Data for: A Denoising Autoencoder for Improved Kikuchi Pattern Quality and Indexing in Electron Backscattered Diffraction

Published: 22 November 2022| Version 1 | DOI: 10.17632/jf4jwfm249.1
Contributor:
Caleb Andrews

Description

ANG datasets, txt grain files, and MATLAB workspaces used in referenced paper

Files

Steps to reproduce

Data is sample dependent, meaning the exact results could only be replicated with the same sampleset of struts/meltpools. The same methodology could be applied to any material if the user has an SEM, an EBSD camera, and polishing equipment. However, the analysis can be replicated to reproduce the findings in the paper by loading the data into MATLAB and using MTEX and OpenXY using the workflow described in the paper.

Institutions

Johns Hopkins University, Lawrence Livermore National Laboratory

Categories

Materials Science, Machine Learning, Electron Backscatter Diffraction

Funding

Lawrence Livermore National Laboratory

DE-AC52-07NA27344

Licence