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However, when generating the mesh of real casting model, the cells need to Number is swept by 3D surface of the casting.
Fig.7 Filling mesh by outer grain grid note：in figure the curve stands for outline of casting Fig.1 Once search result Fig.2 The next search result If θ is greater than 90°, the grid is inside the triangle facet; and if θ is less than 90°, the grid is outside the triangle facet, and the grid is an invalid grid.
And all the grids between the seed grid and boundary grids are outer grids, and should be marked 9. 5) Continue scanning the whole mesh region, if there exists a grid neighboring to outer grids and has mark 0, take the grid as a new seed grid to do the searching process like that of procedure 4, and mark all the searched grids as number 9. 6) Repeat procedure 5 until there is not any seed grid.
The number of the total grids is 3500840, and the meshing time is 61s.
The number of the total grids is 3503360, and the meshing time is 65s. 3 Conclusion Boundary Search Method with its simple algorithm and high rate is an effective method to do DFM mesh generation.

The obtained effect is due to the formation of a large number of the substrates, which initiate the heterogeneous nucleation of silicon crystals.
Technology related concept of overheating the AlSi17Cu5 alloy The concept of producing fine-crystal structure by strong overheating of the liquid alloy is based on a number of methodological assumptions, taking also into account the technology of melting and casting.
Overheating above Tliq is accompanied by intense dissolution of silicon crystals and formation in the liquid of a large number of the microregions rich in silicon.
In these areas, favourable conditions occur for the formation of a large number of the long-range order states (clusters of Si-Si type).
The technological treatment that considerably contributes to the formation of fine-grained structure is overheating of alloy by about 250°C above the Tliq point.

Defect features such as dislocations, substitutional solutes and solute segregation to grain boundaries play important roles in influencing mechanical properties.
The Arabic numbers correspond to those in Fig. 1 Atomic Pair Condition IE (eV) ΔE(eV) 1-Ni6 Clean DC -1.35 -0.29 1-Ni6 Zr-doped DC -1.64 -0.29 1-Ni10 Clean DC -1.03 -0.68 1-Ni10 Zr-doped DC -1.71 -0.68 1-Al3 Clean DC -0.71 -0.29 1-Al3 Zr-doped DC -1.00 -0.29 Al3-Al4 Clean DC -1.50 -0.64 Al3-Al4 Zr-doped DC -2.14 -0.64 Ni10-Ni11 Clean DC -0.93 -0.02 Ni10-Ni11 Zr-doped DC -0.95 -0.02 where Nn is the occupation number for molecular orbital , , and is the Hamiltonian matrix element connecting the atomic orbital β of atom m and the atomic orbital α of atom l.
Usually, a negative number with a large absolute value means a strong interatomic interaction.
The Arabic numbers correspond to those in Fig. 1 4.

SupowerTM PCD cutters with 2-30 μm tool grain size were used in the machining tests.
Test number Cutting speed v [m/min] Feed rate fz [mm/tooth] Radial depth of cut aw [mm] No.1 250 0.15 3 No.2 250 0.1 2 No.3 250 0.05 1 No.4 150 0.15 2 No.5 150 0.1 1 No.6 150 0.05 3 No.7 50 0.15 1 No.8 50 0.1 3 No.9 50 0.05 2 The goal of this study is to optimize the milling parameters to get lower cutting force and cutting temperature so as to reduce the tool wear rate.
That means that the objective function, S/N ratio, is calculated based on the-smaller-the-better characteristic, which can be calculated as: (2) where yi is the observed data and n is the number of observations.
Table 4 Experimental results for cutting force and S/N ratio Test number No.1 No.2 No.3 No.4 No.5 No.6 No.7 No.8 No.9 Cutting force F [N] 888 646 369 836 577 740 764 911 859 S/N ratio [dB] 58.96 56.21 51.35 58.44 55.23 57.38 57.67 59.19 58.68 Table 5 S/N response table for cutting force Symbol Cutting parameters Mean S/N ratio [dB] Level 1 Level 2 Level 3 Max-Min A Cutting speed 55.51 57.02 58.51 3.0 B Feed rate 58.36 56.87 55.81 2.55 C Radial depth of cut 58.51 57.77 54.75 3.76 The non-linear multiple regression analysis method has been deployed to analyze the cutting forces in the form of empirical equation.
Table 6 Experimental results for cutting temperature and S/N ratio Test number No.1 No.2 No.3 No.4 No.5 No.6 No.7 No.8 No.9 Cutting temperature θ[℃] 699 520 321 408 385 473 341 506 347 S/N ratio [dB] 56.89 54.32 50.14 52.22 51.73 53.50 50.66 54.09 50.80 Table 7 S/N response table for cutting temperature Symbol Cutting parameters Mean S/N ratio [dB] Level 1 Level 2 Level 3 Max-Min A Cutting speed 53.79 52.48 51.85 1.94 B Feed rate 53.26 53.38 51.48 1.90 C Radial depth of cut 54.82 52.45 50.84 3.98 Fig. 5 S/N response graph for cutting Fig. 6 Cutting temperature for different temperature workpiece materials and tool wear (v=100 m/min, fz=0.08 mm/tooth, aw=1 mm, ap=3 mm) The non-linear multiple regression analysis method has been deployed to analyze the cutting temperature in the form of empirical equation.

