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ISSN 2753-3239
CCC: 3
PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY
Edited by: B.H.V. Topping and J. Kruis
Paper 17.2

Discrete Element Method framework to simulate metallic Laser Powder-Bed Fusion additive manufacturing process

W. Leclerc1 and A. Chams2

1Laboratoire des Technologies Innovantes UR-UPJV 3899, Université de Picardie Jules Verne, France
2Department of Mechanical Engineering, University of Ottawa, Canada

Full Bibliographic Reference for this paper
W. Leclerc, A. Chams, "Discrete Element Method framework to simulate metallic Laser Powder-Bed Fusion additive manufacturing process", in B.H.V. Topping, J. Kruis, (Editors), "Proceedings of the Fourteenth International Conference on Computational Structures Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 3, Paper 17.2, 2022, doi:10.4203/ccc.3.17.2
Keywords: L-PBF process, 3D printing, numerical modelling, discrete element method, Gusarov model, temperature fields.

Abstract
The present work introduces a Discrete Element Method (DEM) framework to simulate metallic Laser Powder-Bed Fusion (L-PBF) additive manufacturing process. This latter is expected to take into account the main steps of additive manufacturing from the 3D printing G-code to the characterization of printed parts before postprocessing through the simulation of laser/powder bed thermo-mechanical interaction and the determination of residual stresses and distortions. In this paper, a 3-step investigation is led to validate and exploit the developed DEM-based methodology. For validation purposes, we first consider a reference problem to compare melt pool geometrical and thermal characteristics given by Gusarov radiation model with finite element results coming from the literature. Then, we simulate the 3D printing of a simple geometric part. Finally, we exploit the developed approach to determine the influence of laser parameters in this case and more complex configurations. Results highlight the ability of DEM to reproduce L-PBF process and provide crucial information as temperature fields for optimisation purposes.

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