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Quantitative characterization of microstructure of pure copper processed by ECAP.

INTRODUCTION

In microstructural research of metallic materials, methods allowing direct evaluation of the grain structure are often limited to investigation of 2D sections. Planar quantities are used to estimate parameters of the original 3D structure with usual stereological methods. One general limitation is that the estimators of the structural parameters are highly influenced by the resolution given by the grid step of the subsample where the measurements are provided. On the other hand, given the experimental setting, one has comprehensive information about the orientations in grains and disorientations in grain boundaries which can be further analysed with various methods. This paper presents basic principles of processing data from 2D scanning electron microscopy (SEM), describes methods of quantitative characterization of the observed microstructure based on second-order analysis of random marked sets and demonstrates the methods on particular samples.

To describe the structure of polycrystalline materials, modern attitude consists in the characterization of different types of boundaries present in the material and their connectivity in the grain boundary network (Rohrer, 2011). With data from 3-dimensional electron backscatter diffraction (EBSD; Calcagnotto et al., 2010; Wilkinson and Britton, 2012) it is possible to explore the entire 5-parametrical distribution of the grain boundaries where three parameters are related to the disorientation and the other two represent orientation of the interface plane. Despite of limitations of 2-dimensional observations, it still provides great potential for statistical analysis and it allows to process greater amount of observations than space- and time-consuming 3D methods. It is also possible to estimate the 3-dimensional microstructure on the basis of statistical analyses of the data obtained from the 2-dimensional EBSD.

Electron backscatter diffraction is a scanning electron microscope (SEM) based technique which has become well known as a powerful and versatile experimental tool for materials scientists, physicists, geologists and other scientists and engineers (Randle, 2009). It allows the measurement of microtexture (Jiang et al., 2008), microstructure quantification (Bastos et al., 2006), grain and phase boundary characterization (Randle et al., 2008), phase identification (Perez et al., 2006) and strain determination (Britton and Wilkinson, 2012) in crystalline multiphase materials of any crystal structure.

The aim of the present paper is a systematic characterization of Cu processed by equal-channel angular pressing (ECAP) before creep testing. The creep behaviour of ECAPed materials probably belongs to the fewest examined properties of materials processed by ECAP. Creep behaviour was usually investigated in materials which were prepared by severe plastic deformation (SPD) technique at room temperature. For this reason, the creep tests were performed at higher temperatures than the processing temperature of SPD technique was. It is generally accepted that tensile samples are put into the furnace interior and then heated to the creep temperature. It is important to note that each specimen was heated to the testing temperature in the furnace of the creep testing machine up to creep temperature. For this reason, the microstructure is statically annealed and significantly influenced by temperature-induced changes like grain growth, recovery, recrystallization etc.

The systematic characterization of microstructure in Cu specimens processed by ECAP before loading can be important for better understanding of the unusual creep behaviour of ECAPed Cu (Dvorak et al., 2010). Despite of extensive interest in SPD material, there is no systematic work describing real microstructure of ECAP materials before loading when testing temperature in the creep testing machine was reached and stabilized.

To describe the microstructure, statistical methods characterizing spatial distribution of the boundaries marked by their disorientations are introduced. They basically work with a single mark determined by the disorientation angle [theta] but even more complex information about the disorientation can be used by distinguishing different types of "special" boundaries. The methods are applied on samples of copper processed by ECAP followed by annealing with different times and temperatures. Effect of different number of passes on the grain structure was examined in previous studies (Ilucova et al., 2007; Kral et al., 2011).

The rest of the paper is organized as follows. The next section describes the experimental setup including preparation of the samples and their microscopical observations. The following section introduces basics of image processing of these observations. Further, the processed data are used for quantitative analysis of grain boundaries and this analysis is followed by numerical results. Conclusions are made in the final section.

EXPERIMENTAL BACKGROUND

The microstructure of specimens was examined by scanning electron microscope Jeol 6460 equipped with an EBSD unit operating at an accelerating voltage of 20 kV with specimen tilted at 70[degrees]. Results presented in the paper come from the research of copper (99.99% purity) processed by ECAP which involves pressing of a sample through a die within a channel that is bent into an L-shaped configuration (Fig. 1).

