“Accelerating the optimization of metallic materials using correlative measurements in electron microscopy”
Most structural metallic materials are submitted to complex loading conditions in service and generally develop a localized strain field at the microscale, as a response. Whereas strain localization occurs in the form of shear bands, slip bands, deformation twins or others, it is expected to be highly correlated to the materials microstructure. The nature, intensity and spatial distribution of such defects directly influence most mechanical properties such as strength, ductility, or fatigue performance. Understanding strain localization processes as a function of the microstructure is therefore of critical importance, in the global aim of improving a material’s mechanical performance.
Accurate mapping of the strain field as a function of the microstructure generally requires the collection of data over wide regions of interest (over 1mm2), with excellent resolution (few nm), and using a combination of detectors that come with their own artifacts. This makes data handling as well as manual analyzes very challenging and time consuming.
A framework for automated multi-modal data merging involving the combination of various electron microscopy techniques will be presented. Image processing, computer vision and machine learning are used to digitize microstructure features from raw data and generate a hierarchical representation of the microstructure’s architecture. It enables the fast recombination of data acquired with several detectors for a complete, multi-scale and accurate mapping of the strain field at the micro scale and enables the quantitative, automated, and non-human biased analysis of strain localization patterns. The trends obtained serve as a driving force for the optimization of microstructures for improved mechanical properties. Several application examples will be shown in advanced structural materials.
