Deformation twinning in Magnesium alloys both facilitates slip and forms sites for failure. Currently, basic studies of twinning in Mg are facilitated by electron backscatter diffraction (EBSD) which is able to extract a myriad of information relating to crystalline microstructures. Although much information is available via EBSD, various problems relating to deformation twinning have not been solved. This dissertation provides new insights into deformation twinning in Mg alloys, with particular focus on AZ31. These insights were gained through the development of new EBSD and related machine learning tools that extract more information beyond what is currently accessed.The first tool relating to characterization of deformed and twinned materials focuses on surface topography crack detection. The intensity map across EBSD images contains vital information that can be used to detect evolution of surface roughness and crack formation, which typically occurs at twin boundaries. The method of topography recovery resulted in reconstruction errors as low as 2% over a 500 μm length. The method was then applied to a 3 μm x 3 μm area of twinned Tantalum which experienced topographic alterations. The topography of Ta correlated with other measured changes in the microstructure. Additionally, EBSD images were used to identify the presence of cracks in Nickel microstructures. Several cracks were identified on the Ni specimen, demonstrating that cracks as thin as 34 nm could be measured.A further EBSD based tool developed for this study was used to identify thin compression twins in Mg; these are often missed in a traditional EBSD scan due to their size relative to the electron probe. This tool takes advantage of crystallographic relationships that exist between parent and twinned grains; common planes that exist in both grains lead to bands of consistent intensity as a scan crosses a twin. Hence, twin boundaries in a microstructure can be recognized, even when they are associated with thin twins. Proof of concept was performed on known twins in Inconel 600, Tantalum, and Magnesium AZ31. This method was then used to search for undetected twins in a Mg AZ31 structure, revealing nearly double the number of twins compared with those initially measured by standard procedures.To uncover the driving forces behind deformation twinning in Mg, a machine learning framework was developed to leverage all of the data available from EBSD and use that to create a physics based models of twin nucleation and growth. The resultant models for nucleation and growth were measured to be up to 86.5% and 96.1% accurate respectively. Each model revealed a unique combination of crystallographic attributes that affected twinning in the AZ31.



College and Department

Ira A. Fulton College of Engineering and Technology; Mechanical Engineering



Date Submitted


Document Type





EBSD, Magnesium, deformation twinning, surface topography, machine learning