The billets in the cast state with 10 mm x 10 mm cross section and 60 mm length were processed by ECAP at room temperature using a die with two perpendicular channels. Each billet was processed by a selected number of ECAP passes. The ECAP was performed using route Bc (Furukawa et al., 1998) in which the billet was rotated around the longitudinal axis by 90[degrees] clockwise between the passes. Each pass corresponds to an additional strain value approximately equal to 1. After ECAP, billets were annealed at 373 K, 423 K, 473 K or 573 K for 10 hours. The microstructure analyses were focused on samples processed by 8 ECAP passes. The microstructure changes of pure Cu (99.99%) processed by 8 ECAP passes occurring during the annealing caused considerable decrease of hardness (Fig. 2).

The specimens for microstructure analyses were cut using electro spark process in an oil bath which minimalizes the effect of deformation and temperature on the surface. The specimens were grinded by 600-4000 SiC paper and water was used as the lubricant during grinding. The specimens were grinded by 600 SiC paper until their surfaces were flat. The surfaces of grinded specimens were checked by light microscope Neophot 32. The specimens were rotated by 90[degrees] between subsequent grinding steps and grinded perpendicular to the scratches which were created in the former grinding step until these scratches disappeared. Last grinding step was performed using 4000 SiC paper in order to reduce time of electrolytic polishing. It is generally accepted that electropolishing is a widely used method for final step preparation because it removes the strains induced by mechanical grinding. Finally, the specimens were electropolished using 250 ml phosphoric acid, 250 ml ethanol, 50 ml propylalcohol and 500 ml water for 60 s at room temperature.

All the three cross sections XY, XZ and YZ were examined but the analyses were especially focused on the section XZ. The area from which the EBSD patterns is acquired with an electron beam focused on a 70[degrees] tilted sample, is approximately elliptical (Humphreys, 2001). For this reason, resolution perpendicular and parallel to the tilt axis can be distinguished, see Fig. 3 in Humphreys (2001). The major axis, which is perpendicular to the tilt axis, is about three times longer than the minor axis.

The electron beam is deflected and the orientation data are acquired and stored in each point of selected area. The point-to-point step size is based on the expected microstructure and examined size region. In the present work, the size of the image window was selected as 128 x 96 [micro]m and the step of the EBSD was ~ 0.5 [micro]m for the annealed specimens and these parameters were 40 x 30 [micro][micro]m and ~ 0.07 [micro]m for the specimen only processed by ECAP.

Each EBSD pattern is analyzed and the solution is found when at least 4 diffraction lines are used for its determination. When the number of determined lines is lower the solution is not found and non-indexed data point is assigned. In the case that the number of non-indexing points is low, the data can be repaired by clean-up procedure. This procedure ensures that data points with the probability > 0.95 of correct indexing are retained in the analysis. The points where the probability of correct indexing is lower (points with low pattern quality) are re-assigned to neighbouring regions of similar orientation. The procedure assumes that low pattern quality points are associated with grain boundaries or regions of high dislocation density. It is known that EBSD lines are not always ideal and for this reason, the standard angle difference of 2.5[degrees] between acquired lines and lines of the solution is adjusted.

The quality map of EBSD patterns with the demonstration of an EBSD pattern is in Fig. 3. The light points denote high quality and the black points denote non-indexable EBSD patterns. The inspection of Fig. 3 shows that quality of EBSD patterns was high. The number of indexable EBSD patterns was approximately 97-98% in the annealed specimens and 79% in the specimens only processed by ECAP.

IMAGE PROCESSING

The main information measured by EBSD are Euler angles [[phi].sub.1], [PHI], [[phi].sub.2] representing the crystal orientation in each grid point (Fig. 4). These three parameters are sufficient to describe the mutual position of a reference coordinate system and orientation of the crystal lattice.

Fig. 5a describes the definition of a crystal direction [uvw]. Further, it is common to denote <uvw> all crystalographically-related directions, i.e., the directions coincident with [uvw] with respect to all symmetries of the crystal lattice. Supposing the difference in orientations between two neighbouring points which is called their misorientation, it is common to use an angle-axis representation [theta]<uvw> based on the fact that one orientation can be matched to another using rotation by an angle [theta] around an axis <uvw>. Because of non-uniqueness of this transformation especially in highly symmetric systems, only the solution with the minimum angle [theta] is considered and such a transformation [theta]<uvw> is called disorientation. Results presented in this paper come from a research of metals with cubic crystal systems where the upper limit for the angle [theta] is about 62.8[degrees]. Details of conversion among Euler angles, transformation matrices and angle-axis representation can be found in (Engler and Randle, 2010).

