Effect of Particle Spreading Dynamics on Powder Bed Quality in Metal Additive Manufacturing

Yousub Lee; A. Kate Gurnon; David Bodner; Srdjan Simunovic

doi:10.1007/s40192-020-00193-1

Effect of Particle Spreading Dynamics on Powder Bed Quality in Metal Additive Manufacturing

Yousub Lee, A. Kate Gurnon, David Bodner, Srdjan Simunovic

Multiscale Materials

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

41 Scopus citations

Abstract

Powder spreading precedes creation of every new layer in powder bed additive manufacturing (AM). The powder spreading process can lead to powder layer defects such as porosity, poor surface roughness and particle segregation. Therefore, the creation of homogeneous layers is the first task for optimal part printing. Discrete element methods (DEM) powder spreading simulations are typically limited to a single layer and/or small number of particles. Therefore, results from such model configurations may not be generalized to multiple layer processes. In this study, a computationally efficient multi-layer powder spreading DEM simulation model is proposed. The model is calibrated experimentally using static Angle of Repose measurements. The adhesion model parameter, cohesive energy density is related to adhesive surface energy and strain energy release rate parameters. The model results show that interaction between particle and the powder spreading rake leads to noticeable variation in packing density, surface roughness, dynamic angle of repose (AOR), particle size distribution, and particle segregation. The powder model is experimentally validated using a recoater spreading rig to measure the dynamic AOR at spreading speeds consistent with recoating speeds and layer heights used in AM processes.

Original language	English
Pages (from-to)	410-422
Number of pages	13
Journal	Integrating Materials and Manufacturing Innovation
Volume	9
Issue number	4
DOIs	https://doi.org/10.1007/s40192-020-00193-1
State	Published - Dec 2020

Funding

This research is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This research was supported by the High-Performance Computing for Manufacturing Project Program (HPC4Mfg), managed by the U.S. Department of Energy Advanced Manufacturing Office within the Energy Efficiency and Renewable Energy Office. This research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory. The authors thank Dr. John Turner for support in preparation of this manuscript. This research was supported by the High-Performance Computing for Manufacturing Project Program (HPC4Mfg), managed by the U.S. Department of Energy Advanced Manufacturing Office within the Energy Efficiency and Renewable Energy Office. This research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory. The authors thank Dr. John Turner for support in preparation of this manuscript.

Funders	Funder number
CADES
Data Environment for Science
High-Performance Computing for Manufacturing Project Program
U.S. Department of Energy Advanced Manufacturing Office
U.S. Department of Energy	DE-AC05-00OR22725
Office of Science
Oak Ridge National Laboratory

Keywords

Cohesive energy density
Discrete element methods (DEM)
Multi-layer deposition
Powder bed additive manufacturing
Powder bed quality
Powder spreading

UN SDGs

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

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10.1007/s40192-020-00193-1

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title = "Effect of Particle Spreading Dynamics on Powder Bed Quality in Metal Additive Manufacturing",

abstract = "Powder spreading precedes creation of every new layer in powder bed additive manufacturing (AM). The powder spreading process can lead to powder layer defects such as porosity, poor surface roughness and particle segregation. Therefore, the creation of homogeneous layers is the first task for optimal part printing. Discrete element methods (DEM) powder spreading simulations are typically limited to a single layer and/or small number of particles. Therefore, results from such model configurations may not be generalized to multiple layer processes. In this study, a computationally efficient multi-layer powder spreading DEM simulation model is proposed. The model is calibrated experimentally using static Angle of Repose measurements. The adhesion model parameter, cohesive energy density is related to adhesive surface energy and strain energy release rate parameters. The model results show that interaction between particle and the powder spreading rake leads to noticeable variation in packing density, surface roughness, dynamic angle of repose (AOR), particle size distribution, and particle segregation. The powder model is experimentally validated using a recoater spreading rig to measure the dynamic AOR at spreading speeds consistent with recoating speeds and layer heights used in AM processes.",

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author = "Yousub Lee and Gurnon, {A. Kate} and David Bodner and Srdjan Simunovic",

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AU - Gurnon, A. Kate

AU - Bodner, David

AU - Simunovic, Srdjan

N1 - Publisher Copyright: © 2020, This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection.

PY - 2020/12

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N2 - Powder spreading precedes creation of every new layer in powder bed additive manufacturing (AM). The powder spreading process can lead to powder layer defects such as porosity, poor surface roughness and particle segregation. Therefore, the creation of homogeneous layers is the first task for optimal part printing. Discrete element methods (DEM) powder spreading simulations are typically limited to a single layer and/or small number of particles. Therefore, results from such model configurations may not be generalized to multiple layer processes. In this study, a computationally efficient multi-layer powder spreading DEM simulation model is proposed. The model is calibrated experimentally using static Angle of Repose measurements. The adhesion model parameter, cohesive energy density is related to adhesive surface energy and strain energy release rate parameters. The model results show that interaction between particle and the powder spreading rake leads to noticeable variation in packing density, surface roughness, dynamic angle of repose (AOR), particle size distribution, and particle segregation. The powder model is experimentally validated using a recoater spreading rig to measure the dynamic AOR at spreading speeds consistent with recoating speeds and layer heights used in AM processes.

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Effect of Particle Spreading Dynamics on Powder Bed Quality in Metal Additive Manufacturing

Abstract

Funding

Keywords

UN SDGs

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