The Quality Map group provides a set of maps that provide insight into how well the cross correlation (XCF) process worked on the EBSD data. This includes the Mean Angular Error, Geometric Mean and High Resolution KAM map.
High Resolution KAM
One of the immediate benefits of applying a cross correlation function to look for shifts in the EBSD pattern is to generate a better version of the well known KAM map. The High Resolution Kernel Average Misorientation map. We can be far more precise about the measurement of misorientations across the sample, typically with an accuracy of better than 1/100th of a degree.
Elastic Map Group
The Elastic map group will provide results for strain and stress measurement. This includes the full matrix of normal and shear strains ε11, ε12, ε31, ε22, ε23, ε33 together with rotations ω12, ω31, ω23.
Plastic Strain Group
The Plastic Strain map group includes Infinitesimal Rotations, Finite Rotations and Geometrically Necessary Dislocations (GND’s). Rotations include ω11, ω12, ω13, ω21, ω22, ω23, ω31, ω32, ω33.
Geometrically Necessary Dislocations are normally encountered when high stress gradients are observed. Lattice rotation and elastic stretching of the lattice is usually needed in order to ensure compatibility of displacement. CrossCourt4’s GND function provides total GND, selected phase and selected slip system options.
Shift Map Group
For each pattern ROI used in the cross correlation function the X corrected and Y corrected shift maps can be examined as well as peak height and error.
Principal Strains Group
The Remapping function allows for improved measurement of elastic strain in plastically deformed samples. It works by discriminating large lattice rotations from the elastic strains. In particular this is the situation in a plastically deformed metals. Remapping is a method of back rotating the EBSD patterns so that the rotations are ‘removed’ and ‘only’ the elastic stress and strains are left.
The Analysis package includes a set of data filters that can be applied to the shift results to remove or isolate specific features from the data set. Included for these purposes is a set of filter functions such as the Threshold filter, Grain filter, Pixel filter and Area filter.