Data obtained from EBSD can be immediately displayed in a pixel image where each pixel corresponds to one grid point and its colour is related to the crystal orientation (Fig. 9 in the section Numerical results). To represent each orientation by a single colour, an inverse pole figure shown in Fig. 5b is used as a colour scheme. Every orientation is located there according to the direction of one chosen axis of a reference coordinate system with respect to the crystal coordinate system in the given point. For instance, pure blue (vertex [111]) reveals that the chosen reference axis is parallel to the body diagonal of a unit cubic crystal cell. Because of invariance under rotations around the reference axis, this representation is not sufficient but still very illustrative for recognizing differences in orientations. Grain boundaries with the disorientation angle exceeding a limit value [DELTA], which is equal to 15[degrees] in Fig. 9, are coloured white. Another possibility often used is colouring the grains with random colours in order to distinguish them easily (Fig. 7).

When focusing just on the grain boundaries, we can draw them dependently on the disorientation angle [theta]. In Fig. 6a the darkness of each boundary is related to the disorientation angle; pure black colour corresponds to the maximum angle 62.8[degrees]. In Fig. 6b two types of boundaries are distinguished--green low angle boundaries with [theta] [less than or equal to] 15[degrees] and red high angle boundaries with [theta] [greater than or equal to] 15[degrees]. In a similar way it is also possible to visualize some special boundaries according to their coincidence site lattice (CSL) type.

QUANTITATIVE ANALYSIS OF GRAIN BOUNDARIES

To specify a grain boundary, five parameters are needed in general. Three of them are related to its misorientation (e.g., Euler angles) and the other two describe the orientation of the interface plane (e.g., spherical angles of the plane normal). However, characterizing the whole five-parameter distribution requires a large population of observable grain boundaries. In that case it is possible to use statistical methods developed for estimating the distribution density. For better insight, only changes in several parameters fixing the other ones are actually investigated which usually leads to some discretization of the parametrical space. In what follows, we will focus just on the misorientations of grain boundaries, especially the disorientation angle. This quantity represents a real-valued mark of the grain boundary network which allows to characterize it by the means of random marked closed sets (RMCS). In the following, the terminology and notation from (Ballani et al., 2012) are used.

We consider a random marked closed set (Y, Z) in the d-dimensional Euclidean space [R.sup.d] as a random function Z defined on a random domain Y [subset] [R.sup.d]. RMCS is stationary if its distribution is invariant under translations and it is isotropic if its distribution is invariant under rotations.

For any [epsilon] [greater than or equal to] 0 define the random field

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

where [B.sub.[epsilon]](x) denotes the Euclidean ball in [R.sup.d] with centre x and radius [epsilon], [Y.sub.[direct sum][epsilon]] is a dilated set [Y.sub.[direct sum][epsilon]] = Y [direct sum] [B.sub.[epsilon]](o).

Second-order characteristics of RMCS (Y, Z) for x, y [member of] [R.sup.d] are defined as follows. Let us define

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (1)

whenever [kappa][absolute value of f] (x, y) < [infinity] and P(x, y [member of] [Y.sub.[direct sum][epsilon]]) > 0 for all [epsilon] > 0, otherwise [[kappa].sub.f](x, y) is undefined. Eq. 1 is a limit of conditional expectation given the two points x, y lie in the dilated set. Common choices of f are

e(m, n) = m, c(m, n) = mn, v(m, n) = [m.sup.2], g(m, n) = [(m - n).sup.2].

Then define the conditional mean mark and the mark covariance function

[E.sub.[kappa]](x, y) = [[kappa].sub.[epsilon]](x, y), (2)

[cov.sub.[kappa]](x, y) = [[kappa].sub.c](x, y) - [[kappa].sub.e](x, y)[[kappa].sub.e](y, x). (3)

Further express the mark correlation function

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)

and the mark variogram

[[kappa].sub.c](x,y) = [[kappa].sub.g](x,y)/2 = [[[kappa].sub.v](x,y) + [[kappa].sub.v](y,x)]/2 - [[kappa].sub.c](x,y). (5)

Another characteristic is the Stoyan's [k.sub.mm] function (Stoyan et al., 1995)

[k.sub.mm](x, y) = [[bar.m].sup.-2][[kappa].sub.c](x, y), [bar.m] = E[Z(x)|x [member of] Y].

