The iterative series-expansion method for quantitative texture analysis. II. Applications

1992 ◽  
Vol 25 (2) ◽  
pp. 259-267 ◽  
Author(s):  
M. Dahms
Keyword(s):  
Distribution Function ◽  
Series Expansion ◽  
Expansion Method ◽  
Orientation Distribution ◽  
Polycrystalline Materials ◽  
Pole Density ◽  
General Outline ◽  
Positivity Condition ◽  
Pole Figures ◽  
Series Expansion Method

The orientation distribution function (ODF) of the crystallites of polycrystalline materials can be calculated from experimentally measured pole density functions (pole figures). This procedure, called pole-figure inversion, can be achieved by the series-expansion method (harmonic method). As a consequence of the (hkl)-({\bar h}{\bar k}{\bar l}) superposition, the solution is mathematically not unique. There is a range of possible solutions (the kernel) that is only limited by the positivity condition of the distribution function. The complete distribution function f(g) can be split into two parts, \tilde {f}(g) and \tildes {f}(q), expressed by even- and odd-order terms of the series expansions. For the calculation of the even part \tilde {f}(g), the positivity condition for all pole figures contributes essentially to an `economic' calculation of this part, whereas, for the odd part, the positivity condition of the ODF is the essential basis. Both of these positivity conditions can be easily incorporated in the series-expansion method by using several iterative cycles. This method proves to be particularly versatile since it makes use of the orthogonality and positivity at the same time. In the previous paper in this series [Dahms & Bunge (1989) J. Appl. Cryst. 22, 439–447] a general outline of the method was given. This, the second part, gives details of the system of programs used as well as typical examples showing the versatility of the method.

scholarly journals Approximation of the Orientation Distribution of Grains in Polycrystalline Samples by Means of Gaussians

1992 ◽  
Vol 19 (1-2) ◽  
pp. 9-27 ◽  
Author(s):  
D. I. Nikolayev ◽  
T. I. Savyolova ◽  
K. Feldmann
Keyword(s):  
Rolling Texture ◽  
Expansion Method ◽  
Operating Mode ◽  
Orientation Distribution ◽  
New Method ◽  
Gaussian Distributions ◽  
Positivity Condition ◽  
The Central Limit Theorem ◽  
Pole Figures ◽  
Series Expansion Method

The orientation distribution function (ODF) obtained by classical spherical harmonics analysis may be falsified by ghost influences as well as series truncation effects. The ghosts are a consequence of the inversion symmetry of experimental pole figures which leads to the loss of information on the “odd” part of ODF.In the present paper a new method for ODF reproduction is proposed. It is based on the superposition of Gaussian distributions satisfying the central limit theorem in the SO(3)-space as well as the ODF positivity condition. The kind of ODF determination offered here is restricted to the fit of Gaussian parameters and weights with respect to the experimental pole figures. The operating mode of the new method is demonstrated for a rolling texture of copper. The results are compared with the corresponding ones obtained by the series expansion method.

scholarly journals A Positivity Method for the Determination of Complete Orientation Distribution Functions

1988 ◽  
Vol 10 (1) ◽  
pp. 21-35 ◽  
Author(s):  
M. Dahms ◽  
H. J. Bunge
Keyword(s):  
Expansion Method ◽  
Orientation Distribution ◽  
Distribution Functions ◽  
Positivity Condition ◽  
Odd Order ◽  
Pole Figures ◽  
Zero Range ◽  
Even Order ◽  
Series Expansion Method

A refinement of the zero-range method, a procedure to calculate the odd order coefficients in the series expansion method of texture analysis, is presented. The only assumption in this procedure is the positivity condition. In this respect, it is comparable to the quadratic method. Contrary to this method, however, the even order coefficients are not changed. No zero range in the pole figures and no shape of the existing texture is to be assumed.

scholarly journals Optimal Calculation of ODF With the Canonical Normal Distribution on the Rotation Group

1999 ◽  
Vol 33 (1-4) ◽  
pp. 337-341
Author(s):  
T. I. Savyolova ◽  
E. A. Davidzhan ◽  
T. M. Ivanova
Keyword(s):  
Experimental Data ◽  
Distribution Function ◽  
Physical Properties ◽  
Normal Distribution ◽  
Orientation Distribution Function ◽  
Orientation Distribution ◽  
Rotation Group ◽  
Polycrystalline Materials ◽  
Pole Figures ◽  
Optimal Calculation

Macroscopic physical properties of most polycrystalline materials are controlled by orientation distribution of their grains. The orientation distribution function (ODF) of a polycrystal is seldom if ever determined directly from an experiment. Usually experimental data are represented by a set of pole figures (PFs), these latter are some integral projections of the ODF. The main problem of quantitative texture analysis is to recover ODF from its corresponding PFs. With any set of PFs the solution of this problem is non-unique. That is why some assumptions about ODF structure are necessary. We consider ODF as superposition of the canonical normal distribution (CND) on the rotation group SO(3).

scholarly journals ODF Calculation by Series Expansion from Incompletely Measured Pole Figures Using the Positivity Condition Part I—Cubic Crystal Symmetry

