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There are a large number of peaks from 700 to 1000, so it could be a period of crystallization [5].
The microstructure of three samples were compared, and samples C2 and C3 have obvious grain boundaries, while the grain boundaries of sample C1 cannot be seen clearly, which means sample C1 is over sintered, resulting in a great amount of glass phases.
In order that the crystal phases can be inferred, four typical micro domains in the selected area, shown in figure 6, were chosen for quantitative analysis. 1 ——Al2O3, CaO, SiO2； 2 —— SiO2； 3 ——Al2O3, MgO； 4 ——Al2O3, MgO, CaO, SiO2； 4 3 2 1 Fig. 6 The distribution picture of the ceramic sample Shown in figure 6, the micro domains are numbered as 1, 2, 3 and 4.

The Vickers microhardness was measured in the center of the grains using a micro-hardness tester Leica VMHT Mot applying a load of 1 gf for 10 s.
The grain centers used as locations for hardness measurement had been marked by indentations applying a high load of 500 gf at the etched specimen surface before the mechanical polishing.
The numbers of kinetic parameters which describe the considered phase transformation depend on the chosen model description.
The microstructure after quenching to room temperature exhibits a high number density of dislocations as observed with TEM: No other structural defects were found after intensive analysis [5].

Therefore, as the scale goes higher (lower frequencies), the sampling rate can be decreased thus reducing the number of computations.
However, due to the down sampling process the overall number of coefficients is still the same and there is no redundancy.
Coarse-grained parallel algorithms for multi-dimensional wavelet transforms: The Journal of Supercomputing Vol.12(1998), p.99-118,
Jamieson, in: Scalability of 2-D wavelet transform algorithms: analytical and experimental results on coarse-grain parallel computers, In Proceedings of the 1996 IEEE Workshop on VLSI Signal Processing, IEEE Publishers(1996)

Electroplated diamond wire saw applying ultrasonic vibration to cut polysilicon experiment and simulation analysis Zhang Liaoyuan1, a, Wang Shuo2,b and Shao Zhongqing3,c Lv Zhongxiu4,d 1Shenyang Ligong University, Shenyang , 110159, China 2Shenyang Ligong University, Shenyang , 110159, China azly6217@163.com bwangshuo_dream@126.com Keywords: Ultrasonic processing；polycrystalline silicon；Electroplated diamond wire saw Abstract: In this paper, on the basis of a large number of literature at home and abroad are summarized, we used the complete with independent intellectual property rights ultrasound wire-cutting processing machine tool to carry out machining experiment , and the ultrasonic wire cutting mechanism and process was carried out in-depth study.
Meanwhile, during the ultrasonic composite cutting process, abrasive contact with the workpiece is periodic in the tangential direction, there is a certain time in a period which has abrasive grains and the workpiece are separated, the interaction does not occur between the two. so in the whole process of cutting the tangential sawing force average will be smaller.Therefore, tangential sawing force under the action of ultrasonic is smaller than in ordinary cutting in the same conditions, the sawing force was reduced by an average of 20% to 30% in this test condition.
A number of micro cracks and pits are residual on workpiece, because the material is removed in a brittle mode, chip formation is result of the crack cross extension, eventually remove in microscopic crushing of granular form.

This paper describes an overview of a new meshless solution procedure for calculation of one-domain coupled macroscopic heat, mass, momentum and species transfer problems as well as phase-field concepts of grain evolution.
This fact is probably due to the highly convective situations with sharp thermal and solutal gradients, characterized by the low Prandtl (0.01), high Schmidt numbers (100), and high thermal (10e7), and solutal (10e11) Rayleigh numbers.
For the coefficients to be computable, the number of the shape functions has to match the number of the collocation points, and the collocation matrix has to be non-singular ( ) ( ) 1 ; l K l k l n l k l n l l k K
 ψ α = = Φ = ∑ p p .
The adaptivity with respect to the type of the collocation functions (P-adaptivity) is achieved by enhancing the number of the nodes in a sub-domain (Fig.3 center) or by adding additional functions to the collocation set (augmentation) at the constant number of nodes.
The method can cope with very large problems, since the computational effort grows approximately linear with the number of the nodes.

The relationship between the deformability and the number of the slip planes is similarly observed in the plane strain compression tests.
Underlined numbers in the parenthesis indicate the numbers of the observed slip planes.
Fig. 3 clearly shows the good correlation between the flow stress and the numbers of the activated slip planes, i.e. flow stress becomes higher with increasing the numbers of the slip planes.
This indicates that heavy cold deformation is expected to become more difficult with increasing the number of required operative slip planes.
Such extensive formation of the stacking faults must be closely related to the development of the parallelograms as well as the absence of equiaxed cells or sub-grain structure inside the parallelograms.

Here the powders are not fully melted and the increased temperature on the grain surface is responsible for the fusion of powders [6].
These processes can bring about desired improvements in porosity levels, precipitate phases, grain size, grain orientation and mechanical properties [69].
The ST time should be brief enough to limit grain growth and at the same time long enough to dissolve the unwanted precipitates.
High layer resolution although produces a better surface finish, the greater number of layers required to achieve the desired geometry are responsible for a significant increase in the product lead time.
High layer resolution can provide better surface finish but the build time increases due to the necessity of greater number of layers required to achieve the desired geometry.

