Grain structure and texture selection regimes in metal powder bed fusion

Matt Rolchigo; John Coleman; Gerry L. Knapp; Alex Plotkowski

doi:10.1016/j.addma.2024.104024

Grain structure and texture selection regimes in metal powder bed fusion

Matt Rolchigo, John Coleman, Gerry L. Knapp, Alex Plotkowski

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

Additive manufacturing (AM) offers opportunities to produce complex part geometries not possible with conventional processing and in some cases even improve part performance. However, adoption has been slowed by difficulties assessing microstructure variability and there is no straightforward approach to relate processing to grain structure characteristics. In this study, datasets from AdditiveFOAM heat transport simulations of laser powder bed fusion (LPBF) are used to drive ExaCA simulations of grain structure. The GPU utilization of ExaCA and an algorithmic update for modeling melt pool overlap region solidification enabled rapid and parallel simulation across a wider range of process conditions than previously explored with cellular automata-based solidification models. A texture selection angle θ_s is defined based on melt pool overlap geometry, and the range of θ_s over which a commonly observed texture transition occurs in characterized AM builds was well-reproduced by ExaCA simulations over a wide range of melt pool shape, hatch spacing, and layer height. ExaCA simulations with 90 degree rotation of the scan direction on every other layer reproduced a number of trends from the AM literature including grain refinement, the dominance of layers with larger melt pools on the final grain structure, and the weakening or strengthening of texture depending on odd and even layer melt pool overlap geometry. EBSD data from a benchmark AM part is used to validate the simulated mechanism of a layer rotation-induced texture strengthening effect. These results expand the understanding of the mechanisms for texture selection in alloys with cubic crystal symmetry and offer an approach to easily evaluate processing conditions. With this new understanding, these modeling tools will enable anticipation of previously unexpected variations in grain structure and target specific microstructures and properties.

Original language	English
Article number	104024
Journal	Additive Manufacturing
Volume	81
DOIs	https://doi.org/10.1016/j.addma.2024.104024
State	Published - Feb 5 2024

Funding

We would like to thank Sam Reeve for his help with code development and Adam Creuziger for his help using the MTEX software, along with the entire ExaAM project team for useful discussions on code optimization and microstructure development mechanisms. Research was performed at the U.S. Department of Energy's Manufacturing Demonstration Facility, located at Oak Ridge National Laboratory. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. Research was co-sponsored by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy, Advanced Manufacturing Office. This research was also supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. DOE Office of Science and the NNSA. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). AdditiveFOAM and ExaCA are open source software and available for use at https://github.com/ORNL/AdditiveFOAM and https://github.com/LLNL/ExaCA, respectively. Correspondence regarding AdditiveFOAM should be addressed to John Coleman, and correspondence regarding the manuscript, manuscript data, or ExaCA should be addressed to Matt Rolchigo. We would like to thank Sam Reeve for his help with code development and Adam Creuziger for his help using the MTEX software, along with the entire ExaAM project team for useful discussions on code optimization and microstructure development mechanisms. Research was performed at the U.S. Department of Energy’s Manufacturing Demonstration Facility, located at Oak Ridge National Laboratory. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy . Research was co-sponsored by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy, Advanced Manufacturing Office . This research was also supported by the Exascale Computing Project ( 17-SC-20-SC ), a collaborative effort of the U.S. DOE Office of Science and the NNSA . The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).

Funders	Funder number
DOE Public Access Plan
United States Government
U.S. Department of Energy
Advanced Manufacturing Office	17-SC-20-SC
Office of Energy Efficiency and Renewable Energy
National Nuclear Security Administration
Oak Ridge National Laboratory	DE-AC05-00OR22725

Keywords

Cellular automata
Grain structure
High performance computing
Texture

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.addma.2024.104024

Cite this

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abstract = "Additive manufacturing (AM) offers opportunities to produce complex part geometries not possible with conventional processing and in some cases even improve part performance. However, adoption has been slowed by difficulties assessing microstructure variability and there is no straightforward approach to relate processing to grain structure characteristics. In this study, datasets from AdditiveFOAM heat transport simulations of laser powder bed fusion (LPBF) are used to drive ExaCA simulations of grain structure. The GPU utilization of ExaCA and an algorithmic update for modeling melt pool overlap region solidification enabled rapid and parallel simulation across a wider range of process conditions than previously explored with cellular automata-based solidification models. A texture selection angle θs is defined based on melt pool overlap geometry, and the range of θs over which a commonly observed texture transition occurs in characterized AM builds was well-reproduced by ExaCA simulations over a wide range of melt pool shape, hatch spacing, and layer height. ExaCA simulations with 90 degree rotation of the scan direction on every other layer reproduced a number of trends from the AM literature including grain refinement, the dominance of layers with larger melt pools on the final grain structure, and the weakening or strengthening of texture depending on odd and even layer melt pool overlap geometry. EBSD data from a benchmark AM part is used to validate the simulated mechanism of a layer rotation-induced texture strengthening effect. These results expand the understanding of the mechanisms for texture selection in alloys with cubic crystal symmetry and offer an approach to easily evaluate processing conditions. With this new understanding, these modeling tools will enable anticipation of previously unexpected variations in grain structure and target specific microstructures and properties.",

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Grain structure and texture selection regimes in metal powder bed fusion

Abstract

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Keywords

UN SDGs

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