Carnegie Mellon University
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Melt Pools, Process Parameters, and Defects in LPBF UNS N07718

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posted on 2022-06-06, 20:07 authored by Lonnie SmithLonnie Smith

In this work, mathematically derived relations from the Rosenthal equation are used to predict the melt pool dimensions of UNS N07718 samples created using the laser powder bed fusion (LPBF) process. Various laser powers (P) and scan speeds (V) were used to create the samples in this work; however, it was determined that the recommended standard conditions result in keyhole-shaped melt pools. The predicted dimensions are then compared to the measured values on plots where (P/V)1/2 is on the x-axis. Another mathematical relation is derived to predict the groove depth between samples with varying hatch spacing, where the melt pool is approximated by two half-ellipses with different minor axis values. Actual measurements of the groove depth are compared to the predictions on plots where the ratio of hatch spacing (H) to melt pool width (W) is on the x-axis. This work also utilized a previously derived geometrical relation to predict which of the fabricated samples were expected to have lack-of-fusion (LOF) porosity.

The other part of this work explores the formation of both incorporated and surface aluminum oxides. Incorporated and surface oxide concentration are plotted against certain variables to determine potential correlations. Incorporated oxide area fraction was also used as a means of determining whether the location where a part is built on the build plate affects the defect concentration. The idea of oxide flotation in a molten UNS N07718 matrix is tested by conducting analysis of the oxygen content at the top and bottom of tall samples. Finally, compositional analysis was done on samples made with different powders to determine whether elemental composition differences could explain the variance in hot cracking behavior seen.

History

Date

2021-08-25

Degree Type

  • Dissertation

Department

  • Materials Science and Engineering

Degree Name

  • Doctor of Philosophy (PhD)

Advisor(s)

Chris Pistorius

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