Convolutional neural network-based method for real-time orientation indexing of measured electron backscatter diffraction patterns
- 27 March 2019
- journal article
- research article
- Published by Elsevier BV in Acta Materialia
- Vol. 170, 118-131
- https://doi.org/10.1016/j.actamat.2019.03.026
Abstract
No abstract availableKeywords
Funding Information
- U.S. Department of Energy
- Los Alamos National Laboratory
- Triad National Security, LLC
- National Nuclear Security Administration of U.S. Department of Energy (89233218CNA000001)
- Los Alamos National Laboratory’s Momentum Laboratory Directed Research and Development (20180677ER)
- LDRD-ECR (20190571ECR)
This publication has 25 references indexed in Scilit:
- Introduction and comparison of new EBSD post-processing methodologiesUltramicroscopy, 2015
- A Dictionary Approach to Electron Backscatter Diffraction IndexingMicroscopy and Microanalysis, 2015
- Dynamical Electron Backscatter Diffraction Patterns. Part I: Pattern SimulationsMicroscopy and Microanalysis, 2013
- Strains, planes, and EBSD in materials scienceMaterials Today, 2012
- Image denoising: Can plain neural networks compete with BM3D?Published by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- A perspective on trends in multiscale plasticityInternational Journal of Plasticity, 2010
- EBSD Image Quality MappingMicroscopy and Microanalysis, 2005
- Errors, Artifacts, and Improvements in EBSD Processing and MappingMicroscopy and Microanalysis, 2005
- Multilayer feedforward networks are universal approximatorsNeural Networks, 1989
- Approximation by superpositions of a sigmoidal functionMathematics of Control, Signals, and Systems, 1989