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At the early stage of milling, a number of agglomerate particles can be seen in Fig. 1(b) as pointed out in circle regions, and is almost not changed compared with the original powder before milling.
That is because the grain size is refining and internal strain is increasing during milling.
Milling time (h) Cold rolling results Rolling texture (<15o) Cube texture (<10o) Carnelian-150 rmp 3 Cracked -- -- 5 Succeed 87.92% 90.2% 24 Succeed 85.80% 89.5% WC-150 rmp 6 Succeed 81.37% 75.2% 24 Succeed 72.34% 32.6% 60 Cracked -- -- 84 Cracked -- -- WC-300 rmp 6 Cracked -- -- 12 Cracked -- -- 24 Cracked -- -- 48 Cracked -- -- Compared with the two samples after using TSA procedure as shown in Fig. 5, the fraction of cube texture in substrate of WC-150 rmp after 6 h milling is much lower than the substrate of Carnelian-150 rmp after 5 h milling, and the average grain size is only 28.1 μm, due to the pinning effect of WC particles during annealing process.

The raw materials comprised the 52.5 ordinary silicate cement, polycarboxylate-type high-performance water-reducer with reduction from 15% to 25%, machine-made sand and class-800 shale ceramisite for the aggregate of SFRLAC, natural sand and crushed stone with 20mm maximum grain size for the aggregate of ordinary concrete. the performances of shale ceramisite are shown in Table 1.
Table 1 Physical and mechanical perfomances of shale ceramisite Grain size (mm) ratio Bulk density (kg/m3) Apparent density (kg/m3) 1h absorption (%) Mud content (%) Tube-compressive strength (MPa) 5-10/10-16/16-20 4.5/3.3/2.2 788.7 1333 6.47 1.3 3.15 Table 2 Basic information of specimens No.
For B5, B3 and B6 slabs, before failure with the increasing load, deflection tended linearly increased, cracks on bottom surfaces widened with more and more numbers, the failure took place gradually with the increasing load.

The major neck growth mechanisms for nanoparticles more than 10 nm in sizes are grain-boundary diffusion and surface diffusion [11].
To model the process a number of simplifying assumptions has been made, as follows: 1.
Nomenclature a particle radius (m) Db grain-boundary diffusion coefficient (m/s2) Ds surface diffusion coefficient (m/s2) Dv lattice diffusion coefficient (m/s2) Ef film Young’s modulus (GPa) k Boltzmann’s constant (J/⁰K) R gas constant (J/mol.⁰K) T absolute temperature (⁰K) Tm melting temperature (⁰K) x neck radius (m) y half of shrinkage (m) δb effective boundary thickness (m) δs effective surface thickness (m) εf film strain γ surface energy (J/m2) µ shear modulus (N/m2) νf film Poisson’s ratio ρ radius of neck surface (m) ρ0 initial resistivity Ω.m ρ(t) resistivity Ω.m σf film stress (MPa) Ω atom volume (m3) References [1] J.

By studying historical data related to the peasants' income, quantitative analysis using the following factors, agricultural output value, forestry output value, animal husbandry output value, fishery output value, possession of large and medium tractors, possession of small tractors, possession of diesel engines, generating capacity, grain yield, oilseed yield, these factors as input factors of peasants' income prediction.
Table 1 Index system of peasants' income prediction classification detailed index A The total output value of Agriculture, Forestry, Animal Husbandry and fishery A1 Agricultural output value A2 Forestry output value A3 Animal husbandry output value A4 Fishery output value B Possession of agricultural machinery B1 Possession of large and medium tractors B2 Possession of small tractors B3 Possession of diesel engines C Generating capacity C1 Generating capacity D Crop yield D1 Grain yield D2 Oilseed yield Application of SVM methods in the prediction of peasants’ income SVM prediction is looking for a nonlinear mapping function of input data are mapped onto the output space,φx:Rd→F, and linear estimation in high dimensional feature space.
（2）The training and test data format must be the same, the prediction sample data file format for the text, set each row represents the evaluation index value： < Pre_Value > = : : … < Pre_Value >is prediction values, to verify the accuracy; it is the number of the peasants’ income prediction result.

During straightening, this crack was caused by tensile stress, it was proper crack of niobium-containing steel, NbC and AlN were separated out from γ grain boundary, stress concentration was related to proeutectoid ferrite that was separated out from γ grain boundary.
FeO was the major ingredient in crack, and a small number of special elements of cast powder, such as Ca, Mg, Al, Si, Mn, S, F, Na.

