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Thermodynamic data for the system was from the TTTi3 database and the kinetic diffusion coefficients of hydrogen in α-Ti, β-Ti and TiHx were, respectively, calculated by (m2/s) [5] (1) (m2/s) [6] (2) () (m2/s) [7] (3) According to the Ti-H phase diagram, four typical temperature ranges (under 573K, 573-956K, 956-1155K, and above 1155K) were identified based on the phase transformation characteristics.
Fig. 4 Change of α-Ti volume fraction in the sheet with 0.003 wt% initial H Fig. 5 Variation of moving velocity of α/β boundary with average H content The addition of hydrogen to a series of α and α+β Ti alloys can greatly enhance their superplastic forming capabilities due to the reduction in the β-transus temperature and the presence of a two-phase microstructure at temperatures at which the hydrogen-free alloy is normally predominantly single phase [8-9].
Due to the low H diffusivity in TiHx and the high diffusivity in the β, the hydrogen-concentration gradient in the TiHx increased with the reduction of the TiHx thickness, resulting in the increase in the hydrogen diffusion and, therefore, the increase in the growth of the β phase (Fig. 7).
The thermodynamic and kinetic data reported by Li et al. [12] for the Ti-H system were used.
With the simulation of the hydrogen diffusion between titanium sheets and the analysis of the hydrogen diffusion in titanium particle packings, the diffusion data and the behavior of phase transformation in different phases, including the diffusion velocity, the phase growth rate and the diffusion end time, were discovered for the thermohydrogen process of titanium to improve its machinability.

It follows that the principle of forming a multilayer coating can be formulated only if we take into account data on coating damage resulting from cutting, as well as data on temperatures and stress in the tool’s cutting wedge. 2.
The upper layer of such a coating is to provide maximum reduction of equivalent stresses in the cutting wedge to increase its stability of shape and to ensure high residual compression stresses in the coating.
Thus, the highest compression stresses in coatings during cutting and at idle are to be expected of those coatings, which have higher endurance properties and higher residual compression stresses, while the most effective temperature reduction and the narrowest temperature range are provided by coatings with lower endurance and lower residual stresses.
Our study of fracture-resistance in various coatings during turning and end milling operations has supplied data which supports the above-formulated principles of forming multilayer coatings.

From the data obtained during unloading of the indentation, elastic displacements can be determined and from these measurements the elastic modulus, E, can be calculated.
Table 2 exhibits a relatively small scatter for indentations in the matrix (non-segregated regions), but the data for the segregated display much wider scatter.
Fitted stress–strain curves obtained from the raw data of the Gleeble tests are shown in Figure 3 (a) and (b) for samples from region 1 (a region near the slab surface representing dendritic structures), and region 2 (highly segregated areas from the quarter thickness region, representing inter-dendritic structures) respectively at testing temperatures 800, 900, 1000 and 1100°C.
Ductility was determined by measuring the reduction in area in the tensile tests.
The reduction in area of segregated and non-segregated regions is shown in Figure 4 (c).

It can be seen that Mg (JCPDS Powder Diffraction Data Card N° 35- 0821) and Ni (JCPDS Powder Diffraction Data Card N° 04-0850) peaks are well defined in the sample after 2 h of MA (Fig. 1A).
Table 1: Crystllite size d (nm) and average strain (%) of the different phases as a function of milling time In the case of Mg, the peak broadening is associated with both crystallite reduction and strain introduced in the material during milling and with the formation of an amorphous precursor of Mg2Ni.
These changes involve peak broadening and intensity lowering, as it is demonstrated by crystallite size reduction and strain increment (see Table 1).
Nanocrystalline Mg2Ni (JCPDS Powder Diffraction Data Card N° 35-1225) is formed after 200 h MA (see Fig 1 C and Table 1).

Reduction of storage energy is driving force and grain boundary plays as restrainer.
The interior grains follow coarsening mechanism and the reduction of grain boundary is driving force.
Experimental 0.0 0.2 0.4 0.6 0.8 1.0 1 10 100 1000 Time (sec) Volume fraction recrystallized, fx Calculated Experimental Fig.1 Predicted and experimental recrystallized grain size and volume fraction of 304L steel 0 0.5 1 1 10 100 1000 10000 Time [s] Fraction recrystallized, fx x o ∆∆∆∆ Experimental __________ Calculated Tdef =1050 o C ε=0.40ε=0.40ε=0.40ε=0.40 dε/ε/ε/ε/dt=2s -1 doγ =180µm 300µm 520µm 0.0 0.2 0.4 0.6 0.8 1.0 1 10 100 Time (sec) Volume fraction recrystallized, fx Calc 1000C Exp. 1000C Clac 1100C Exp. 1100C 0 0.5 1 1 10 100 1000 10000 Time [s] Fraction recrystallized, fx x o ∆∆∆∆ Experimental __________ Calculated 0.2 0.1 Tdef =1050 oC doγγγγ =220µµµµm dεεεε/dt=2s -1 ε=0.4 0.0 0.2 0.4 0.6 0.8 1.0 1 10 100 Time (sec) Volume fraction recrystallized, fx Calc 0.5s-1 Exp. 0.5s-1 Clac 5s-1 Exp. 5s-1 Fig.2, Verification of initial grain size and strain effects, experimental data
from Siwicki [9] Fig.3, Verification of temperature and strain rate effects, experimental data from Cho [10] Results and Discussion The austenite stainless steels were selected to verify the recrystallization model, because there is no phase transformation during cooling of the samples from high temperature.
The initial austenite grain size and strain effects are verified by experimental data of 317LMN steel from Siwicki [9] (Fig.3).

