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Volume 29 Issue 6
Jun.  2022
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文章导航 >  矿物冶金与材料学报（英文）  > 2022  >  29(6): 1197-1205
Hongtao Zhang, Huadong Fu, Yuheng Shen, and Jianxin Xie, Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu–Ni–Co–Si–X alloy via Bayesian optimization machine learning, Int. J. Miner. Metall. Mater., 29(2022), No. 6, pp. 1197-1205. https://doi.org/10.1007/s12613-022-2479-3
Cite this article as:
Hongtao Zhang, Huadong Fu, Yuheng Shen, and Jianxin Xie, Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu–Ni–Co–Si–X alloy via Bayesian optimization machine learning, Int. J. Miner. Metall. Mater., 29(2022), No. 6, pp. 1197-1205. https://doi.org/10.1007/s12613-022-2479-3
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研究论文封面文章

基于贝叶斯优化的超低Co含量新型Cu–Ni–Co–Si–X合金二次形变-时效参数快速设计

  • 1)

    北京科技大学 北京材料基因工程高精尖创新中心，北京 100083，中国

  • 2)

    北京科技大学 材料先进制备技术教育部重点实验室，北京 100083，中国

  • 3)

    北京科技大学 现代交通金属材料与加工技术北京实验室，北京 100083，中国

  • 通讯作者:

    付华栋    E-mail: hdfu@ustb.edu.cn

    谢建新    E-mail: jxxie@mater.ustb.edu.cn

文章亮点

  • (1) 提出了一种工艺参数的快速设计策略，并实现了铜合金二次形变-时效参数的快速设计。
  • (2) 工艺参数优化后超低Co含量新型Cu–Ni–Co–Si–X合金性能水平达到C70350合金的性能。
  • (3) 探究了合金二次形变-时效处理后的显微组织与强化机制。
  • 采用传统试错方法快速设计铜合金工艺参数，同步提升呈倒置矛盾关系的力学和导电性能非常困难。本研究旨在开发一种稀贵Co元素节约型Cu–Ni–Co–Si合金，Co含量低于C70350合金（ASTM标准）下限的一半，而性能达到与C70350合金相同水平。采用贝叶斯优化算法和实验迭代相结合的研究策略，突破时效强化铜合金形变-时效参数试错设计效率低、成本高的难题，仅用32次实验（包括27次基本样本数据获取实验，5次迭代验证实验）快速设计了新型铜合金的二次形变-时效参数（冷轧变形量90%，时效温度450°C，时效时间1.25 h）。经实验验证新型铜合金样品的硬度、抗拉强度和导电率为HV (285 ± 4)、(872 ± 3) MPa和(44.2 ± 0.7)% IACS，达到商用引线框架C70350合金的性能水平。本文研究工作为实现材料制备工艺参数快速设计、力学和导电性能同步提升提供了新的思路。
    • Research Article

    Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu–Ni–Co–Si–X alloy via Bayesian optimization machine learning

    + Author Affiliations
      Abstract
    • It is difficult to rapidly design the process parameters of copper alloys by using the traditional trial-and-error method and simultaneously improve the conflicting mechanical and electrical properties. The purpose of this work is to develop a new type of Cu–Ni–Co–Si alloy saving scarce and expensive Co element, in which the Co content is less than half of the lower limit in ASTM standard C70350 alloy, while the properties are as the same level as C70350 alloy. Here we adopted a strategy combining Bayesian optimization machine learning and experimental iteration and quickly designed the secondary deformation-aging parameters (cold rolling deformation 90%, aging temperature 450°C, and aging time 1.25 h) of the new copper alloy with only 32 experiments (27 basic sample data acquisition experiments and 5 iteration experiments), which broke through the barrier of low efficiency and high cost of trial-and-error design of deformation-aging parameters in precipitation strengthened copper alloy. The experimental hardness, tensile strength, and electrical conductivity of the new copper alloy are HV (285 ± 4), (872 ± 3) MPa, and (44.2 ± 0.7)% IACS (international annealed copper standard), reaching the property level of the commercial lead frame C70350 alloy. This work provides a new idea for the rapid design of material process parameters and the simultaneous improvement of mechanical and electrical properties.
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