Design of a novel Cu-Cr-X alloy with high strength and high electrical conductivity based on mechanical learning
Yunqing Zhu, Yicheng Cao, Lijun Peng, Qian Yu, Zhen Yang, Zengde Li, Junsheng Wu, Haofeng Xie
Abstract
• A novel Cu-Cr-Zr-Mg-Ti alloy with excellent strength and electrical conductivity was designed by BP neural network and GA algorithm. • The Zr, Mg and Ti elements segregated in the Cr-rich phase and inhibits the Cr precipitates of transformation from fcc to bcc structure. • The process sensitivity of Cu-Cr-Zr-Mg-Ti alloys was guided aid of predictive models. • The Cu 5 Zr phase tend to accompany precipitation along the bcc-Cr phase during aging at 480 °C, and maintained ( 1 ¯ 13 ) Cu5Zr // ( 0 1 ¯ 1 ) Cr and [011] Cu5Zr //[111] Cr OR during aging. Micro-alloying and thermo-mechanical treatments are crucial to the further development of high-strength and high-conductivity Cu-Cr-X alloys. In this study, a high accuracy ultimate tensile strength and electrical conductivity prediction model was obtained by training the BP neural networks with different compositions and processing experimental data. The Cu-Cr-Zr-Mg-Ti alloy with superior properties was optimally designed by genetic algorithm from the massive solutions, which the experimental tensile strength and conductivity reached 668 MPa and 71.5 %IACS, respectively. The atom probe tomography results show that Zr, Mg, and Ti simultaneously segregated in the Cr-rich phase after aging at 440 °C for 480 h, which significantly improves the stability of the precipitated Cr phase and inhibited the Cr-rich phase transition from fcc to bcc structure. With the increase of aging temperature, the bcc structure Cr phase gradually replaces the fine fcc-Cr phase and exhibited a higher coarsening rate. It was found that the addition of Zr tends to nucleation at the interface of the K-S orientation relationship (OR) bcc-Cr phases. The Cu 5 Zr phase maintains the ( 1 ¯ 13 ) Cu5Zr // ( 0 1 ¯ 1 ) Cr and [011] Cu5Zr //[111] Cr ORs with the bcc structure Cr precipitates. The evolution of microstructure and properties exhibited a narrow aging process region of 80 % cold-rolled Cu-Cr-Zr-Mg-Ti alloy, while the consistent results from prediction model provide a favorable guidance for process designs. The outstanding performance enhancement and rapid prediction of process scopes leads us to recognise that the great potential of multi-objective machine-learning-aid design in the complex composition and process problems.