Three-dimensional study of grain scale tensile twinning activity in magnesium: A combination of microstructure characterization and mechanical modeling
Source abstract
Abstract
Tensile twinning is a main deformation mode in hexagonal close packed structure metals, so it is important to comprehensively understand twinning mechanisms which are not fully disclosed using 2D or small volume 3D characterization techniques. A large area 3D electron backscatter diffraction (EBSD) measurement and crystal plasticity modeling were carried out to investigate the tensile twinning behaviors in a magnesium (Mg) alloy. The results showed that tensile twinning activity was underestimated using conventional 2D EBSD scans. When compressed to yield point, the examined twin frequency with 2D was lower than that using 3D EBSD. The effects of Schmid factor (SF) on twinning were investigated. Almost all high Schmid factor (SF>0.35) grains were twinned. A surprising high twin frequency of 82% in middle SF (0.35>=SF>=0.15) grains was observed, which was unexpected since the middle SF grains were believed to be unfavorable for twinning. The twin frequency in low SF (SF<0.15) grains was slightly increased from 2D to 3D EBSD due to the small volume of twins. The shear stress maintained a high level and was homogeneously distributed in high SF grains, facilitating twin nucleation and growth. The shear stress was distributed heterogeneously within the middle SF grains, and twins were nucleated within areas with positive shear stress. The shear stress in low SF grains was not favorable for twinning and twins occurred in the vicinity of stress accumulation. Twinning activities in the same grain varied on different layers. It was attributed to the stress fluctuation derived from grain environment changes.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Magnesium Alloys: Properties and Applications · Aluminum Alloys Composites Properties · Microstructure and mechanical properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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