Lawrence Livermore National Laboratory



This model is used to obtain understanding of how the particle-scale feedstock-powder properties, and the powder-spreading process parameters, affect uniformity and smoothness of the pre-heating-state powder-bed, especially the spatial distribution of particle-sizes, bed porosity and thickness – all of which are inputs to the downstream powder-scale model of the heating, melting and part-formation processes. This meso-scale model bridges a scale range from particle-surface morphology effects (cohesion, friction, deformation, rolling resistance, etc., which are approximated by mathematical models of inter-particle contact interactions) to bulk-powder deformation and flow behavior on a centimeter scale, but simulated at the individual particle scale. The powder-dynamics model can simulate the effects of particle-size and shape distributions and cohesion, on phenomena such as size-segregation during shearing flow. One square centimeter of a single build layer in an SLM process can involve from hundreds of thousands to over a million feedstock powder particles, depending on system and powder parameters so scaling efficiency on parallel processors is also a concern. Model development is focusing on evaluating and improving particle interaction-model fidelity, particle-shape representations, and uncertainty quantification. Once validated these higher-fidelity models could also be migrated to DEM codes other than those used for the initial evaluation (e.g., DOE-supported open-source codes with DEM capabilities such as LAMMPS or MFIX). Among the anticipated future improvements will be inclusion of the effects of interstitial gas (ambient air, or controlled atmosphere) on the spreading behavior.

Approach

Modeling approach:

  • Discrete Element Method (DEM) using 1) the relatively new DEM module in the LLNL-developed Geodyn-L code; or 2) the recently-revised DEM module in the commercial code Star-CCM (CD-Adapco)
  • Tightly cooupled with experiment

Contact: Otis Walton

Click for Accelerating Characterization


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