Abstract
Laser-wire metal additive manufacturing (AM) is one of the ideal direct energy deposition (DED) processes for creating large-scale parts with a medium level of complexity. However, the DED process involves complex thermal signatures and wide length scales making the fabrication of realistic AM components and part qualification often reliant on experimental trial-and-error optimization. While experimental measurements over the full volume of a part are valuable and necessary, measuring the entire area of a part is significantly laborious and practically infeasible, particularly for large parts in terms of cost and rapid qualification. Therefore, in this work, we developed an effective thermal and microstructure modeling framework based on the Johnson–Mehl-Avrami-Kolmogorov (JMAK) and Koistinen & Marburger (KM) models through a top-down approach that considers plate distortion-affected thermal profiles. A voxel-by-voxel simulation method is used to predict individual phase fractions of Ti-6Al-4 V. The predicted results were validated through detailed metallurgical measurements. A combined voxel-by-voxel approach with a sparse data reconstruction technique produced a near-perfect reconstruction of the original data. This approach anticipates a significant reduction in data points and computation time and resources. Lastly, we conclude with potential extensions of this work to other modeling efforts.
Original language | English |
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Article number | 113434 |
Journal | Materials and Design |
Volume | 247 |
DOIs | |
State | Published - Nov 2024 |
Funding
This work was supported by the US Department of Energy Advanced Materials and Manufacturing Technologies Office (AMMTO) within the Energy Efficiency and Renewable Energy Office under contract No. DE-AC05-00OR22725 with UT-Battelle LLC. The authors like to acknowledge the contribution of the extended Oak Ridge National Laboratory and GKN Aerospace teams, particularly those of Ms. Sara Graham and Dr. Lonnie Love of ORNL and W. Chad Henry and Chris Allison of GKN Aerospace .
Funders | Funder number |
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Oak Ridge National Laboratory | |
US Department of Energy Advanced Materials and Manufacturing Technologies Office | |
UT-Battelle | |
Advanced Materials and Manufacturing Technologies Office | |
Office of Energy Efficiency and Renewable Energy | DE-AC05-00OR22725 |
Office of Energy Efficiency and Renewable Energy |
Keywords
- Additive manufacturing
- Gaussian Process
- Microstructure
- Ti-6Al-4V
- Top-down process modeling
- Voxel-based microstructure modeling