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A machine learning approach to map crystal orientation by optical microscopy

Engineering and Technology

A machine learning approach to map crystal orientation by optical microscopy

M. Wittwer and M. Seita

Discover a groundbreaking machine learning technique for high-throughput crystal orientation mapping in metal alloys, pioneered by Mallory Wittwer and Matteo Seita. This innovative approach, demonstrated on Inconel 718 specimens from additive manufacturing, leverages directional reflectance microscopy, enabling easier mapping and extending to various alloys and manufacturing processes.

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Playback language: English
Abstract
Mapping grain orientation in crystalline solids is crucial for understanding the link between microstructure and material properties. Electron backscatter diffraction (EBSD) is a standard technique, but its low throughput limits its application to large samples or material libraries. This paper introduces a machine learning approach for high-throughput crystal orientation mapping using directional reflectance microscopy (DRM). The method, successfully applied to Inconel 718 specimens from additive manufacturing, demonstrates the feasibility of optical orientation mapping in metal alloys. Its data-driven nature allows easy extension to other alloys and manufacturing processes.
Publisher
npj Computational Materials
Published On
Jan 19, 2022
Authors
Mallory Wittwer, Matteo Seita
Tags
crystal orientation
machine learning
high-throughput
directional reflectance microscopy
metal alloys
Inconel 718
additive manufacturing

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