Convolutional neural network-based method for real-time orientation indexing of measured electron backscatter diffraction patterns. (15th May 2019)

• Record Type: Journal Article
• Authors: Shen, Yu-Feng; Pokharel, Reeju; Nizolek, Thomas J.; Kumar, Anil; Lookman, Turab
• Is Part Of: Acta materialia. Volume 170(2019)
• Page Start: 118
• Page End: 131
• Publication Date: 2019-05-15
• Subjects: Microstructure reconstruction -- Convolutional neural network -- Electron backscatter diffraction; Materials -- Periodicals; Materials science -- Periodicals; Materials -- Mechanical properties -- Periodicals; Metallurgy -- Periodicals; Chemistry, Inorganic -- Periodicals; 620.112
• DOI: 10.1016/j.actamat.2019.03.026
• Languages: English
• ISSNs: 1359-6454

Abstract

Abstract: Electron backscatter diffraction (EBSD) is the most commonly used technique for obtaining spatially resolved microstructural information from polycrystalline materials. We have developed two convolutional neural network approaches based on domain transform and transfer learning to reconstruct crystal orientations from electron backscatter diffraction patterns. Our models are robust to experimentally measured image noise and index orientations as fast as the highest EBSD scanning rates. We demonstrate that the quaternion norm metric is a strong indicator for assessing the reliability of the reconstructions in the absence of the ground truth. We demonstrate the applicability of the current methods on a tantalum sample. Graphical abstract: Image 1