Search results

The resistance method has been used for the study of metallurgy subject, such as phase diagram, phase transformation, lattice defect and grain boundary, etc[1~4].
The electron density is defined as (3) where N is the total number of free electron, V is the volume.
The average number of phonon is given by Eq. (4) (4) where k is Boltzmann Constant.
In the region above the liquidus temperature, the alloy is complete liquid, along with the decreasing of the temperature, the average number of phonon decreases.
When the temperature continues to decrease, the decrease of resistance is contributed only by the reducing of the number of phonon, and the second turning point on the R-T curve should appear as a result.

The required number of cycles needed for carbonization of the samples were was were 4, 2 and 3 s for CG, CH and PH, respectively.
Adsorbent porosity was evaluated according to iodine number, based on the sodium thiosulfate volumetric method [8].
The iodine number (IN) is a technique commonly employed to evaluate the structure of activated carbons and can be used as an approximation for surface area and microporosity with reasonable precision [4].
It is a measure of activity level (higher number indicates higher degree of activation), often reported in mg/g (typical range 500-1200 mg/g).
It can be seen from the micrographs that the external surface has cracks, crevices, and some grains in various sizes in large holes.

The corresponding samples were sequentially numbered for S-1, S-2, S-3, S-0, S-4, S-5 and S-6.
Number Raw material S-1 S-2 S-3 S-0 S-4 S-5 S-6 Alumina Wt.% 69.53 74.53 79.53 84.53 89.53 94.53 99.53 White sand Wt.% 15.47 15.47 15.47 15.47 15.47 15.47 15.47 Fig. 1 XRD pattern of white beach sand.
The samples of theoretical ratio were sintered at 1500 °C, 1550 °C, 1600 °C and 1650 °C for 3 h which were orderly numbered 1-S-0, 2-S-0, 3-S-0 and 4-S-0, and the XRD patterns were shown in Fig. 5.
Acknowledgment This research was supported by the Key Projects in the National Science & Technology Pillar Program (Grant No. 2011BAB03B08), the National Natural Science Foundation of China under grant number 51072186, the National Key Technology R & D Program under grant number 2009AA032501, and the Fundamental Research Funds for the Central Universities under grant number 2011PY0172.
Chan, Control of calcium hexaluminate grain morphology in in-situ toughened ceramic composites, Mater.

Experimental details The microstructure of the examined coatings has been thoroughly investigated in previous studies [3,4], revealing the dispersion of AlN and TiN nanocrystals with a grain size of 10 nm, in a Si3N4 amorphous phase.
During testing the friction coefficient was continuously recorded as a function of the number of sliding revolutions.
In order to estimate the effect of the gradient structure on the lifetime of TiN-based coatings, the duration of the first stage, expressed in number of sliding cycles, was experimentally determined for all the sliding speed values applied and the relevant lifetime factor (L.F.) was calculated, as below: (1) where (TiAlSiN): the duration, in number of cycles, of steady-state wear of the gradient coating, tested at vi sliding speed and (TiN): the duration, in number of cycles, of steady-state wear of the single coating, tested at the same vi sliding speed In Table 1, indicative experimental results are presented for three values of sliding speed; the improved tribological response of the gradient coatings is pinpointed, especially at higher speed values.
In this case, the multilayer character of the films was found also to have a beneficial effect on their wear lifetime, a fact that has been correlated with the repetition number of the essential Ti/TiN pair.
Sliding speed [m.s-1] Friction coefficient Wear track width [µm] Number of cycles to transition zone Lifetime factor TiN TiAlSiN TiN TiAlSiN TiN TiAlSiN 0.02 0.054 0.09 170 220 3420 8613 2.5 0.10 0.056 0.12 120 195 5223 15528 3.0 0.20 0.067 0.22 95 185 6472 22969 3.5 Fig. 3.

In [4] Haddar et al. present a procedure to obtain an initial configuration based on grain sizes and Monte Carlo simulations.
The analysis was continued until the number of load cycles exceeded 80000.
The number of load cycles and the corresponding crack lengths of 5 predominant cracks are depicted in Fig. 7. 5.
Fig. 5: Configuration after 85000 cycles Fig. 6 (right): Number of casting cycles and penetration depth of 5 cracks over the maximum crack length in the system.
E.g. if the leading crack penetrates 2 mm crack number 10 is about 0.6 mm long and the corresponding number of casting cycles is 17000 6.

Coarse aggregate gradation design index According to the theory of filling and interference particles, achieving maximum density, and forming effective embedded crowded, the gap which formed by the former level grain should be filled by the subprime particles, or else different sizes among particles will disturb each other.
The reason that choosing 16 as the level number factor is based on facts as follows.
In the case of a five-factor of the formula designing, the regression equation of degree of freedom should not be less than five, a level number at least fifteen.
Secondly, using the level number sixteen, the evenness deviation is smaller than that of using the level number 15, 17, 18, 19, 20.
Thirdly, the number of level is greater, the work is greater [4][6].

