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In Fig 1.a grains have a distribution of strip perpendicular to the sintering pressure, Fig 1.b grains looks like small ball.
From Fig 1.b it is clearly that in the surface of the sample has a flaw, the reason is that when MoS2 join in the ball mill, a large number of energy will gathered in the surface of powder particles, the particles become unstable which lead to a violent reaction between MoS2 and Cu in the process Figure1.
SEM morphology of sintering sample with different mixing method a.MoS2 not join in MA b.MoS2 join in MA of sintering, resulting in the change of grain size, then the sintering body will expand, when it expand to a certain degree, the body will be cracked, this appearance is called ‘densification phenomenon’ in the field of powder metallurgy. 3.2 The influence of molybdenum disulfide content on the composite structure According to the above analysis ,can get a result that MoS2 can’t join in MA, so when research the influence of molybdenum disulfide content on the composite structure, MoS2 was mixed in the conventional method.
Conclusion · When MoS2 join in the MA process together with copper and graphite, a large number of surface energy will gather in the surface of powder particles which can lead to sharp reaction between copper and molybdenum disulfide, and ‘densification phenomenon’ will happen, so molybdenum disulfide should be mixed by conventional method

In this article, we will propose a common fitting method to separately solve the relationship between hardenability coefficients and end-quench distance and alloying elements (include grain size).
The form of the data is like , while m represents the number of the samples and n is sum of the hardness values in different end distance and the alloying elements number (). 2) To calculate the basing on the carbon content in each row and Eq.2 we have talked about. 3) To figure out , and .
During this step, we use nonlinear fitting based on the hardness values with different end distances, referring to Eq.1. 4) For the entire sample set, the independent variables are the grain size and the alloying element.
In Eq.1, let , we can get: (5) So, the formula showed below becomes correct: (6) (7) (G is the grain size level, M represents the alloy equivalent and C is the carbon content.)

As an efficient method to refine grains and improve strength through shear deformation, equal channel angular pressing (ECAP) has been widely used in structural material.
Recent some studies indicate: ECAP can both improve the mechanical property and shape memory effect of NiTi alloy by refining grains [5-6].
Original coarse equiaxed grains become flat elongated along the shear direction and seem slender ribbon.
It can be seen that there is no significant change in the number and size of phase which hard and brittle organization (Ti, Nb) 2Ni after one-pass ECAP deformation.
After two passes extrusion, (Ti, Nb) 2Ni phase was crushed by the extrusion pressure and the volume of (Ti, Nb) 2Ni phase become smaller and the overall number of the original organization presents a decreasing trend.

Table 1 Technical parameters of the membrane double ditch planting corn upright type precise dibbling machine Parameter Value The power of form a complete set (kw) 2.82 Engine speed (r/min) 3000 The whole machine weight (kg) 170.8 The whole machine size (mm) 1700×545×1090 Membrane specifications (mm) 0.008×1200 Hole distance (mm) 330 Plant spacing (mm) 400 Operating speed (mm/s) 500 Each hole row of kind of grain number (grain) 2 Sowing depth (mm) 30~50 The efficiency (hm2/h) 0.18 Design of power transmission system project Because the route of transmission of the self propelled machine is the engine transmission’s, the speed, which is 3000 r/min, is too high, so it must be passed through retarding mechanism to meet the requirements.
Table 2 Transmission ratio and other related parameters Level Transmission ratio Speedr/min Number of teeth Specification Position Level 1 1:4 750 Z1=17 Z2=68 Spur gear m=2 The chassis Level 2 1:4.125 181.818 Z1=8 Z2=33 Sprocket wheel 12A The chassis Level 3 1:4 45.455 Z1=8 Z2=32 Sprocket wheel 12A Land axle Level 4 2.4:1 109.091 Z1=48 Z2=20 Sprocket wheel 08B Axle for cam Level 5 1:1 109.091 Z1=20 Z2=20 Sprocket wheel 08B Axle for crank Level 6 1:2 54.545 Z1=16 Z2=32 Sprocket wheel m=2.5 Sowing axle Dynamics simulation analysis based on ADAMS Import of the Model.
Acknowledgement A “Research and demonstration of agricultural machinery and agronomy key technologies integration for grain crop” project supported by “the 12th Five-Year Plan” National Science-technology Support Plan (2013BAD08B01); A “Research and demonstration of Agro-Pastoral circular technologies integration in North-west oasis area” project supported by “the 12th Five-Year Plan” National Science-technology Support Plan (2012BAD14B10).

This paper presents a Tree-ART2 network model, through the adjustment of weight function and the learning of space distance, as well as adding norm information controlled, while learning new knowledge we also keep the memory of the old patterns in order to keep the training stable under the clustering criterion of high vigilance, fine-grained and to reduce the subjective requirement of network vigilance parameter setting by introducing the optimization structure of cycle tree.
The introduction of the cycle tree is structured to gather once again in fine-grained network class, it gets the optimization cluster class Topt at last.
Compared experiments and analysis The set of network parameters This experiment selected the annotation of land type with coordinates of location in two-dimensional vector which has 7290 in number.
The compared parameters in ART2 and TART2’ neurons network are below, as Table 1 show: Table.1 Network Parameters The name of parsmeter The value of parameter The name of paremeter The value of parameter A 10 E 1×10-6 B 10 rphase 0.999 C 0.1 rnorm 0.05 D 0.9 Θ 1/n1/2 Note: n=the number of input vector Experimental analysis Fig. 2 compared with Fig.3, we can know that ART2 classify the 7290 annotations into twenty-one class based only on phase information.
First, high vigilance and fine-grained cluster are taken, then adopting the mechanism of optimization in cycle tree structure to reduce the incidence of mixed mode delivery.

