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.
C. S. Lough et al., "In-Situ Local Part Qualification of SLM 304L Stainless Steel through Voxel based Processing of SWIR Imaging Data," Proceedings of the 30th Annual International Solid Freeform Fabrication Symposium (2019, Austin, TX), pp. 1611-1625, University of Texas at Austin, Aug 2019.
30th Annual International Solid Freeform Fabrication Symposium -- An Additive Manufacturing Conference, SFF 2019 (2019: Aug. 12-14, Austin, TX)
Mechanical and Aerospace Engineering
Intelligent Systems Center
Second Research Center/Lab
Center for Research in Energy and Environment (CREE)
Article - Conference proceedings
14 Aug 2019