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

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.