The x-ray diffraction patterns in Fig. 1 show that SrFe12-xInxO19 samples with x = 0,05; 0,1; 0,2; and 0,5 are well fitted along with a fitting curve on each diffraction trace, these are indicated by flat residue data within the scanned 2-theta diffraction angle.
Table 1 shows the result of the XRD refined data analysis.
Result of XRD Refined Data Analysis of SrFe12-xInxO19 with x = 0,05; 0,1; 0,2; and 0,5 X Lattice parameters SrFe12O19 (Å) V (Å3) r (g/cm3) Weight Fraction a b c SrFe12O19 Fe2O3 0.05 5.8830(3) 5.8830(3) 23.044(1) 690.7(1) 5.469 100 % 0 % 0.10 5.8852(3) 5.8852(3) 23.053(1) 691.5(1) 5.463 100 % 0 % 0.20 5.8886(3) 5.8886(3) 23.068(2) 692.7(1) 5.453 81.23 % 18.77 % 0.50 5.9020(4) 5.9020(4) 23.131(2) 697.8(1) 5.413 67.91 % 32.09 % Magnetic and Microwave Absorption Properties.
Whereas the reduction of coercivity must be due to reduction of magneto crystalline constant of magnetic phase as the Ti4+ ion octahedral sites which effective to reduce the anisotropy constant [11].

XRD results are supported by the data of BET analysis summarized in Table 2.
The SSA measurements show primary particle size increasing for low concentrations of incorporated Sr followed by reduction for higher concentration of Sr incorporated into the HAp lattice.
Table 2 BET data of the Sr-substituted HAp powders Sample designation SSA [m2/g] dBET [nm] 1_HAp 47 40 1_HAp_Sr2 45 42 1_HAp_Sr10 54 35 2_HAp 47 40 2_HAp_Sr2 43 44 2_HAp_Sr10 47 40 3_HAp 82 23 3_HAp_Sr2 79 24 3_HAp_Sr10 83 23 The samples were collected in the form of a suspensions after synthesis to investigate the morphology of as-synthesized Sr-substituted HAp powders using SEM.
Brunauer–Emmett–Teller data showed an initial increasing of primary particles followed by a reduction with higher concentration of Sr substitutions in the products.

Nervous fatigue manifests itself by: reducing accuracy and movement speed reduction reduced visual precision, reduction reactions, headache, dizziness and insomnia.
Table 1 – The set of data Training Testing Parameters 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 Temperature [ºC] 21 40 40 25 22 50 46 21 33 15 9 23 37 30 25 40 Humidity [%] 40 55 60 35 40 90 80 40 67 40 67 40 75 55 30 55 Noise [dB] 20 60 50 50 40 110 100 60 70 60 40 40 90 60 40 60 Lighting [lux] 600 200 400 500 800 30 40 300 125 125 100 600 60 200 600 125 Ranking of workspace 1 0.4 0.5 0.9 1 0 0.1 0.7 0.3 0.5 0.2 0.9 0.2 0.5 0.9 0.3 The mains parameters of training are: · The learning rate =0,60; · The target error =0,1; For the test parameters, the network predicted the following values: 0.5à 0,5575; 0.2à 0,0996; 0.9à 0,877 and 0.3à 0,5543.
The values predicted by the neural network according a set of data For a complete view of the analyzed workstation, it is necessary to provide to the actors involved in the redesign process a dashboard with all the tasks realized at the workstation, by the human operator, figure 5.
As it can be seen from the experimental results, the designed feed forward neural network recognizes very well the test data.

This allowed a reduction in sewage water volume to an amount less than the ambient consumptive capacity.
In accordance with the principle of "reduction, resources, harmless", it improved the efficiency of the wastewater recycling process.
It collects data and transports the information to the remote monitoring center.
It transmits data through advanced 3G technology.
In order to ensure data security and real-time operations, all of the substation equipment is logically divided into a multiple segments relationship.

The seam weld metal generally undergoes an increase in tensile and yield strength at the expense of a sever reduction in ductility [6,7].
Fatigue data is limited for titanium laser welding however for applications such as dental prosthesis and stents this is an important property.
Roggensack et al. demonstrated a significant reduction in fatigue strength for laser welds in the order of 40% [10].
Bend tests however give limited elongation data and little work has gone into comparing notched and un-notched tests.
These problems make it hard to infer comprehensive mechanical property data.