Under the assumptions of stationarity and isotropy of the RMCS (Y, Z), the characteristics defined in Eq. 1 are functions of the distance r = [parallel]x - y[parallel] only. For their estimation on a bounded window W [subset] [R.sup.d], choose a finite set of test points T [subset] W such that for a fixed [epsilon] > 0 and for suitable interpoint distances r [member of] [R.sup.+], the sets

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

[N.sup.(2).sub.[epsilon]T](r) = {(x, y) [member of] [([Y.sub.[direct sum][epsilon]] [intersection] T).sup.2] : [parallel]x - y[parallel] = r}

are nonempty. For [[kappa].sub.e], [[kappa].sub.v] and [[kappa].sub.c] we use the following statistical estimators:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (6)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (7)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (8)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (9)

where [absolute value of *] stands for cardinality of the set. Under some additional assumptions, these estimators are asymptotically unbiased when [epsilon] [down arrow] 0.

Taking into account the grain boundary structure with almost constant marks along its edges, it is reasonable to consider whether a given pair of points lies on the same edge or not. An important feature of the estimators introduced above is that marks in a small distance are highly correlated simply because of the fact that the pair of points often belongs to the same edge. However, correlations just among different edges can give us more valuable information about the second-order structure. In what follows, we denote by x ~ y the relation that the points x and y belong to [epsilon]-neighbourhood of one edge and by x [??] y the opposite case. Under stationarity and isotropy we define

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

and [[??].sub.e](r), [[??].sub.v](r), [[??].sub.c](r), [[??].sub.g](r) can be defined in a similar way like Eqs. 6-9, using non edge-related sets [[??].sup.(1).sub.[epsilon],T](r), [[??].sup.(2).sub.[epsilon],T](r) instead of [N.sup.(1).sub.[epsilon],T](r), [N.sup.(2).sub.[epsilon],T](r). Benefit of these estimators is that they suppress the effect of high correlation of pairs of points belonging to the same edge.

Another approach to characterizing the grain boundary structure as a marked fibre process consists in investigating several types of special boundaries dominantly influencing properties of the material. Lei us suppose that each point of the grain boundary network is given a categorical mark Z(x) [member of] L = L {1, 2, ..., m} which is constant for points belonging to the same edge. For each pair of values i, j [member of] L, we define the cross K-function

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (10)

where [[lambda].sub.i] is an intensity function of a fibre subprocess of edges with the mark i [member of] L and A [member of] [B.sub.0] is an arbitrary bounded Borel set with positive Lebesgue measure [delta](A) > 0. To estimate Eq. 10 it is necessary to provide a segmentation of pixellated grain boundaries (Arnould et al., 2001; Jeulin and Moreaud, 2008). During this smoothing procedure, the grain boundaries are identified as lines or curves separating different phases in the image. On the segmented fibre structure [Y.sub.s] we firstly define a simple intensity estimator of [[lambda].sub.i]

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].

Based on the set of test points [V.sub.i] [subset] {y [member of] [Y.sub.s] : Z(y) = i}, an estimator of Eq. 10 can be expressed as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII],

where [W.sub.[??]r] = W [??] [B.sub.r](o) is eroded window providing the edge effects correction and [V.sub.i[?]r] = [V.sup.i] [intersection] [W.sub.[??]r].

The K-function can be useful for interpretation of clustering of different types of grain boundaries. For instance, higher values of [K.sub.i,j](r) indicate clustering of edges with the mark j around edges with the mark i.

NUMERICAL RESULTS

The methods were applied to data obtained from EBSD of copper processed by equal-channel angular pressing. After 8 passes through the die, the samples were annealed for 10 hours at different temperatures -373 K, 423 K, 473 K and 573 K (Fig. 9). It is obvious that the temperature of annealing influences the microstructure.

To indicate changes in the average grain size, Table 1 summarizes the mean areas of grain profiles and their variation coefficients, i.e., ratio of the standard deviation and the mean area, in observed cross section.

Increasing tendency in average grain size also influences the lengths of the edges of the grain boundaries observed in the cross sections. Fig. 11 shows that the majority of them lies below 2 [micro]m (edges shorter than 0.5 [micro]m were excluded during the segmentation) but especially in the last sample even several times longer edges are present.

