Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy
Received: 5 February 2022 | Revised: 17 February 2022 | Accepted: 18 February 2022 | Online: 9 April 2022
Corresponding author: D. K. Suker
Abstract
Indexing of Electron Backscatter Diffraction (EBSD) is a well-established method of crystalline material characterization that provides phase and orientation information about the crystals on the material surface. A deep learning Convolutional Neural Network was trained to predict crystal orientation from the EBSD patterns based on the mean disorientation error between the predicted crystal orientation and the ground truth. The CNN is trained using EBSD images for different deformation conditions of AA5083.
Keywords:
AA5083, microstructure, EBSD, machine learning, deep learningDownloads
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