Machine-learning optimization of microstructure and mechanical properties in 3D-printed W-alloyed refractory high-entropy alloys

Abstract

Preparation of fine-grain, low-microsegregation Ti-rich refractory high-entropy alloys (RHEAs) remains challenging for conventional techniques. Additive manufacturing (AM)/3D printing can weaken the microsegregation but its strong directional heat flux is unfavorable to formation of fine equiaxed grains. Refining grains by alloying requires a lot of experiments to match appropriate AM parameters to these newly designed alloys, resulting in high cost and being time-consuming. Here, W was selected as a grain-refining element for a Ti-rich RHEA, and a solidification theory-guided machine-learning framework was developed to concurrently optimize W content and AM parameters. By integrating velocity-dependent solidification and heat-transfer calculations, the framework identified an optimized alloy, (Ti3.6Zr1.2V0.8NbAl0.3)93.7W6.3, with AM parameters of 1,556 W and 7.75 mm/s . Experimental validation showed that the designed alloy exhibited a fully fine-equiaxed microstructure with an average grain size of 49.6 μm, approximately 80% smaller than that of the W-free counterpart. The alloy tested along the L direction achieved a yield strength of 1,215 ± 15 MPa, an ultimate tensile strength of 1,241 ± 9 MPa, and an elongation of approximately 16%. These results demonstrate an efficient composition-process co-design strategy for AM fine-grained Ti-rich RHEAs.