A stochastic scan strategy for grain structure control in complex geometries using electron beam powder bed fusion

A. Plotkowski; J. Ferguson; B. Stump; W. Halsey; V. Paquit; C. Joslin; S. S. Babu; A. Marquez Rossy; M. M. Kirka; R. R. Dehoff

doi:10.1016/j.addma.2021.102092

A stochastic scan strategy for grain structure control in complex geometries using electron beam powder bed fusion

A. Plotkowski, J. Ferguson, B. Stump, W. Halsey, V. Paquit, C. Joslin, S. S. Babu, A. Marquez Rossy, M. M. Kirka, R. R. Dehoff

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

53 Scopus citations

Abstract

Spatial control of microstructure within a three-dimensional component has been a dream of materials scientists for centuries. However, limitations in traditional manufacturing processes prevent detailed control over the distribution of microstructures in a single part. Here, we demonstrate the ability to control grain structure and crystallographic texture during metal additive manufacturing for arbitrary cross-sections of a practical size, with profound implications for the design and optimization of next-generation products. The key to this advance is a new geometry agnostic scan path algorithm that manipulates the spatial distribution of solidification conditions. Utilizing a fundamental understanding of solidification dynamics and a model of the heat transfer during processing, we have designed this algorithm to manipulate the natural competition between epitaxial dendrite growth and grain nucleation. With this algorithm, we successfully controlled the grain structure of Ni-based superalloy IN718 in the shape of the Mona Lisa.

Original language	English
Article number	102092
Journal	Additive Manufacturing
Volume	46
DOIs	https://doi.org/10.1016/j.addma.2021.102092
State	Published - Oct 2021

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05–00OR22725 with the U.S. Department of Energy. Research was sponsored the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Advanced Manufacturing Office, 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 (). This manuscript has been authored by UT-Battelle , LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. Research was sponsored the U.S. Department of Energy , Office of Energy Efficiency and Renewable Energy , Advanced Manufacturing Office , 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 (). This research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. We thank Bobby Sumpter, Chris Fancher, Xin Sun, and Sheng Dai for valuable feedback on the manuscript, and Bill Peter and Craig Blue for research support. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. Research was sponsored the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Advanced Manufacturing Office, 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>). This research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

Funders	Funder number
CADES
DOE Public Access Plan
Data Environment for Science
United States Government
U.S. Department of Energy
Advanced Manufacturing Office
Office of Science
Office of Energy Efficiency and Renewable Energy

Keywords

Electron beam powder bed fusion
Grain structure
Metal additive manufacturing
Solidification

UN SDGs

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

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10.1016/j.addma.2021.102092

Cite this

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title = "A stochastic scan strategy for grain structure control in complex geometries using electron beam powder bed fusion",

abstract = "Spatial control of microstructure within a three-dimensional component has been a dream of materials scientists for centuries. However, limitations in traditional manufacturing processes prevent detailed control over the distribution of microstructures in a single part. Here, we demonstrate the ability to control grain structure and crystallographic texture during metal additive manufacturing for arbitrary cross-sections of a practical size, with profound implications for the design and optimization of next-generation products. The key to this advance is a new geometry agnostic scan path algorithm that manipulates the spatial distribution of solidification conditions. Utilizing a fundamental understanding of solidification dynamics and a model of the heat transfer during processing, we have designed this algorithm to manipulate the natural competition between epitaxial dendrite growth and grain nucleation. With this algorithm, we successfully controlled the grain structure of Ni-based superalloy IN718 in the shape of the Mona Lisa.",

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AU - Plotkowski, A.

AU - Ferguson, J.

AU - Stump, B.

AU - Halsey, W.

AU - Paquit, V.

AU - Joslin, C.

AU - Babu, S. S.

AU - Marquez Rossy, A.

AU - Kirka, M. M.

AU - Dehoff, R. R.

PY - 2021/10

Y1 - 2021/10

N2 - Spatial control of microstructure within a three-dimensional component has been a dream of materials scientists for centuries. However, limitations in traditional manufacturing processes prevent detailed control over the distribution of microstructures in a single part. Here, we demonstrate the ability to control grain structure and crystallographic texture during metal additive manufacturing for arbitrary cross-sections of a practical size, with profound implications for the design and optimization of next-generation products. The key to this advance is a new geometry agnostic scan path algorithm that manipulates the spatial distribution of solidification conditions. Utilizing a fundamental understanding of solidification dynamics and a model of the heat transfer during processing, we have designed this algorithm to manipulate the natural competition between epitaxial dendrite growth and grain nucleation. With this algorithm, we successfully controlled the grain structure of Ni-based superalloy IN718 in the shape of the Mona Lisa.

AB - Spatial control of microstructure within a three-dimensional component has been a dream of materials scientists for centuries. However, limitations in traditional manufacturing processes prevent detailed control over the distribution of microstructures in a single part. Here, we demonstrate the ability to control grain structure and crystallographic texture during metal additive manufacturing for arbitrary cross-sections of a practical size, with profound implications for the design and optimization of next-generation products. The key to this advance is a new geometry agnostic scan path algorithm that manipulates the spatial distribution of solidification conditions. Utilizing a fundamental understanding of solidification dynamics and a model of the heat transfer during processing, we have designed this algorithm to manipulate the natural competition between epitaxial dendrite growth and grain nucleation. With this algorithm, we successfully controlled the grain structure of Ni-based superalloy IN718 in the shape of the Mona Lisa.

KW - Electron beam powder bed fusion

KW - Grain structure

KW - Metal additive manufacturing

KW - Solidification

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A stochastic scan strategy for grain structure control in complex geometries using electron beam powder bed fusion

Abstract

Funding

Keywords

UN SDGs

Access to Document

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