The internal stress between the deposit and the basement is inexistent with the tiny granules and well-proportioned grains at the coating surface.
Where 2θ≈30°, there is a strong diffraction peak compared with 2θ≈51°, the conjugated peak is the symbol of grain growth orientation.
The pH changes show that numbers of hydrogen ions are very small in solution, the hydroxyl will be depleted in the alkaline carbonate solution, as formula (1-4) and (1-5).
Both the obtained alloys coating surface is smooth and compact tiny grains for cauliflower construct.
And the body-centered cubic (BCC) and face-centered cubic (FCC) alloys have a different close-grained density, so the formed alloys microhardness is related with the original deoxidized element.

It is well known that the sensing properties of SnO2 based materials depend on their chemical and physical characteristics, which are strongly dependent on the preparation conditions, dopant and grain size.
The initial electron density at each pixel is fixed uniformly as F000/a03=1.8265 e/Å3, where F000 is the total number of electrons in the unit cell and a0 is the cell parameter.
Parameters SnO2 Number of cycles Number of electrons in the unit cell Number of pixels in the unit cell Lagrange parameter (λ) RMEM (%) wRMEM (%) 2079 132 73728 ( 48 × 48 × 32) 0.001757 1.51 1.70 Table 3: Parameters from MEM refinement Figure 2 shows the 3D electron density distribution of SnO2.
System Particle size (SEM) Grain Size** (XRD) SnO2 134 nm 20.4993 nm Table 6: The average particle size from SEM and XRD [22].
[21] Jean Laugier et Bernard Bochu: GRETEP, Domaine universitaire BP 46, 38402 Saint Martin d'Hères http:/www.inpg.fr/LMGP [22] R.Saravanan: GRAIN Software available at http://www.saraxraygroup.net/

Original lower control arm (a) CAD model of the original component (b) Results and Discussion Considering samples NH1ꓕ and H2ꓕ, CT analysis were performed to evaluate number and average dimensions of pores before and after HIP treatment.
Results of CT NH1ꓕ H2ꓕ Analyzed Volume (V) [mm3] 3.016×103 3.016×103 Number of pores 88 0 Average equivalent diameter [μm] 66.6 Total porosity (Vp/V) [%] 5.04×10-4 Non-treated sample presents a relatively high porosity, this is drastically reduced by HIP treatment: in HIPed sample the number of detected pores in the considered volume is zero.
SEM and XRD analysis of the cross section of the samples were carried out to determine the microstructure of as-built and post-HIP samples: both samples present columnar grains oriented along the building direction (Figure 4 (a) and (b)) and a Widmanstätten type structure, which consists in colonies of lamellar α phase separated by a fine layer of β phase (Figure 4 (c) and (d)).
The samples present a structure made of columnar grain oriented along the printing direction with a lamellar Widmanstätten microstructure.
CT analysis highlighted a high degree of porosity in as-built samples (regardless of building orientation) which can be eliminated with a HIP treatment, this treatment also leads to larger α grains.

Fine-grained tetragonal zirconia polycrystals containing 3mol% yttria (3Y-TZP) is a typical superplastic ceramics having a very high ductility at elevated temperatures.
Experimental A fine-grained 3Y-TZP ceramics having initial average grain size about 0.4x10-6m were used in this study.
Average (MPa) Number Standard deviation (MPa) Bending 902 37 98 Compressive 3374 14 130 0 1000 2000 3000 4000 0 0.2 0.4 0.6 0.8 1 deflection of crosshead (mm) stress (MPa) 6.0 6.5 7.0 7.5 8.0 8.5 9.0 -6 -5 -4 -3 -2 -1 0 1 2 lnln(1/Sj) ln σσσσs Test Specimen : 3Y-TZP Bending strength average : 902MPa Test number : 37 Compression strength average : 3374MPa Test number : 14 m=9.5 m=26.5 It indicates that the 3Y-TZP can be treated as a brittle material same as graphite.
Table 4 Design stress limits for core component made of 3Y-TZP The safety margin of the 3Y-TZP was evaluated by comparing the average stress of the data avσ and the design stress limit slσ as follows: td slav nS<−σσ (3) where tdS is the standard deviation of the data and n is a number.

Equal Channel Angular (ECAP) pressing has been showed as an attractive route to produce fine and ultrafine-grained metals and alloys with high strength and fracture toughness.
Introduction ECAP is considered one of the most viable methods to produce ultrafine grained (UFG) metals since the late 1980s [1].
At each rolling pass, there is an increasing number of defects generated (mainly dislocations), these being accumulated and strain-hardening the material [5,6,7].
There were high levels of stress with a tendency of a slight increase as a function of the number of passes.
Such fragmentation of the cementite was also observed by ECAP, and is more evidenced with the increase in the number of passes, as shown in Fig. 5.