the parent to child orientation relationship that fits child to child misorientations best, refined from an initial guess

Syntax

p2c = calcParent2Child(c2c, p2c0)

From child to child misorientations.

p2c = calcParent2Child(childOri, p2c0)

From pairs of child orientations.

p2c = calcParent2Child(c2c, p2c0, quantile=0.8)

The fit on the best 80 percent.

p2c = calcParent2Child(c2c, p2c0, local=True)

Iterate from p2c0 only.

p2c, fit = calcParent2Child(c2c, p2c0, fit=True)

With the misfit of every misorientation.

Description

Two children of one parent grain are related by a misorientation the relationship predicts, V_i^-1 V_j for two of its variants. The fitted relationship minimises the trimmed chordal misfit of the measured misorientations to the nearest predicted one, taken over the share quantile of the smallest misfits, by a fixed point iteration.

With local=True the iteration starts from p2c0 alone. By default it also starts from the best basins of a scan over all relationships, so that a poor start still finds the global minimum; p2c0 refined on its own remains a candidate.

Pairs of child orientations, an (n, 2) array in the specimen frame, are turned into their misorientations. The progress is reported unless verbose=False.

Input

`c2c`	orientation, child to child misorientations
`childOri`	orientation, (n, 2) pairs of child orientations
`p2c0`	orientation, the initial guess of the parent to child relationship

Keywords

keyword	default	description
`local`	`False`	iterate from p2c0 only, without the scan
`quantile`	`0.9`	the share of the misorientations the fit is based on
`threshold`	`inf`	misorientations misfitting by more than this angle are outliers
`maxIteration`	`30`	the largest number of iterations
`maxSample`	`50000`	fit on at most this many misorientations, a random subsample of the rest
`searchResolution`	`4*degree`	the resolution of the grid of the scan
`scanSample`	`250`	the number of misorientations that rank the basins of the scan
`numLocal`	`5`	the number of basins of the scan fitted locally
`verbose`	`True`	report the progress
`fit`	`False`	also return the misfit of every misorientation, MATLAB’s second output

Output

`p2c`	orientation, the fitted parent to child relationship
`fit`	the disorientation angle of every misorientation to the nearest predicted one

References

T. Nyyssonen et al., Crystallography, morphology, and martensite transformation of prior austenite in intercritically annealed high-aluminum steel, Metall. Mater. Trans. A 49 (2018), doi:10.1007/s11661-018-4904-9

Used on

MaParentGrainReconstructionAdvanced, MartensiteVariants, OrientationRelationshipFit

MATLAB’s calcParent2Child

source

Citing this page. This page is part of the documentation of MTEX, a free and open source MATLAB toolbox for analyzing and modeling crystallographic textures. It was written by The MTEX Developers and is published at https://mtex-toolbox.github.io/calcParent2Child_py.html. If you use MTEX, or reuse text or figures from this page, in your research, please cite

F. Bachmann, R. Hielscher, H. Schaeben: Texture Analysis with MTEX - Free and Open Source Software Toolbox, Solid State Phenomena 160 (2010), 63-68. 10.4028/www.scientific.net/SSP.160.63

BibTeX

@article{bachmann2010mtex,
  author  = {F. Bachmann and R. Hielscher and H. Schaeben},
  title   = {Texture Analysis with MTEX - Free and Open Source Software Toolbox},
  journal = {Solid State Phenomena},
  volume  = {160},
  pages   = {63-68},
  year    = {2010},
  doi     = {10.4028/www.scientific.net/SSP.160.63},
  url     = {https://doi.org/10.4028/www.scientific.net/SSP.160.63}
}

Other papers describing specific MTEX methods are listed under Publications — please cite the one that best fits your application. The MTEX source code is licensed under the GNU General Public License v2.0; the text and figures of this documentation are licensed under CC BY 4.0, which permits reuse — including by automated systems — provided The MTEX Developers and this page are credited.

calcParent2Child