Data-augmented machine learning design and performance-enhancing quaternary synergistic mechanism of novel Cu-Be alloy

Environmental-cost constraints, performance-composition imbalances, and medium-temperature stress relaxation failure significantly hinder the development and application of Cu-Be alloys. In this study, a novel low-cost, high-performance Cu-1.47Be-0.62Ni-0.1Mg alloy was designed using a data-augmented machine learning approach. Subsequently, the synergistic effects of Ni and Mg elements on the microstructure and properties of Cu-Be alloys were investigated, and the unique performance-enhancement mechanism was revealed. Ni and Mg synergistically enhance precipitation strengthening via cooperative regulation of the solid solubility of Be in Cu. Meanwhile, Ni suppresses grain boundary segregation of Be via the formation of the thermally stable NiBe phases, while Mg occupies grain-boundary vacancies and reduces the diffusion rate of Be. This synergy promotes the transition of precipitation behavior from grain-boundary-dominated discontinuous precipitation to intragranular-dominated continuous precipitation. The transition promotes the proliferation of nanoscale NiBe phases and inhibits the formation of coarse-grain-boundary phases, thereby effectively hindering dislocation motion, preventing recrystallization, and significantly improving stress relaxation resistance. Compared to the commercial C17200 alloy, the Cu-1.47Be-0.62Ni-0.1Mg alloy exhibits comparable tensile strength (1350 MPa), 26% higher electrical conductivity (29.2% IACS) and 53% better stress relaxation resistance (8.5% after 200 °C/20 h), achieving an 18% reduction in raw cost. This work proposes a novel multicomponent microalloying design strategy based on the quaternary synergistic mechanism of “elemental solubility optimization + second-phase directed precipitation + interfacial solute segregation + grain-boundary vacancy depletion”, providing novel insights for the design of other high-performance alloys for extreme service conditions.