|About this Abstract
||2018 TMS Annual Meeting & Exhibition
||Advanced Characterization Techniques for Quantifying and Modeling Deformation
||Characterization of Dislocation/GB Interactions via HR-EBSD and Machine Learning
||Landon Hansen, Jay Carroll, David Fullwood, Eric Homer, Robert Wagoner
|On-Site Speaker (Planned)
By combining machine learning with HR-EBSD, new insights into dislocation/GB interactions can be gained that would not otherwise be possible through human observation of the experimental results. Electron backscatter diffraction (EBSD) and optical microscopy were used to characterize the microstructure of strained polycrystalline tensile samples while high resolution EBSD (HR-EBSD) was used to characterize the geometrically necessary dislocations (GND) throughout the grains. By analyzing large amounts of GB and GND information via machine learning, several preexisting theories about dislocation/GB interactions were validated and various trends identified.
||Planned: Supplemental Proceedings volume