Breaking hardness and electrical conductivity trade-off in Cu-Ti alloys through machine learning and Pareto front

Abstract

Balancing the hardness and electrical conductivity of copper alloys within complex compositions and processes poses a formidable challenge. This study proposes a strategy combining machine learning with the Pareto front techniques to identify optimal combinations of composition and processing for Cu-xTi (1.5 ≤ x ≤ 5.4, in wt.%) alloys. Through thermodynamic calculations, precipitation simulations, and experimental characterizations, the microstructural evolution of β'-Cu4Ti precipitates in the designed alloys was explored. The interpretability and predictability of the machine learning model played a crucial role in understanding impact of complex alloy compositions and processing on the evolution of properties, thereby guiding the design of Cu-Ti alloys towards improved attributes.