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¶ 3. Mathematical Representation of Crystal Orientation, Misorientation

¶ 3. Mathematical Representation of Crystal Orientation, Misorientation

¶ 3. Mathematical Representation of Crystal Orientation, Misorientation

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more obvious that each column provides the equivalent in terms <strong>of</strong> expressing a sample<br />

direction with coefficients based on the crystal frame <strong>of</strong> reference.<br />

It is very important to remember that this definition <strong>of</strong> an orientation should be<br />

interpreted as a transformation <strong>of</strong> axes (passive rotation) that converts tensor quantities<br />

from the sample reference frame to the crystal frame. This means, for example, that<br />

quantities known in the crystal frame, such as anisotropic elastic moduli, must be<br />

converted to the sample frame with the inverse transformation. For the matrix<br />

description provided above, the inverse is simply the transpose <strong>of</strong> the orientation matrix.<br />

Be aware that no simple method exists <strong>of</strong> combining rotations when expressed in Miller<br />

index form. Moreover, Miller index notation does not provide a continuous description<br />

<strong>of</strong> rotations and is therefore not suitable for numerical descriptions <strong>of</strong> orientation.<br />

Similarly axis-angle notation can provide a continuous description if the axis is described<br />

as a unit vector with three real numbers, but not if the axis is described in terms <strong>of</strong> integer<br />

Miller indices (with a finite number <strong>of</strong> digits).<br />

<strong>3.</strong>B.2 Number <strong>of</strong> variables needed for an orientation.<br />

We only need 3 variables to describe an orientation. This is hard to see in a 3x3<br />

matrix with nine entries, though counting up the various constraints on the coefficients <strong>of</strong><br />

the matrix does provide the answer (orthogonality gives one set <strong>of</strong> 3 in terms <strong>of</strong> the other<br />

6, i.e. reduces the number from 9 to 6; then each set <strong>of</strong> three direction cosines is<br />

normalized, which reduces the number from 6 to 3). In fact, a matrix that describes a<br />

rotation is termed an orthogonal matrix. It is much easier, however, to see the<br />

requirement in terms <strong>of</strong> rotation angles and, specifically, Euler angles. Later on we will<br />

show how axis-angle pairs, Rodrigues vectors, and quaternions are related to these<br />

descriptions. An orthogonal matrix, a, is one for which one can write the following set <strong>of</strong><br />

six equations:<br />

<strong>3.</strong>C Euler angles<br />

2<br />

2 ∑ aij =1, aij i<br />

j<br />

∑ =1 (3B.2.1)<br />

Euler angles are the € most commonly used basis for representation <strong>of</strong> textures. They were<br />

adopted early on in the development <strong>of</strong> texture analysis and it is convenient to develop a<br />

familiarity with the locations <strong>of</strong> texture components <strong>of</strong> special interest for interpreting<br />

experimental and simulation results. The reason for adopting them was that there was<br />

already a large body <strong>of</strong> work on the physics <strong>of</strong> electrons and atoms that uses generalized<br />

spherical harmonics. These (orthonormal) functions are most conveniently expressed in<br />

terms <strong>of</strong> Euler angles. One very confusing point, however, is that different communities<br />

have adopted different conventions for the definition <strong>of</strong> Euler angles (Bunge, Roe,<br />

Canova, Kocks, etc.).<br />

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