Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy

Authors

  • D. K. Suker Department of Mechnical Engineering, Umm Al-Qura University, Saudi Arabia

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 learning

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How to Cite

[1]
D. K. Suker, “Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 2, pp. 8393–8401, Apr. 2022.

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