Histograms of disorientation angles in Fig. 10 indicate among others a high fraction of [summation]3 boundaries with [theta] = 60[degrees], mostly being so called twin boundaries with axis of rotation <1, 1, 1>, and [summation]9 boundaries with [theta] [??] 39[degrees]. However, in the microstructure of copper processed by 8 ECAP passes rather low-angle grain boundaries (LAGB's) and random high-angle grain boundaries (HAGB's) predominated. From Table 2 it is apparent that frequency of random HAGB's remains about 40-50% while frequency of twin boundaries rapidly increases when the material is annealed.

Observation of high frequency of twins is fully consistent with the investigation of the role of shear stress in formation of annealing twin boundaries in copper (Field et al., 2006). Field et al. revealed that the twin content in rolled copper with 92% reduction is significantly lower than that in any copper deformed by ECAP, regardless of the annealing temperature. (Molodova et al., 2007) found a very low thermal stability of pure copper processed by ECAP. They observed that in the microstructure of pure copper processed by 12 ECAP passes, large recrystallized grains can be already found even after annealing at 393 K for 10 min and 423 K for 2 min. In our study, the occurrence of large recrystallized grains was observed at all annealed temperatures with markedly local character.

Occurrence of high frequency of twin boundaries in the microstructure of annealed copper samples before loading in the creep testing machine can significantly influence creep behaviour. It was found (Watanabe and Tsurekawa, 1999; Watanabe, 2011) that a high fraction of strong low-[summation] boundaries is a key factor controlling intergranular brittleness. The control of intergranular fracture and intergranular brittleness can be achieved by reduction of random boundaries or conversely by increasing the fraction of LAGB's or special low [summation] coincidence boundaries resistant to fracture.

It is generally accepted that the damage near grain boundaries is one of the key factors controlling creep life because many cracks are initiated at grain boundaries and frequently major degradation phenomena in materials are subjected to the creep exposure. Furthermore, grain boundaries can influence creep behaviour of ultrafine-grained materials due to synergetic effect of additional operating creep mechanisms like grain boundary sliding (GBS), intergranular cavitation or more intensive grain boundary diffusion (Kral et al., 2012). Nevertheless the ability of GBS takes place more significantly at a random grain boundary compared with low-[summation] boundaries (Kokawa et al., 1981; Watanabe et al., 1984).

With the characteristics introduced in the previous section, we aim at characterizing the microstructure in a more complex view. The following results show that even though the marginal distribution of disorientation angles and the fraction of special boundaries can be similar, their arrangement in the grain boundary network can differ crucially. The second-order characteristics describe these aspects on the basis of correlations and clustering of different grain boundary types measured in a small distance radius. The quantities defined in Eq. 1 are estimated using a set of test points T given by all the grid points of the EBSD measurement and a fixed [epsilon] > 0 given by the grid step. The cross K-function Eq. 10 is estimated using a set of test points [V.sub.i] given by midpoints of the i-th type boundaries, i [member of] L = {3, 9, L, H}, where the marks correspond to [summation]3, [summation]9, LAGB's and random HAGB's.

Fig. 12 shows the estimators of the mark expectation [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] and the mark correlation function [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] of four examined samples. The first estimator is almost constant because for every step distance r plotted there, the set [[??].sup.(1).sub.[epsilon]T](r) contains almost every point x [member of] [Y.sub.[direct sum][epsilon]] [intersection] T and in this case the estimator equals to the unconditional mean mark with respect to T. With increasing annealing temperature, we can observe increasing trend of the mark expectation and decreasing trend of the mark correlation function. In other words, increase in the annealing temperature leads to more random arrangements of edges within the distance r in the sense of their disorientation angles.

In the next we aim at localization of the dominant [summation]3 boundaries near the other boundary types. Fig. 3 shows the estimators of the cross K-functions [[??].sub.3,3](r), [[??].sub.9,3](r), [[??].sub.L,3](r) and [[??].sub.H,3](r), which help to interpret the occurrence of [summation]3 boundaries in neighbourhoods of [summation]3, [summation]9, LAGB's or random HAGB's. While Table 2 shows the marginal proportions of different boundary types, these K-functions bring an additional information about their mutual positions in the structure. We see that in the samples with higher annealing temperature, values [[??].sub.3,3](r) and [[??].sub.9,3](r) are generally lower which indicates higher regularity of [summation]3 boundaries with respect to themselves or [summation]9 boundaries. On the other hand, the situation is different in the neighbourhood of less dominant boundary types where these functions are minimal for the low-temperature annealed 423 K sample but any clearly interpretable trend is missing here.

