Computationally guided composition optimization of Ni50–xFe25Co25Cux for additive manufacturing

Abstract

Additive manufacturing has emerged as a prominent fabrication technology but is hindered by the limited portfolio of printable alloys. Herein, a combination of molecular dynamics simulations, thermodynamic modeling, and high-throughput experiments is used to address this limitation by screening Ni_50–xFe₂₅Co₂₅Cu_x high-entropy alloys for promising candidate materials. The thermodynamic simulations indicate that the printability can be enhanced by increasing the Ni content at the expense of Cu by narrowing the solidification temperature range and suppressing the formation of additional phases, while the atomistic simulations show that the composition is prone to forming Cu-rich atomic-clusters. Based on these insights, crack-free samples were printed using high-throughput laser powder bed fusion. A trend of increasing yield strength with a higher Cu content was observed, which could not be explained by the microstructural features. Instead, atomistic simulations suggest that the trend is due to the formation of Fe–Cu clusters forming at the grain boundary, which increases the resistance to dislocation slip. The findings present valuable design guidelines for developing computational frameworks to design printable alloys, while highlighting the intertwined nature of chemical composition, atomic ordering, and stacking fault energy.