Table 2 The total solution of simulation rolling Sample Numbers Heating time/min Thickness /mm After cold rolling speed/℃/S Number 1 60 4.1 1（Air cooling） Number 3 60 4.3 20（Laminar cooling） Number 4 60 7.5 20（Laminar cooling） Number 6 100 4.2 20（Laminar cooling） Number 7 100 7.4 20（Laminar cooling） The whole rolling process demands half an hour, 80 mm thick slabs are rolled into 7 mm thick rolling plate 2 blocks, 4 mm thick rolling plate 3 blocks.
Metallographic test.In order to study the influence of heating time on the mechanical properties of HG70C, select No.6 and No.7 to do metallographic examination to observe its morphology.The samples were cut to 25mm×15mm×10mm cube specimens with abrasive wheel cutting machine.After grinding, polishing and 4% nitric acid alcohol etching the polished surface,and the results using metallographic microscope to observe are shown in Fig. 8.9. 500 times 1000 times Fig.8 Simulation rolling of No.7 sample By metallographic examination, a large number of inclusions are found in 7 mm plate No.7 sample, but the inclusions distribute more uniformly,particles are not very large,generally located at the grain boundaries. banded structure appears in 4mm sheet No.6 sample,and at high magnification(1000times),the shape is slightly different with No.7(presence of the sheet-like martensite, shown in the black stripes similar to the thin line portion). 500 times

Wetland restoration was conducted by returning the grain plots to grassland.
The method of calculation was as follows: microbial number from 1g soil equal to colonies average number multiply diluted times.
The numbers of each layer after restoration was much bigger than those correspondence before.
Thus we suppose that quantity of microbial number have a greatly change before and after restoration
Number of Microbes and Its Biodiversity in Soil of Riparian Wetland [J].

The main production processes is illustrated by figure 1: Electrolyzer Furnaces Holding furnace Casting merchine Rolling Furnaces Aluminia cryolite electrifing Furnish, solid waste electrifing Degassing, deslagging grain refining Casting , cooling Hot rolling, cool rolling, molding Aluminium ingot Figure 1.
In order to demonstrate simply, this paper considers the scheduling of a single melting furnace and assumes a furnace corresponding to a casting machine. 1) Notation :the number of orders,; :the number of charge,; :the cleaning cost between the charge and ; :the cost of mold changing; :the advancing or slowing penalty coefficient; :the open-order penalty; :the delivery date of the order; :the weight of open-order for the charge ; :the weight of the order ; :the ingredient of the order in the charge ; :the width coefficient of the order in the charge ; :The maximum capacity of furnace.
,is the coefficient of the penalty 2) Mathematical model Before establishing the model, this paper makes the following assumptions: (1) the smelting and the casing are measured in charges; (2) the number of charge is known;(3) the melting and the casting is the one-to-one correspondence;(4) the maximum capacity of furnace is a constant;(5) the weight of single order is less than the capacity of furnace, and they can not be decomposed;(6) the penalty coefficient of tardiness or advance are same in all the orders
(2) (3) (4) (5) (6) (7) The objective function (1) indicates minimizing the total cost involved set-up cost, open-order penalty and tardiness penalty; the constraint (2) indicates an order can only be arranged in a charge; the constraint (3) indicates orders in the same charge must have same ingredient; the constraint (4) indicates orders which are in the same charge must have same width coefficient; the constraint (5) indicates the total weight of orders are less than the furnace capacity; the constraint (6) indicates the weight of open-order can not be negative; the constraint (7) indicates the total number of charges is .
At the same time, in order to ensure the full utilization of smelting equipment, it is necessary to increase batch processing, reduce the number of charges, minimize the number of cleaning furnaces and save the preparation time on the adjustment of equipment. 4) Integrated production scheduling The processes of aluminum production are worked closely in high temperature.

These layers usually consist of an input layer, a number of hidden layers, and an output layer as shown in Fig. 1 [8].
Various network combinations were initially investigated and a number of trial runs were performed.
Different number of hidden layer neurons and number of epochs were examined.
Comparison of the JPCP ANN Model and MEPDG Model Goodness of Fit Statistics Parameter ANN Model MEPDG Model n 184 184 Sy 0.664 0.964 Se 0.275 0.620 Se/Sy 0.414 0.643 R2 0.828 0.584 n = number of data points, Sy = standard deviation of the measured IRI values, Se = standard error of estimate (predicted) IRI values, and R2= coefficient of determination.
Neuromechanistic-Based Modeling and Simulation of Constitutive Behavior of Fine-Grained Soils.