Limits of dispersoid size and number density in oxide dispersion strengthened alloys fabricated with powder bed fusion-laser beam

Previous work on additively-manufactured oxide dispersion strengthened alloys focused on experimental approaches, resulting in larger dispersoid sizes and lower number densities than can be achieved with conventional powder metallurgy. To improve the as-fabricated microstructure, this work integrates experiments with a thermodynamic and kinetic modeling framework to probe the limits of the dispersoid sizes and number densities that can be achieved with powder bed fusion-laser beam. Bulk samples of a Ni-20Cr $+$ 1 wt.\% Y$_2$O$_3$ alloy are fabricated using a range of laser power and scanning velocity combinations. Scanning transmission electron microscopy characterization is performed to quantify the dispersoid size distributions across the processing space. The smallest mean dispersoid diameter (29 nm) is observed at 300 W and 1200 mm/s, with a number density of 1.0$\times$10$^{20}$ m$^{-3}$. The largest mean diameter (72 nm) is observed at 200 W and 200 mm/s, with a number density of 1.5$\times$10$^{19}$ m$^{-3}$. Scanning electron microscopy suggests that a considerable fraction of the oxide added to the feedstock is lost during processing, due to oxide agglomeration and the ejection of oxide-rich spatter from the melt pool. After accounting for these losses, the model predictions for the dispersoid diameter and number density align with the experimental trends. The results suggest that the mechanism that limits the final number density is collision coarsening of dispersoids in the melt pool. The modeling framework is leveraged to propose processing strategies to limit dispersoid size and increase number density.Comment: Main text: 36 pages, 12 figure

Toward Enabling Spatial Control of Ti-6Al-4V Solidification Microstructure in the Electron Beam Melting Process

In this work, relationships between prior beta grain size in solidified Ti-6Al-4V and melting process parameters in the Arcam Electron Beam Melting (EBM) process are investigated. Toward this goal, samples are built on an Arcam S12 machine at Carnegie Mellon University by specifically varying the Arcam proprietary speed function and beam current over process space for a variety of test specimens. Optical microscopy is used to measure the prior beta grain widths and assess the number of prior beta grains present in a melt pool in the raster region of the build. Results demonstrate that the number of grains across the width of a bead is constant for a fixed deposition geometry. The resulting understanding of the relationship between primary machine variables and prior beta grain widths is a key step toward understanding and enabling the spatial control of as-built microstructure in the EBM process.Mechanical Engineerin

Accelerating High-Fidelity Thermal Process Simulation of Laser Powder Bed Fusion via the Computational Fluid Dynamics Imposed Finite Element Method (CIFEM)

The current work proposes a finite element method (FEM) to accelerate scanwise thermal process simulation of the laser powder bed fusion (L-PBF) process with computational fluid dynamics (CFD) resolution near the melt pool. Termed the CFD imposed FEM (CIFEM), the transient thermal fields from a high-fidelity CFD simulation and inferred by deep learning are imposed as temperature values rather than utilizing a conventional heat source model as in existing FEM-based process simulations. These fields are enforced only within a relatively small computational region encompassing the melt pool, while heat diffusion effects elsewhere are solved via the FEM. For a wide range of laser power and scan speeds covering the conduction, transition, and keyhole melting regimes, 29 of the 30 total CIFEM-simulated melt pool sizes lie within two standard deviations of the experimental melt pool sizes. Compared with the CFD simulations, the thermal fields obtained by CIFEM possess 7.44% mean absolute relative error (MARE), significantly less than the 43.76% MARE on three representative test cases simulated using the Goldak heat source model calibrated to the measured melt pool dimensions. In terms of computational efficiency, the CIFEM model running on a GPU card with 4,608 Compute Unified Device Architecture (CUDA) cores is 28.2× more efficient than the CFD simulations running on 24 CPU cores in parallel