| About this Abstract |
| Meeting |
MS&T21: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
| Presentation Title |
Spatial and Statistical Representation of Strain Localization as a Function of the 3D Microstructure Using Multi-modal and Multi-scale Data Merging |
| Author(s) |
Marie Charpagne, J.C. Stinville, Andrew T. Polonsky, McLean P. Echlin, Kelly Nygren, Dalton Shadle, Matthew P. Miller, Tresa M. Pollock |
| On-Site Speaker (Planned) |
Marie Charpagne |
| Abstract Scope |
Most structural materials exhibit a localized strain field upon loading. Whereas strain localization occurs in the form of shear bands, slip bands, deformation twins or other forms, it is expected to be highly correlated to the materials microstructure. The intensity and spatial distribution of such deformation structures directly influence most mechanical properties such as strength, ductility and fatigue life. Understanding strain localization processes as a function of the microstructure is therefore of critical importance, in the global aim of improving a materials mechanical properties. A framework for automated multi-modal data merging, involving the combination of digital image correlation captured in the scanning electron microscope and microstructure data collected using 3D electron backscatter diffraction will be presented. The use of computer vision tools and statistical microstructure descriptors enables a quantitative, automated and non-human biased analysis of strain localization patterns. Application examples will be shown in a superalloy and a titanium alloy. |