Place of origin Type of coal ash Date of sampling Number of samples Počerady Powder FA 2013, 2014 4 Tušimice Powder FA 2013, 2014 3 Tisová K9 Powder FA 2014 2 Tisová K11+K12 Fluid FA 2013, 2014 8 Tisová K11+K12 Fluid FP 2013, 2014 8 Tisová K11+K12 Fluid MIX 1 2013, 2014 5 Tisová K11+K12 Fluid MIX 2 2013, 2014 5 There were researched following properties: · Chemical properties · Specific weight (ČSN 72 2071, ČSN 72 2080, ČSN EN 1097-7) · Loose bulk density and Shake down loose bulk density (ČSN 72 2071, ČSN 72 2080) · Content of free CaO · Granulometry · Volume Stability Chemical Properties and Content of Free CaO Chemical properties.
Type of ash Fluid FA K11 Fluid FP K11 Fluid FA K12 Fluid FP K12 Fluid MIX 2 Fluid MIX 1 d63,8[µm] 36.75 252.59 36.37 191.82 28.90 26.69 d50 [µm] 27.11 208.46 23.05 160.66 15.25 16.14 Powder FA are coarse-grained particulars than fluid ashes.
Fluid MIX 1 and Fluid MIX 2 are more small-grained than powder FA.

In general, residual stresses in a ground surface are primarily generated due to three effects: thermal expansion and contraction during grinding; phase transformations due to high grinding temperature; plastic deformation caused by the abrasive grains of the wheel [1].
But, there is workpiece burn under ground condition of working procedure 3 at Vs=120m/s and variables angle α=0 0, β=0 0. 3D structure of working procedure 5 shows inferior result with cut depth increased as grain fracture and fall out, which is said strength of high/ultra-high speed grinding wheel was inferior than overseas.
residual compressive stress have better exhibition, which owe to friction decreased between workpiece and grinding wheel when α, β angle bigger and grinding wheel thick too thin. 1.169 1.170 1.171 1.172 1.173 1.174 1.175 1.176 0.0 0.1 0.2 0.3 0.4 d-spacing (Å) sin ² (Psi) 1.169 1.170 1.171 1.172 1.173 1.174 1.175 0.0 0.1 0.2 0.3 0.4 d-spacing (Å) sin ² (Psi) Working procedure 1 Working procedure 2 1.172 1.174 1.176 1.178 1.180 1.182 1.184 1.186 0.0 0.1 0.2 0.3 0.4 d-spacing (Å) sin ² (Psi) Phi .0 1.155 1.160 1.165 1.170 1.175 1.180 0.0 0.1 0.2 0.3 0.4 d-spacing (Å) sin ² (Psi) Working procedure 3 Working procedure 4 1.12 1.13 1.14 1.15 1.16 1.17 1.18 0.0 0.1 0.2 0.3 0.4 d-spacing (Å) sin ² (Psi) Working procedure 5 Fig.5 The relation of sin2 (ψ) and d-spacing of 5 specimens Residual stress [Mpa] Fig.6 Residual stress vs. working procedure number

Nanocrystalline materials with grain size of less than 100 nm belong to a new class of materials which have received considerable attention in the scientific and industrial communities during the last decade [2].
A number of corrosion investigations have been reported on many multi-component Zr-, Tibased bulk amorphous alloys and conventional alloys [4-12].
It was also reported that the corrosion resistance decreases with increase in grain size and the presence of multi-phase structure [13,16].

Numbers at points − the content of the second phase in composites (% vol.); Р=1 N.
Increase in fracture strain of composites with the greater porosity (Fig. 3) have an effect on increase both intragranular and intergranular deformation in a material, i.e. elongation of separate grains and increase of quantity of intergranular pores (Fig. 4).
At the same time the high contents of the second phase in porous composites results in substantial growth of quantity of the grains which failed by the intragranular shear mechanism.

GA helps in identifying the best set of nanocomposites, this helps in reducing the number of combinations to analyse. 2.
[9] Venkatesan A, Gopinath VM and Rajadurai A (2005) Simulation of Casting Solidification and its Grain Structure Prediction using FEM.
[16] Lelito J, Zak PL, Shirzadi AA, Greer AL, Krajewski WK, Suchy JS, Haberl K and Schumacher P (2012) Effect of SiC Reinforcement Particles on the Grain Density in a Magnesium-based Metal-Matrix Composite: Modelling and Experiment.