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Here, a continuum microstructure is bitmapped onto a 2D square lattice, initially taking the form of a matrix populated with random numbers ranging from 1 to Q, with Q representing the number of probable orientations shown by the grains in the simulated microstructure.
At this juncture, a random number (r) is generated which is uniformly distributed between 0 & 1.
Fig 10 suggests the relationship of R(lim) with the percentage of particles (φ) interacting with the grain boundaries, in simulated microstructures. φ was computed, through coding, by counting the number of impurities physically in contact with the grain boundaries.
The number of MCS which resulted in grain growth stagnation is also given alongside and it peaks at KTs=0.4, the critical temperature.
They clearly demonstrate (i) the log-normal behavior of the grain sizes atFig. 12: Grain Size Distributions for pinned regimes at different values of KTs, at N=1000, Q=64 and f=0.1 various values of KTs, and (ii) that the number of grains at stagnation dwindles as KTs are increased, because of enhanced grain growth.

A large number of strain-induced subboundaries with low-angle misorientations appeared at early deformation.
A great number of planar low-angle boundaries evolved at relatively small strains and form well-defined banded structure during MDF and ECAP (Figs. 2a and 2d).
At early stage of deformation, the formation of a large number of strain-induced low-angle boundaries occurs in the AT samples (Figs. 3a and 3d) that is similar to the ST samples.
On the other hand, the AT samples subjected to ECAP are characterized by almost linear relationships of FHAB and FUFG with number of passes.
Acknowledgements The financial support received from the Ministry of Education and Science, Russia, under Grant No. 14.575.21.0005 (ID number RFMEFI57514X0005) is gratefully acknowledged.

After solving the quantity problem of grain, the quality of grain becomes more and more serious.
Because of the complexity and wide application of the grain, it will be a long-term process to establish a scientific evaluation of quality index for grain, achieve rapid detection of grain quality, utilize and pricing grain according to end-use functionality of grain.
NIR technology is routinely used to determine the protein content of wheat to allow breeders to screen large numbers of lines for this key feature from early generation breeder samples [4].
The breeder can screen large numbers of anticipated varieties “identify” quality according to targeted customer preference.
Grain testing at mill intake is a crucial quality control step for grain processing.

Although a number of authors studied the influence of crystallography on GBF nucleation [2-7], it seems to be far from thorough understanding.
The occupancy fraction of these corners was ~55% although the number is too few to draw a definite conclusion.
A considerable number of precipitates at corners were in contact with a coherent twin boundary, as shown in Fig. 3.
Because it was difficult to determine which side of the twin was actually in contact with the other three grains and the number of possible combinations of grains forming a grain boundary increased rapidly, these corners were classified as h) in the table.
1.9/7.6 6.6/9.8 one P6 c 12.4, 38.4, 11.2, 33.1, 3.6, 31.9 4.1, 1.2, 27.6 1.5 6.6/1.7, 1.9/3.6 34.7/25.6 4.4/1.5 two U1 a 44.7, 56.9, 49.9, 54.8, 53.9, 23.5 three* U2 a 25.3, 54.0, 32.4, 48.3, 56.7, 28.5 one* U3 a 56.5, 44.0, 49.9 33.4, 57.3, 37.9 two* U4 a 36.7, 45.4, 48.0, 36.8, 57.1, 49.8 one* U5 b 50.0, 7.3, 53.9, 53.9, 50.2, 52.8 two* * These are the numbers of grains having the OR with the precipitate, if a hypothetical precipitate had the OR with one grain.

Each region is given a number corresponding to a grain orientation, grain boundary exists between block with different orientation.
The grain volume V is the number of site within a 3D grain, the average grain volume is the total number of lattice points divided by the number of grains.
The increase of the number of neighbours tend to reduce the grain size distribution and avoid the occurrence of abnormal grain growth.
Here we investigate the way the number of neighbouring grains influence the variation of volume of a grain.
For all the grains, we calculate the number of grains with different orientations connected to them and their variation of volume during 1 MCS (dV/dt) .

Influence of number of passes in ECAP on superplastic behavior in a magnesium alloy Roberto B.
The flow behavior was found to vary with the number of passes of ECAP.
However, the values of the elongations varied substantially with the number of passes of ECAP.
Grain structure.
The number of passes of ECAP has a major influence on the flow behavior, the rate of grain growth and the evolution of the strain rate sensitivity during deformation. 4.

During grinding a large number of micrometer or sub-micrometer grains remove material from the surface of a workpiece, acting as cutting tools.
They were able to determine the origins of lattice distortion of the substrate and the dependence of material removal rate on the ratio of sp2 to sp3 phases by conducting analyses of RDF and coordination number.
At a later moment, all grains are involved in the process, with the two back grains removing less material than the two front ones and forming chip in the area between the two rows of abrasive grains.
It can be seen that the number of atoms in a deformed lattice constitute a small part of the total number of atoms, as expected, because depth of cut is less than the 1/10 of total workpiece height and thus, deformation occurs at a few atomic layers beneath the surface.
The number of atoms belonging either to the plastic deformation region or the cutting chip range is larger with larger depth of cut.

This has led to the development of a number of commercially important Zr-containing magnesium alloys.
This complex behaviour of Zr on grain structure with the presence of intensive melt shearing could be explained by the change of the type and number of the heterogenous nucleating particles.
Therefore, when intensive melt shearing was applied to pure Mg melt, the grain structure was transformed from columnar grains to equiaxed grains, contributing to the increasing number of heterogenious nucleating particles (MgO).
Potent nucleating particles can be effective for grain refinement only if they have sufficiently high particle number density in the melt.
When Zr concentration is larger than 0.443%, the un-dissolved Zr particles may experience rapid coarsening under intensive melt shearing, resulting in a rapid decrease in particle number density.

Its grain output reached 111.41 billion kg in 2011; commodity grain output amounted to 90 billion kg and became the first major grain-producing province beyond Henan.
Meanwhile, Heilongjiang also ranks first in total grain output, commodity grain output and grain self-sufficiency rate. 30 billion kg of 90 billion kg commodity grain is contributed by the Heilongjiang reclamation.
The rate of its commodity grain is as high as 90% and the grain farmers make great contributions to guarantee national grain security in Heilongjiang reclamation.
It includes: the number of labor force, whether have cadre relatives or not, the number of underage children, the number of boys, the number of girls, the highest education in family and the number of non-healthy persons. (1) The number of labor force has a positive impact on farmers’ income.
It includes: the number of land blocks, sown area, the proportion of plain land, deeply plowing the land and land Huancha. (1) The number of land blocks has a negative impact on farmers’ income.

Concerning grain growth, the statistical model is based on the assumption of [10]: · Super-position of average grain curvatures in individual grain boundaries; · Homogeneous surroundings of the grains.
The integration of all the above assumptions in the model leads to the following final form of the grain growth rate equation: dRidt= Mj1Rj-1Rinj4πRj2jnj4πRj2 (1) Where Ri [cm] and Rj [cm] are the radius of grain belonging to class i and j and nj and nj are the total numbers of grains in class i and j.
The effect of reduction rate has been exploited maintaining the temperature constant at 1100°C and varying the cold reduction rate jointly to the dislocation density (Δρ) and the initial numbers of deformation nuclei (N) measured by means of X ray diffraction analysis.
The amount of strain affects also the recrystallization rate because the strain modifies the amount of stored energy and the number of effective nuclei.
Four temperatures, ranging from 700°C to 1100°C, have been simulated for AISI 304 steel grade while, at the same time, cold reduction rate (90%), dislocation density Δρ and the number of nuclei N were maintained constant.