Abstract
Mapping grain orientation in crystalline solids is essential to investigate the relationships between local microstructure and crystallography and interpret materials properties. One of the main techniques used to perform these studies is electron backscatter diffraction (EBSD). Due to the limited measurement throughput, however, EBSD is not suitable for characterizing samples with long-range microstructure heterogeneity, nor for building large material libraries that include numerous specimens. We present a machine learning approach for high-throughput crystal orientation mapping, which relies on the optical technique called directional reflectance microscopy. We successfully apply our method on Inconel 718 specimens produced by additive manufacturing, which exhibit complex, spatially-varying microstructures. These results demonstrate that optical orientation mapping on a metal alloy is achievable. Since our method is data-driven, it can be easily extended to different alloy systems produced using different manufacturing processes.
Highlights
Characterizing the microstructure of polycrystalline solids—including the size, morphology, and crystallographic orientation of the constituent crystal grains—is essential for understanding the relationships between process history, microstructure, and materials properties
electron backscatter diffraction (EBSD) is inefficient at building materials libraries that are based on numerous specimens produced using different process parameters[4], or at characterizing samples that exhibit large-scale microstructure heterogeneity
We describe our machine learning workflow and demonstrate its effectiveness on Inconel 718 (I718) specimens produced by directed energy deposition (DED)
Summary
Characterizing the microstructure of polycrystalline solids—including the size, morphology, and crystallographic orientation of the constituent crystal grains—is essential for understanding the relationships between process history, microstructure, and materials properties. Once captured by DRM, directional reflectance data is analyzed by computational methods to enable spatial mapping of grain orientation[10,11].
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