ExaCA grain structure predictions for laser powder bed fusion processing

Matthew Rolchigo | Oak Ridge National Laboratory

John Coleman | Oak Ridge National Laboratory

Description

This dataset contains simulated cross-sections of laser powder bed fusion grain structures produced using the microstructure model ExaCA, in turn using time-temperature history data produced using the heat transport model AdditiveFOAM. These predictions show the grain structure using various permutations of hatch spacing and nucleation density. Also contained in the dataset are the input files necessary to reproduce the results. AdditiveFOAM (https://github.com/ORNL/AdditiveFOAM) and ExaCA (https://github.com/LLNL/ExaCA), both open source software, are required to reproduce the results in this dataset using the given input files. This DOI was updated 12/09/2025.

Funding Information

DOE Contract Number

DE-AC05-00OR22725

Originating Research Organization

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization

Office of Energy Efficiency and Renewable Energy (EERE); Advanced Materials and Manufacturing Technologies Office (AMMTO)

Related Works

References (URL): https://github.com/ORNL/AdditiveFOAM
References (URL): https://github.com/LLNL/ExaCA

Details

DOI

https://doi.org/10.13139/OLCF/2428925

Release Date

August 27, 2024

Subject

36 MATERIALS SCIENCE

Keywords

Microstructure, Additive Manufacturing, Cellular automata

Dataset

Dataset Type

AS Animations/Simulations

Software

AdditiveFOAM and ExaCA open source software are both required to reproduce the results in this dataset using the given input files.

Cite This Dataset:

Rolchigo, M., Coleman, J. (2024). ExaCA grain structure predictions for laser powder bed fusion processing. Oak Ridge National Laboratory. https://doi.org/10.13139/OLCF/2428925.

Acknowledgements

This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Advanced Scientific Computing Research programs in the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.