This paper demonstrates the potential for qualification through local part property prediction of 304L stainless steel parts manufactured by Selective Laser Melting (SLM). This is accomplished through voxel based processing of SWIR imaging data measured in-situ. Thermal features are extracted from time-series SWIR imaging data recorded from layer-to-layer to generate 3D point cloud reconstructions of parts. The voxel based data is indexed with localized measurements of SLM part properties (light-to-dark microstructural feature ratio, microhardness, μCT data) to demonstrate the correlations. Various features are extracted from the thermal history for comparison of their respective abilities to predict the resulting local part properties. The correlations and comparisons developed in this paper are then used to discuss the capability of a voxel based framework using information from in-situ measurements of the thermal history to locally qualify parts manufactured by SLM.

Meeting Name

30th Annual International Solid Freeform Fabrication Symposium -- An Additive Manufacturing Conference, SFF 2019 (2019: Aug. 12-14, Austin, TX)


Mechanical and Aerospace Engineering

Research Center/Lab(s)

Intelligent Systems Center

Second Research Center/Lab

Center for Research in Energy and Environment (CREE)


This work was funded by the Department of Energy’s Kansas City National Security Campus which is operated and managed by Honeywell Federal Manufacturing Technologies, LLC under contract number DE-NA0002839.

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type




Publication Date

14 Aug 2019

Included in

Manufacturing Commons