Zirconium has been known to be effective in modifying the primary silicon particles and refining the grains.
Hardness number is taken as an average of 10 readings.
RSP alloys Mean (Vickers Hardness Number) S.D RSP446-2 113.2 ±2.2 RSP446-3 108.1 ±0.5 RSP461 137.3 ±2.7 RSP444 117.9 ±2.9 RSP431 106.0 ±2.0 461Deg.
As received sample number RSP 461 has the highest hardness of all extruded bars (see table 1).
Sample number 461 degassed and HIPped at 400°C for 3hrs has the highest hardness of all degassed and HIPped samples and indeed of all the test samples (including those subjected to extrusion or heat treatment).

The method is therefore suitable for materials with a relatively large maximum grain size (e.g., 5 mm), which requires huge specimen dimensions (e.g., 100mm edge length).
In the case of an odd number in the first column Young’s modulus was one of the parameters identified.
On the other hand, in the cases with even number Young’s modulus was measured by Resonance Frequency and Damping Analyzer (RFDA, IMCE in Belgium) at room temperature, which resulted in 81 GPa.
Number of parameters Evaluated parameters Curve type 2 ft, Gf linear 3 ft, Gf, E linear 4 ft, σ1, X1, Xult bilinear 5 ft, σ1, X1, Xult, E bilinear 6 ft, σ1, σ2, X1, X2, Xult trilinear 7 ft, σ1, σ2, X1, X2, Xult, E trilinear The influences of Young’s modulus on the identified results for the total specific fracture energy Gf and the tensile strength ft were analyzed as well.
The values attributive to the dots in figures 2 and 3 indicate the number of evaluated parameters.

Thereby, two various methods for evaluating the temperature susceptibility of asphalt binder is being applied named the penetration index and the pen-vis number.
Pen-Vis Number (PVN) PVN was determining the temperature susceptibility of asphalt binder, this is based on penetration at 25°C and viscosity at either 135°C or 60°C which is usually specification requirements for viscosity graded asphalt cements [13].
Table 4: The penetration aging index after aging Percentages (%) Unaged RTFO Aging Index RTFO/Unaged Control 94.93 52.46 0.553 2% BPSC 93.03 42.53 0.457 4% BPSC 90.67 47.96 0.529 6% BPSC 92.02 47.45 0.516 8% BPSC 89.54 48.66 0.543 Temperature susceptibility Table 5 listed the results of Penetration Index (PI) and Pen-Vis Number (PVN) for unmodified and BPSC-modified-asphalt binder.
Similar result had been obtained when scanning electron microscope captures confirm higher homogeneity of modified slag grains due to an increase of SiO2 [17].
[13] McLeod, N., Asphalt cements: pen-vis number and its application to moduli of stiffness.

Slow crack growth of the surface defects from oxidation at grain boundaries in the fibers has been shown to be the driving mechanism of delayed failure [6].
It depends on the size of the cluster defined by the number of touching adjacent fibers.
First of all, the fibers which are considered successively are selected randomly through 3 parameters (fiber section Si, elementary force f and strength f ) which were choosen using random numbers X, Y and U: X = PG (Si) (6) Y = PN (f) (7) U = PW (f) (8) Then, the overload that is experienced by the next fiber is also selected using the same method: Z = PN (f + f) (9) The microstructure is characterized by the distribution of cluster sizes (figure 4).
The force F operating on the longitudinal tows was derived by assuming that the force on composite was shared equally by all the longitudinal tows: F =  compF (10)  was taken to be the number of longitudinal tows within the specimen (150). 50 computations per force were carried out.
It was based on the number of tows present in the composite specimens.

From the FTIR spectra of HA-Zn (Fig.1.) it can be observed the presence of a small number of the absorption bands.
The most intense band in the apatites FTIR spectra is the band attributed to the stretching vibrations of P-O bonds in phosphate group (PO) in the area of 1000-1100 cm-1 wave number.
This band is accompanied by a secondary band due to the bending vibrations in the area of 500-600 cm-1 wave number.
SEM micrographs of the HA-Zn sample, at 100 °C, 300°C, 500°C 800°C, and 1000°C (x15 000) Brunauer, Emmett, Teller analysis (BET) The specific surface area of the powder is closely related to the size of the powder grains.
Preliminary results to determination of the effective atomic number

The Research of LED Lights Model Based on Road Lighting in Mesopic Vision Fangzhou Zhu *1, a, Weihai Yuan1,b , Huamei Xu 2,c and Yuefeng Yuan1,d 1Anhui Economic Management Cadres Institute, China 2 Anhui Vocational College of Grain Engineering, China afangzhou_zhu@126.com, byuanweihai@sina.com, cxuhuameimei@126.com, dyf.yuan@163.com Keywords: LED, the white and green LED, human visual characteristics, Mesopic vision.
The road lighting high pressure sodium and metal halide lamps, and many scholars in the middle of the visual state of high pressure sodium, metal halide light efficiency, a number of outputs [5].
The feelings of the human eye on the overall intensity of the LED light source can be sensitive visual intensity of each color cell integrated into a synthesis of the impulse to pass into the brain, its LED light source with the optical model shown in Fig.4: Fig. 4 LED light distribution model Fig.4 can be seen R, G and B present three primary colors of red, green and blue intensity of stimulation, M said that the human eye cone cell sensitivity coefficient, T1, T2, T3, pyramidal cells of the human eye on the three primary colors corresponding sensitivity, log used to simulate the human visual pyramidal cells in the visual response curve to match the sensitivity to take on the number of log operations, N said that the human eye rod cell sensitivity coefficient, C1,, C2, C3, respectively, the contrast in black and white, red and green contrast, yellow-blue contrast, , , are filtering function, Y is the brightness, B1and B2 are Chroma.
When the white and green LED is used for road lighting, it ensures the safety of a large number in energy saving.
Acknowledgements The young talents Fund project of Anhui province in 2010( Number: 2010SQRL206).