. Sci. Technol., 2025, 207(0): 46-59.
[15]	Keer Li, Wei Chen, Jinyu Zhang, Shewei Xin, Jun Sun. Making titanium alloys ultrahigh strength and toughness synergy through deformation kinks-mediated hierarchical α-precipitation [J]. J. Mater. Sci. Technol., 2025, 207(0): 142-159.