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This article deals with an experimental determination of the static modulus of elasticity in compression on fine-grained composites based on alkali-activated slag.
Two fine-grained AAS composites were produced for the presented experiment.
It can therefore be concluded that loading of the composite "IX" causes the formation of a higher number of smaller defects (cracks) than in the composite "VIII".
Kalina, Effect of the by-pass cement-kiln dust and fluidized-bed-combustion fly ash on the properties of fine-grained alkali-activated slag-based composites, Mater.
Karel, Shrinkage of Fine-Grained Composites Based on Alkali- Activated Slag, Non-traditional Cement & Concrete, Brno, 2017, 20-21

As seen from Fig.2, O′-Sialon grains exhibit equiaxied morphology and the grains surface is smooth and the size is about 1-3 μm, whereas granular grains are TiN that the size is mostly smaller than 0.5 μm.
From Fig.2 (a) to Fig.2 (d), the number and particles size of O′-Sialon exhibiting equiaxied morphology decrease gradually, and the granular TiN grains increase constantly.
This is due to the increasing number of TiN particles, limiting O′-Sialon grains growth, resulting in their particles smaller.
O′-Sialon grains mostly present transcrystalline fracture, whereas TiN grains mainly intergranular fracture.
O′-Sialon grains exhibited equiaxied morphology, while TiN grains showed fine granular morphology and most of them have lower size than 0.5μm.

Position Microstructures Before 3 2 1 View : [A] 3 2 13 2 1 View : [A] After 6 54 View : [A] 6 54 View : [A] Fig. 4 Microstructures of tube product during the rotary swaging process It is obviously observed from the figure that the number of small and elongated grain boundary increased after the rotary swaging process.
Figure 4 also notes that the shape of grain boundary becomes smaller and more dense after the rotary swag process.
As can be seen in the figure, 1, 2, and 3 are numbered along the rotary swaging procedure.
Grain sizes are quantitatively shown in Table 2 with respect to the designated position as depicted in Fig. 5.
Along the axial direction from No. 1 to No. 3 grain sizes became smaller.

Based on the grain size analysis, the size distribution of dustfall mainly ranges from 1µm to 40µm. 1 Introduction With the rapid development of economic and society, air pollution become a global hotspot.
Size distribution results, relevant grained parameters together with volume percentage contribution of dustfall in variant grain size were listed in table 1.
The mass percentage of dusts that grain size varied from 10 to 30µm account up to 60.25%.
Dust with grain size under 2.5µm, 10µm accounted for 5.16% and 20.84% respectively.
Grained parameters and volume percentage contribution of dusts in variant grain size Sample number size range（µm） grained parameters（µm） <0.5 0.5~1.0 1.0~2.5 2.5~10 10~30 30~60 >60 D[4,3] D[3,2] d(0.1) d(0.5) d(0.9) Valume ratio 0 0.54 4.62 15.68 60.25 18.94 0.01 19.916 9.959 5.059 18.415 36.367 * d(0.5), d(0.1), d(0.9) represent grain size, under which the number ratio of dusts against the total dusts were 50%, 10% and 90% respectively.

Page Numbers.
The equations have to be numbered sequentially, and the number put in parentheses at the right-hand edge of the text.
Large grain boundary region is highly disordered, and having large number of defect states due to incomplete atomic bonding.
Large grain boundary region is highly disordered, and having large number of defect states due to incomplete atomic bonding.
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In rolling process, the grains start reversing to some main orientation of rolling texture.
In order to facilitate the analysis the process of grain aggregate, the distribution of grains orientation is simplified to some orientation lines in fcc metal deformation process [13].
With increasing plastic deformation, the grains aggregate to B and C components.
With increasing deformation, the number of slip system for coordination deformation increases gradually, some extra slip system is opened with not occur in normal rolling.
When the thickness of samples reach to a certain value, the grain sizes are refined to nanoscale, at that time, the distance to grain boundary for dislocation is much shorter, the most dislocaiton could disappear at the grain boundary[16], that more deformable are reflect in texture growth rate incresing.

Sintering process can directly affect the grain size, pore size and the volume fraction of the grain boundary of cermets.
A part of grains in Fig. 2(b) grew up abnormally, however, almost all of the grains in Fig. 2(d) grew up abnormally.
*Note: Take “T2M6N6-1” as an example, “T2M6N6” represents component number, and “1” represents sintering process.
As shown in Fig. 2(c), the grains of T2M6N6-3 were more uniform and finer than that of T5M6N6-3.
The refinement of grains was contributive to improve the flexural strength and the Vickers hardness.

Reason for the phenomenon is that with the increasing of linear speed, grinding number of times of a single grain gets increased in unit time.
So, the scratch on the surface of ceramic material becomes shallow and the number of cracks gets reduced.
The abrasive dust could be increased at high speed grinding, it can plug the grains and make the grains passivated.
When the axial speed is lower, number of times of grinding for the workpiece by one grain gets increased.
Through a large number of experimental studies the best combination of parameters for grinding cylindrical of silicon nitride ceramic spindle is that the size of grinding wheel grain is W40, the linear speed of grinding wheel is 35m/s, the transverse feed is 0.017mm and the axial feeding speed is 3.0mm/s.

A Recognition Method of License Plate Number Based on BP Neural Network Bingxiang Liu1,a, Yanhua Huang1,Xudong Wu2, Yingxi Li2 1 Jingdezhen Ceramic Institute, Jingdezhen,333403, China 2 Jiangxi Ceramic & Art Institute, Jingdezhen, 333000, China a lbx1966@163.com Keywords: BP neural network; character recognition Abstract: According to the current technological deficiency of license plate recognition, this paper uses digital graphic processing technique and BP Neural Network algorithm fusion to achieve automatic recognition of license plate.
The major tasks of license plate recognition are analytical processing auto images, automatic recognition of registration numbers including license plate location, license plate segmentation and license plate recognition and license plate recognition is a core part for achieving license plate recognition system.
License plate after remedy This paper has a binaryzation about the remedied license plate image and uses binary image morphology to further process the image including disconnecting H, removing fuzzy grain, opening operation, first erasing and so on.
Remove fuzzy grain Figure 7.Opening operation Figure 8.
Training error curve Experiment result All the experimental results in this paper are achieved through MATLAB software which shoots all kinds of litter cars and segments their characters as training sample to form then numbers, 26 letters, 9 province abbreviations and other 45 training samples.

The area near the 7A52 Al-matrix in heat affected zone (HAZ) was rolling state microstructure combined with recrystallization grains.
Compared to the grains in the matrix, that in this area had grown up obviously.
Average size of the grains in the welded zone by using 5183 welding wire micro-alloyed via single Sc-element was the smallest.
Due to thermal cycle from welding process, the grains in heat affected zone (HAZ) had grown up in different degree.
The average size of the grains in the fused zone was the smallest when the 5183 welding wire was micro-alloyed via single Sc-element.