Data-driven prediction of the structure-property relationships for grain boundaries in metallic alloys
Nanocrystalline materials have unique properties such as high ultimate strength and superior hardness. However, they also exhibit some disadvantages, such as low thermal stability. An effective strategy to address this issue is alloying with other materials. Grain boundaries play a pivotal role in property prediction due to their orientation between grains and the complexity of their structure. The prediction of structure-property relationships for GBs with microstructural complexity represents a difficult challenge.
To understand the effects of dopants on the material properties of grain boundaries, we constructed some bicrystal models for Al and Mg-doped Al (Al-Mg) alloys. Findings from shearing simulations of these GBs indicate that the GB structure and dopant distribution can influence GB migration. Dopants inhibit GB migration at certain GBs, effectively reinforcing these GBs. Shear-coupled GB migration in pure Al, as well as dopant inhibition of GB Al-Mg alloys, both contribute to the mechanisms of GB migration.
- Doctor of Philosophy
- Mechanical Engineering
- West Lafayette