CSL Boundaries edit page

In this section we consider the analysis of CSL boundaries. Therefore lets start by importing some Iron data and reconstructing the grain structure.

mtexdata csl

% grain segementation
[grains,ebsd.grainId] = calcGrains(ebsd('indexed'));

% grain smoothing
grains = smooth(grains,5);

% plot the result
ebsd = EBSD
 Phase   Orientations  Mineral         Color  Symmetry  Crystal reference frame
    -1  154107 (100%)     iron  LightSkyBlue      m-3m                         
 Properties: ci, error, iq, x, y
 Scan unit : um

Next we plot image quality as it makes the grain boundaries visible. and overlay it with the orientation map

mtexColorMap black2white

% the option 'FaceAlpha',0.4 makes the plot a bit transluent
hold on
hold off

Detecting CSL Boundaries

In order to detect CSL boundaries within the data set we first restrict the grain boundaries to iron to iron phase transitions and check then the boundary misorientations to be a CSL(3) misorientation with threshold of 3 degree.

% restrict to iron to iron phase transition
gB = grains.boundary('iron','iron')

% select CSL(3) grain boundaries
gB3 = gB(angle(gB.misorientation,CSL(3,ebsd.CS)) < 3*degree);

% overlay CSL(3) grain boundaries with the existing plot
hold on
plot(gB3,'lineColor','gold','linewidth',3,'DisplayName','CSL 3')
hold off
gB = grainBoundary
 Segments    length  mineral 1  mineral 2
    20356  16364 µm       iron       iron

Mark triple points

Next we want to mark all triple points with at least 2 CSL boundaries

% logical list of CSL boundaries
isCSL3 = grains.boundary.isTwinning(CSL(3,ebsd.CS),3*degree);

% logical list of triple points with at least 2 CSL boundaries
tPid = sum(isCSL3(grains.triplePoints.boundaryId),2)>=2;

% plot these triple points
hold on
hold off

Merging grains with common CSL(3) boundary

Next we merge all grains together which have a common CSL(3) boundary. This is done with the command merge.

% this merges the grains
[mergedGrains,parentIds] = merge(grains,gB3);

% overlay the boundaries of the merged grains with the previous plot
hold on
hold off

Finaly, we check for all other types of CSL boundaries and overlay them with our plot.

delta = 5*degree;
gB5 = gB(gB.isTwinning(CSL(5,ebsd.CS),delta));
gB7 = gB(gB.isTwinning(CSL(7,ebsd.CS),delta));
gB9 = gB(gB.isTwinning(CSL(9,ebsd.CS),delta));
gB11 = gB(gB.isTwinning(CSL(11,ebsd.CS),delta));

hold on
plot(gB5,'lineColor','b','linewidth',2,'DisplayName','CSL 5')
hold on
plot(gB7,'lineColor','g','linewidth',2,'DisplayName','CSL 7')
hold on
plot(gB9,'lineColor','m','linewidth',2,'DisplayName','CSL 9')
hold on
plot(gB11,'lineColor','c','linewidth',2,'DisplayName','CSL 11')
hold off

Misorientations in the 3d fundamental zone

We can also look at the boundary misorienations in the 3 dimensional fundamental orientation zone.

% compute the boundary of the fundamental zone
oR = fundamentalRegion(ebsd.CS,ebsd.CS,'antipodal');
close all

% plot 500 random misorientations in the 3d fundamenal zone
mori = discreteSample(gB.misorientation,500);
hold on
hold off

% mark the CSL(3) misorientation
hold on
csl3 = CSL(3,ebsd.CS);
plot(csl3.project2FundamentalRegion('antipodal') ,'MarkerColor','r','DisplayName','CSL 3','MarkerSize',20)
hold off

Analyzing the misorientation distribution function

In order to analyze more quantitatively the boundary misorientation distribution we can compute the so called misorientation distribution function. The option antipodal is applied since we want to identify mori and inv(mori).

mdf = calcDensity(gB.misorientation,'halfwidth',5*degree,'bandwidth',48)
mdf = SO3FunHarmonic (iron → iron)
  antipodal: true
  bandwidth: 48
  weight: 1

Next we can visualize the misorientation distribution function in axis angle sections.

plot(mdf,'axisAngle',(25:5:60)*degree,'colorRange',[0 15])



The MDF can be now used to compute prefered misorientations

[~,mori] = max(mdf,'numLocal',2)
mori = misorientation (iron → iron)
  size: 2 x 1
  antipodal:         true
  Bunge Euler angles in degree
     phi1     Phi    phi2
  116.523 48.2466 206.627
  134.977 37.7233  225.02

and their volumes in percent

100 * volume(gB.misorientation,CSL(3,ebsd.CS),2*degree)

100 * volume(gB.misorientation,CSL(9,ebsd.CS),2*degree)
ans =
ans =

or to plot the MDF along certain fibres

omega = linspace(0,60*degree);
fibre100 = orientation.byAxisAngle(xvector,omega,mdf.CS,mdf.SS)
fibre111 = orientation.byAxisAngle(vector3d(1,1,1),omega,mdf.CS,mdf.SS)
fibre101 = orientation.byAxisAngle(vector3d(1,0,1),omega,mdf.CS,mdf.SS)

close all
plot(omega ./ degree,mdf.eval(fibre100),'LineWidth',2)
hold on
plot(omega ./ degree,mdf.eval(fibre111),'LineWidth',2)
plot(omega ./ degree,mdf.eval(fibre101),'LineWidth',2)
hold off
xlabel('misorientation angle');
fibre100 = misorientation (iron → iron)
  size: 1 x 100
fibre111 = misorientation (iron → iron)
  size: 1 x 100
fibre101 = misorientation (iron → iron)
  size: 1 x 100

or to evaluate it in an misorientation directly

mori = orientation.byEuler(15*degree,28*degree,14*degree,mdf.CS,mdf.CS)


mori = misorientation (iron → iron)
  Bunge Euler angles in degree
  phi1  Phi phi2
    15   28   14
ans =
ans =