Application of a Microstructural Characterization Uncertainty Quantification Framework to Widmanstätten ⍺-laths in Additive Manufactured Ti-6Al-4V

Loughnane, Gregory T.
Kuntz, Sarah L.
Klingbeil, Nathan
Sosa, John M.
Irwin, Jeff
Nassar, Abdalla R.
Reutzel, Edward W.
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University of Texas at Austin

This work applies statistical analysis and uncertainty quantification tools developed for characterizing virtual microstructures in three dimensions to a two-dimensional experimental investigation of Ti-6Al-4V Widmanstätten ⍺-lath thicknesses obtained from back-scattered electron (BSE) or electron back-scatter diffraction (EBSD) images on two thin-walled samples manufactured via the LENS® process. The Materials Image Processing and Automated Reconstruction (MIPAR™) software optimizes unique recipes for conversion of the BSE or EBSD images to binary data, and subsequently computes the inverse of the linear intercept for each ⍺-lath. Mean ⍺-lath thicknesses and discrete probability density functions (PDFs) of inverse intercepts are used to make quantitative comparisons of ⍺-lath structures at different heights throughout the thin walls. Real-time thermal data collected during the LENS® experiment is then compared to quantitative microstructural results in order to determine trends between ⍺-lath structures, thermal gradients, and melt pool areas across experimental process parameters.