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Multi-Scale Power Optimization for Powder Bed Laser Fusion Additive Manufacturing

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Powder-Bed Laser Fusion (PBLF) processing typically involves using a set of nominal machine parameters that are applied to different regions of the part (e.g. skin, upskin, downskin, core) based on simple geometric heuristics like overhang angle or distance from the surface of the geometry. However, using fixed core processing parameters in complex geometries can result into non-uniform melt pool sizes at different regions of the part and thus non-uniform material properties. For example, a geometry that starts off building thin features that then thicken as the build progresses, may achieve good melt quality on the thin features but then experience overheating and/or keyholing as the build transitions into the thicker regions. This is due to the evolution of the heat conduction path combined with the increase in energy input into the thicker regions. This non-uniform heating creates a serious challenge for certifying part properties and quality. In this work, a multi-scale approach is proposed to optimize the processing power such that near-uniform melt pool size is achieved over the part. The approach combines an analytic lumped-parameter-based model that enforces the energy balance of the melt-pool with a part-scale finite element heat transfer analysis for full builds. The approach can optimize power on a point, vector, layer group, or part region basis. Experimental results of in-situ inter-layer temperatures taken by thermal camera as well as melt-pool sizes determined by cross-sectioning parts are presented. A baseline experimental case using nominal machine parameters is compared to an experimental case using optimized laser power.

Learning Objectives:

  • Describe the benefits, assumptions, and limitations of multi-scale laser powder or scan speed
  • Define the difference between point, vector, layer group, and part region based parameter optimization.