It was found (Molodova et al., 2007; Saxl et al., 2010) that application of ECAP method could lead to the formation of the bimodal or even multimodal microstructures. The bimodality can depend on an appropriate thermal or creep loading conditions. It is widely accepted that the co-existence of larger recrystallized grains in the bimodal structure can improve deformation behaviour and thereby a ductility of ultrafine-grained material by relaxation of the stress concentration, created by GBS, through plastic deformation inside of larger grains (Ma, 2003; Koch, 2003; Fan et al., 2006). By contrast, a very recent report on creep ductility of ultrafine-grained materials did not confirm general acceptance of this view (Sklenicka et al., 2012).

CONCLUSIONS

The present paper defines the grain boundary structure as a random marked closed set which is observable in a planar section with the use of orientation imaging microscopy. To characterize its spatial distribution, it is useful to extend the common attitude based on marginal distributions to the second-order analysis. Methods of estimation of the second-order characteristics are provided and their use on particular specimens of metallic material is shown. To reveal the dependency between disorientations as a function of distance in a stationary and isotropic structure, appropriate estimators of second-order characteristics of the marks are defined which suppress the effect of high correlation of the marks along particular edges.

The methods are demonstrated on grain boundary structures marked by the disorientation angle or equipped with categorical marks indicating specialness of boundaries according to their CSL type. The subsequent annealing of the microstructure of pure copper processed by 8 ECAP passes led to the formation of the bimodal microstructure containing high fraction of low-[summation] coincidence boundaries. The second-order characteristics provide an additional information about arrangements of different boundary types in the structure. Our results show that increasing temperature of annealing leads to decreasing tendency of [summation]3 boundaries to form clusters but more likely to be placed regularly or create longer paths in the microstructure.

doi: 10.5566/ias.v32.p65-75

ACKNOWLEDGEMENT

The research was supported by the Czech Science Foundation, project P201/10/0472, and Operational Program Research and Development for Innovations co-funded by the European Regional Development Fund (ERDF) and national budget of the Czech Republic, within the framework of project Centre of Polymer Systems (reg. number: CZ.1.05/2.1.00/03.0111). Special thanks belong to Jiri Dvorak for preparation of the samples and operation with the ECAP facilities. The topic of this paper was presented at the S4G Conference, June 25-28, 2012 in Prague, Czech Republic.

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ONDREJ SEDIVY ([mail]), (1) Viktor Benes (1), Petr Ponizil (2), Petr Kral (3) And Vaclav Sklenicka (3)

(1) Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University in Prague, CZ-18675 Prague, Czech Republic; (2) Department of Physics and Material Engineering, Faculty of Technology, Tomas Bata University in Zlin, CZ-76272 Zlin, Czech Republic; (3) Institute of Physics of Materials, Academy of Sciences of the Czech Republic, CZ-61662 Brno, Czech Republic

e-mail: sedivy@karlin.mff.cuni.cz, benesv@karlin.mff.cuni.cz, ponizil@ft.utb.cz, pkral@ipm.cz, sklen@ipm.cz

(Received August 9, 2012; revised February 4, 2013; accepted March 17, 2013)

Table 1. Mean profile areas in [micro][m.sup.2] and their variation
coefficients in the sample without annealing and four
annealed samples.

no annealing   373 K   423 K   473 K   573 K

    0.97        4.90    3.21    7.01   10.57
    0.01        0.20    0.26    0.29    0.12

Table 2. Frequency of selected boundaries in
microstructure of pure copper processed by ECAP
and subsequent annealing at different temperatures for
10h.

  specimen     LAGB's   [summation]3   [summation]9   HAGB's

no annealing    57.92           2.57           0.25    39.26
    373 K        2.50          35.83           6.75    54.92
    423 K        2.10          54.30           7.42    36.18
    473 K        2.89          41.43           6.15    49.53
    573 K        3.08          40.13           5.09    51.70
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Title Annotation:Original Research Paper
Author:Sedivy, Ondrej; Benes, Viktor; Ponizil, Petr; Kral, Petr; Sklenicka, Vaclav
Publication:Image Analysis and Stereology
Article Type:Report
Geographic Code:4EXCZ
Date:Jun 1, 2013
Words:5541
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