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  4. An effective rule for translating optimal selective laser melting processing parameters from one material to another
 
research article

An effective rule for translating optimal selective laser melting processing parameters from one material to another

Ghasemi Tabasi, Hossein  
•
Jhabvala, Jamasp  
•
Boillat, Eric  
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August 5, 2020
Additive Manufacturing

Selective Laser Melting (SLM) or Laser Powder Bed Fusion (L-PBF) is the most studied laser-based additive manufacturing process for metals and alloys. One important issue in SLM is the time-consuming identification of a process window leading to quasi fully dense parts (> 99.8%), usually based on trials and errors. As some metal powders may be very expensive, and therefore not suitable for a whole battery of tests, a method to infer optimal parameters from one material to another would be highly beneficial. In this study, we use bronze as a test material for optimizing SLM parameters, before translating these parameters to red gold and 316 L steel. The concept of normalized enthalpy is used to take into account the differences in thermal and optical properties among the different materials. A translation rule is derived for the prediction of optimal processing conditions, based on the ones found for the test material. One important input for this translation rule is the powder absorptivity, which is measured at the appropriate laser wavelength and at room temperature. This approach eventually leads to the highest reported density for an additive manufactured 18-carat gold alloy (99.81% relative density), to the authors' knowledge. Finite element (FEM) simulations justify the translation rule formulation by showing the importance of the laser/powder interactions during the SLM process, leading to a finite penetration depth of the laser in the powder bed due to multiple reflections. The FEM calculations indicate that a significant part of the laser energy is directly absorbed by the powder during the manufacturing process when operating in near-optimal conditions.

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