The possible training parameters are number of iterations (epoch) learning rate, error goal and number of hidden layers.
However the numbers of neurons in the input layer and output layer are determined based on the problem domain depending up on number of input variables and number of output or target variables.
Tested: Properties tested BC Soil RHA Specific gravity 2.67 1.92 Liquid limit (%) 64.86 128.34 Plastic limit (%) 28.89 - Shrinkage limit (%) 11.48 - Plasticity index (%) 35.97 Non plastic Grain sizes Gravel fraction (%) - - Sand fraction (%) Coarse (%) 1.5 - Medium (%) 4 2.84 Fine (%) 12.54 31.45 Silt fraction (%) 26 52.35 Clay fraction (%) 55.96 13.36 MDD (KN/m2) 14.75 7.09 OMC (%) 26.53 74.24 Free swell index (%) 109.12 - Properties tested Value Grade 43 Specific gravity 3.12 Normal consistency (%) 32 Initial setting time (min) 96 Final setting time (min) 364 Fineness (%) 5.5 Compressive strength 3 days strength (Mpa) 25.5 7 days strength (Mpa) 36.7 28 days strength (Mpa) 41.5 Table 3 Chemical Properties of Soil Elements BC soil (%) RHA (%) SiO2 38.32 83.32 Fe2O2 2.69 0.8 SO3 0.034 0 CaO 3.05 0.71 MgO 2.69 0 Al2O3 5.93 0.8 Ignition loss 11.04 5.23 pH 8.06 9.69 Table 4 CBR of Soil-RHA-Cement
Mixes and RHA Mix proportion (%) Soaked CBR Soil RHA Cement (%) 100 0 0 1.88 (1.89) 95 5 0 4.08(4.08) 90 10 0 5.84(5.83) 85 15 0 5.14(5.36) 96 0 4 25.56(25.53) 92 0 8 48.57(48.53) 88 0 12 58.16(58.15) 91 5 4 28.16(28.18) 87 5 8 54.68(54.73) 83 5 12 63.09(63.04) 86 10 4 30.82(32.73) 82 10 8 60.56(59.11) 78 10 12 68.49(68.84) 81 15 4 25.85(25.85) 77 15 8 56.62(57.55) 73 15 12 67.06(67.34) Fig: 1 Illustrative topology to prognosticate CBR values BC soil- RHA-Cement mix Hidden layer CBR values Fig. 2 Effect of RHA and Cement on Soaked CBR Value of BC Soil Fig.3 Convergence curve The number of hidden layers and neurons in hidden layer are fixed during the training process.
A significant number of reports have been published in application of ANN for the prediction of future events in Civil engineering problems [1,2,3].

Fig. 6 shows the pixel array number dependency of Mn K fluorescence intensities of the three Fe-Mn alloy samples.
The number of detection pixel array was counted from the position at zero detection angle.
Therefore, the detection angle increases with increasing pixel array number.
As shown in Fig. 6(a), the fluorescence intensity distribution of the as-prepared sample increased monotonically with the number of pixel array (detection angle).
The roughness of the sample surfaces becomes larger with increasing the annealing temperature due to formation of manganese oxide and the grain growth at the surface.

It reduces the amount of Mg(OH)2, increase the area of the transition zone of grains, and reduces the amount of Mg(OH)2 which plays an important part in the expansion of cement paste
Based on Table 1, Table 2 and Fig.2 can be further obtained: Table 2 Influence of Content of MgO on Autogenous Deformation Serial number P1 P8 P10 P13 M 0 3 5 6 k1 －16.8 37.3 61.1 77.1 - 40 - 20 0 20 40 60 80 100 0 2 4 6 8 M( %) k 1 417.14455.151 − = M k Fig. 2 Relationship between k1 and M Based on the relationship between influence coefficient of MgO on autogenous deformation and content of MgO, Eq.3 can be obtained to calculate the influence coefficient k1, as follows: 417.14455.151 − = M k .
The influence of the content of fly ash on autogenous deformation are showed by Table 3 and Fig.3: Table 3 Influence of Content of Fly Ash on Autogenous Deformation M（％） 20 25 30 40 Serial number P5 P10 P16 P18 G（×10－6 ） PiG 54.1 61.1 68.2 63.6 Serial number P7 P12 P17 P19 G（×10－6 ） PiG 122.4 112.8 103.7 88.6 k2（×10 －6 ） 88.3 87.0 86.0 72.1 74 76 78 80 82 84 86 88 90 0 10 20 30 40 50 F( %) k 2 74.72446.1034.0 2 2 ++−= F F k Fig. 3 Relationship between k2 and F Based on the relationship of the influence coefficient of the content of fly ash on the autogenous deformation (F), Eq.4 can be obtained to calculate the influence coefficient k2, as follows: 74.72446.1034.0 2 2 ++−= FF k .
Table 4 Influence of Temperature on Autogenous Deformation G（×10 －6） G（×10 －6） G（×10 －6） G（×10 －6） T （℃） Serial number PiG Serial number PiG Serial number PiG Serial number PiG respective ratio 20 P2 45.2 P5 54.1 P10 61.1 P13 77.1 59.4 30 P3 100.9 P6 115.1 P11 71.7 P14 123.7 102.9 40 P4 111.5 P7 122.4 P12 112.8 P15 146.9 123.4 Turning the ratio in Table 4 into k3, we get the influence coefficient of temperature on the autogenous deformation at the temperature of 20 ℃, 30 ℃ and 40 ℃.