Computational Design of Ductile Additively Manufactured Tungsten-Based Refractory Alloys

Abstract

Tungsten exhibits exceptional temperature and radiation resistance, making it well-suited for applications in extreme environments such as nuclear fusion reactors. Additive manufacturing offers geometrical design freedom and rapid prototyping capabilities for these applications, provided the intrinsic brittleness and low printability of tungsten can be overcome. Designing tungsten alloys with improved ductility, and thus printability in additive manufacturing, can be accelerated using a computationally derived performance predictor to screen out brittle compositions. Calculations of the Pugh ratio using density functional theory may serve this purpose, given its correlation with ductility. This process can be made more efficient through the use of machine learning interatomic potentials to accelerate density functional theory calculations. Here, we demonstrate that machine learning interatomic potentials can effectively identify optimal alloy compositions in the W-Ta-Nb system along the melting point-Pugh ratio Pareto front. The trend in Pugh ratio as a function of tungsten fraction is explained in terms of the electronic density of states at the Fermi level. Experimental validation reveals a strong correlation between the computed Pugh ratio and the observed crack fractions in additively manufactured alloys. Notably, the two alloys predicted to have the highest Pugh ratio values, W20Ta70Nb10 and W30Ta60Nb10, exhibit no intergranular microcracking in experiments.

Figures (13)

• Figure 1: Pipeline for using machine learning interatomic potentials (MLIPs) to screen the ternary alloy space, obtain a more accurate trend using DFT, and use that to design experiments of ductile high-temperature W-Ta-Nb alloys.
• Figure 2: SEM/EDS images of blended powder with nominal atomic composition W50-Ta40-Nb10
• Figure 3: a) Schematic of DED nozzle operation during deposition of 3D walls, b) examples of cross-sectioned walls demonstrating the range of geometries and surface finishes produced by DED.
• Figure 4: (Left to right, top to bottom) SEM image of microcracking in tungsten alloy, followed by manual binarization, skeletonization, and labeling
• Figure 5: a) contour plots of the Pugh ratio calculated using the UMA machine learning potential. b) melting point temperatures calculated using Vegard's law. c) Pareto front of melting point temperature and Pugh ratio as a measure of ductility. A melting point of 3300 K was selected as a lower limit.