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.

中文翻译：

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通过机器学习辅助优化两阶段时效加工来改善 Cu-Ni-Si 合金的机械和电气性能


最近的研究表明，连续析出物 （CPs） 和不连续析出物 （DPs） 的协同沉淀是同时提高 Cu-Ni-Si 合金强度和导电性的一种很有前途的方法。然而，沉淀物与两阶段老化过程之间的复杂关系对工艺参数的优化提出了重大挑战。本研究基于正交实验建立了机器学习模型，挖掘了Cu-5.3Ni-1.3Si-0.12Nb合金两阶段时效参数与性能之间的关系，通过多目标优化结合实验迭代策略，得到400 °C/75 min+400 °C/30 min的两阶段时效参数，拉伸强度为875 MPa，电导率为41.43 %IACS， 分别。合金如此出色的综合性能归因于 DPs 和 CPs 的联合沉淀（总体积分数为 5.4%，CPs 与 DPs 的体积比为 6.7）。本研究为提高Cu-Ni-Si合金的综合性能提供了新的途径和见解。