1987 ◽  
Vol 7 (3) ◽  
pp. 171-185 ◽  
Author(s):  
M. Dahms ◽  
H. J. Bunge
Keyword(s):  
Series Expansion ◽  
Iterative Procedure ◽  
Orientation Distribution ◽  
Distribution Functions ◽  
Crystal Symmetry ◽  
Cubic Crystal ◽  
Positivity Condition ◽  
Pole Figures ◽  
Orientation Distribution Functions

The calculation of orientation distribution functions (ODF) from incomplete pole figures can be carried out by an iterative procedure taking into account the positivity condition for all pole figures. This method strongly reduces instabilities which may occasionally occur in other methods.

scholarly journals The Effect of Smoothing on ODF Reproduction

1996 ◽  
Vol 25 (2-4) ◽  
pp. 149-157 ◽  
Author(s):  
D. I. Nikolayev ◽  
K. Ullemeyer
Keyword(s):  
Distribution Function ◽  
Orientation Distribution Function ◽  
Orientation Distribution ◽  
Primary Data ◽  
Pole Density ◽  
Data Set ◽  
Normal Distributions ◽  
Ideal Orientation ◽  
Pole Figures ◽  
Characteristic Component

Although the smoothing of experimental pole-density data modifies the primary data set, the effect of such a procedure on the Orientation Distribution Function and on the interpretation of the texture was not considered until recently. The influence of smoothing on texture reproduction will be derived for the case, that the Orientation Distribution Function is approximated by a linear combination of normal distributions (ideal orientation components) and smoothing is carried out with normal distributions, too. The characteristic component parameters are well-suited to indicate changes of the Orientation Distribution Function. The observed texture variations as a consequence of smoothing lead to the conclusion, that moderate smoothing does not falsifythe texture. Several possibilities to control the effect of smoothing will be discussed. Based on the visual comparison of smoothed and unsmoothed pole-figures it is argued, that even extreme smoothing may be useful for some purposes.

scholarly journals Determination of the Complete Orientation Distribution Function by the Zero-Range Method

1986 ◽  
Vol 6 (4) ◽  
pp. 289-313 ◽  
Author(s):  
H. P. Lee ◽  
H. J. Bunge ◽  
C. Esling
Keyword(s):  
Distribution Function ◽  
Pole Figure ◽  
Orientation Distribution Function ◽  
Statistical Error ◽  
Orientation Distribution ◽  
Positivity Condition ◽  
Additional Information ◽  
Pole Figures ◽  
Zero Range

Because of the superposition of pole figures corresponding to symmetrically equivalent crystal directions, only the reduced orientation distribution function f∼(g) can be obtained directly by pole figure inversion. The additional information contained in the positivity condition of the ODF allows, however, the determination of an approximation to the “indeterminable” part and hence of the complete ODF f(g), if the texture has sufficiently large zero-ranges. The application of the method and the accuracy of the results was tested using two theoretical and one experimental textures. The accuracy of the complete ODF depends on the size of the zero-range, the errors in its determination, and on the errors, experimental and truncational, of the reduced ODF. The “physical zero” used in order to determine the zero-range is defined according to the statistical error of the pole figure measurement.

scholarly journals The Use of a Quadratic Form for the Determination of Non-negative Texture Functions

1983 ◽  
Vol 6 (1) ◽  
pp. 1-19 ◽  
Author(s):  
P. Van Houtte
Keyword(s):  
Series Expansion ◽  
Quadratic Forms ◽  
Expansion Method ◽  
Experimental Methods ◽  
Classical Analysis ◽  
Pole Figures ◽  
Zero Range ◽  
Euler Space ◽  
Series Expansion Method

The classical analysis of measured pole figures of textured polycrystals by the series expansion method does not necessarily produce a non-negative texture function. The main reason for this is, that the method is unable to find the terms of odd rank l of the series expansion.A new method is proposed, which introduces the non-negativity condition into the series expansion method by the use of quadratic forms. The method is found to be successful when treating sharp textures, which have a considerable zero range in Euler space. The preliminary determination of this zero range by experimental methods is however not necessary.

scholarly journals Examination of the Iterative Series-Expansion Method for Quantitative Texture Analysis

1995 ◽  
Vol 23 (2) ◽  
pp. 115-129 ◽  
Author(s):  
D. Raabe
Keyword(s):  
Series Expansion ◽  
Linear Equations ◽  
Three Dimensional ◽  
Expansion Method ◽  
Orientation Space ◽  
First Case ◽  
Pole Figures ◽  
Polycrystalline Aggregates ◽  
Analytical Tools ◽  
Series Expansion Method

Three-dimensional orientation distributions of grains in polycrystalline aggregates are referred to as crystallographic textures. Commonly, they are computed from two-dimensional centro-symmetric pole figures by employment of series expansion techniques or so called direct inversion methods. Both approaches lead to inaccuracies which are due to the absence of the odd coefficients and by truncation errors in the first case and to the under-determination of the set of linear equations combining cells in the pole figures and in the three-dimensional orientation space in the second case. For both types of calculation methods various correction procedures were suggested. In case of the series expansion methods the introduction of the non-negativity condition was reported to considerably improve the obtained solution. However, before large series of experimental data can be processed by such a method, its reliability has to be checked by use of analytical tools. Hence, in the present study a recently introduced iterative series-expansion method which accounts for the non-negativity condition is examined by use of standard functions.

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