A machine learning approach to map crystal orientation by optical microscopy

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.

Automated summary

Mapping grain orientation in crystalline solids is crucial for studying local microstructure and crystallography as well as interpreting materials properties. We propose a machine learning approach that utilizes optical technique for high-throughput crystal orientation mapping, and successfully apply it to metal alloy specimens with complex microstructures.