Prediction of gallium crystal orientation using machine learning

Abstract

Electron Back Scattered Diffraction Pattern (EBSD) is commonly used to evaluate the crys-(breakpoint)tallinity of metals. However, the equipment used for EBSD measurements is expensive, and only a limited number of institutions own such equipment, making the measurement time-consuming and costly. There-(breakpoint)fore, we have been investigating a simpler and quicker method to evaluate the crystallinity of metals. We used machine learning to correlate the solidification pattern of liquid gallium with the Euler angle mea-(breakpoint)sured by EBSD to predict the crystallographic orientation of gallium.