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./109,1 1 10042,0 53,4 6 4 4 3 − − − − ⋅=×⋅=× ×= × = where RC=4,53 *2U/I; I=906 ma = 1A ; 2U= U1+U2/2 The obtained data were listed in the Table 1.
Table 1 Mean value of the electric resistance for the samples Al Initial Sample Al Processed Sample Al Longitudinal section; Оhm/см Cross section Оhm/см Longitudinal section Оhm/см Cross section Ом/см 4,8 4,6 5,33 3,38 On the basis of the obtained data it follows, that the processing of the aluminum samples with the high velocity flow of particles results in anisotropy of the electric resistance: to its increase in the longitudinal direction by 11 % and in reduction - in the cross direction by 14 %.
According to the data of the microRoentgen-spectral analysis, the particles have the chemical composition, close to the composition of the protective body of the microcircuit.

This deformation is responsible for a reduction of the number of possible macromolecular conformations and an increase in the relaxation time of “unfrozen” segmental mobility.
The aim of this work consists in calculation of Tg for amorphous domains proceeding solely from the chemical structure and the degree of crystallinity, with no resort to experimental data.
When the experimental data on the glass transition temperature Tg and melting point Tm are not available, these quantities can be calculated based on chemical structure of any polymer by the following formula [4,5]: (14) where ai are atomic constants, which characterize the energy of weak dispersion interaction in terms of average atomic contributions; bj are constants which describe the energy of strong specific intermolecular interactions (dipole-dipole, hydrogen bonding, etc).
The proposed calculation scheme does not require the knowledge of any experimental data except for the chemical structure of the polymer.

Fig.4 The workpiece & parting-surface graph of mobile front cover Fig.5 Shaded parting-surface graph Design of the mold cavity and core Parting surface creation is completed, allowing for a parting surface split core volume, produced by the volume of the punch and die core and cavity, while completing design of runner system, casting [5].Cell phone cover from "Mold Volume-Split" Division of convex, concave die and then by "Mold Comp-Extract" pump design model, created the runner can be implemented by the basic commands such as cut reduction,Taking into account the precision of cell phone covers on the top surface and sides, cast on specific locations can be placed on the cover of buckle on the edge of concrete pouring forms can use the gate.
Fig.6 Casting Fig. 7 Mold check Fig. 8 Female die Fig.9 Male die Providing mold design using Pro/Engineer software module can be based directly on the product data to complete the mold cavity and core design, and the die structure design, greatly shortening the mold design cycle;Use of Pro/Engineer NC machining module, depending on your machining needs, generate CNC turning, CNC milling and wire-electrode cutting NC code.
Conclusions Cover modeling and die in Pro / E environment on the mobile phone design, to achieve product design, mold design, computer aided manufacturing process, changed the traditional design concept, can make the designer to create, modify, simulation analysis of die components.Its open design of simulation modeling, interference checking enables higher accuracy than the traditional design; The single database provides accurate data source for mold processing.
References [1]N.K.Vail,W.Wilke,H.Beider and G.Junemann(1996)-Interfacing reverse Engineering data to Rapid Prototyping-MIT Symposium proceedings archieve-Vol 56-481-490

Child human finite element model was derived and compered with response data of dummy crash test in 2008 [2].
The values of Table 1 are chosen from the real time frontal impact data [3].
Chosen parameter values from the real time frontal impact data Parameter Value 5 [kg] 5 [kg] 40 [kg] 1 [m] 0.4 [m] `Fig 6.
As a next phase work, a real size of baby car seat will be manufactured and tested to observe the effectiveness of the significant reduction injuries of children from collision accidents.

In order to compare measurements by both methods and from different devices all the data must be acquired in the same time by one device.
During each pictures pair taking the data of four strain gauges and load was recorded too.
To record strain gauges signal as Voltage Data Acquisition Data acquisition system (MGCPlus) was used with modules to measure strain gauge signal - ML801B and for analog output of this signal - ML78B module.
Data for major and minor strains was extracted using point analysis of ARAMIS software.
[5] ASTM Standard for Shear Properties of Composite Materials by the V notched Beam Method [6] Technical Data Sheet Hysol® 9461

DSC and XRD data denote an increase in the presence of crystalline regions as the AgZrP content is increased.
TGA data indicate that the addition of AgZrP has no effect on the thermal stability of the material.
FTIR data indicate a decrease in transmission at higher AgZrP loading.
The combination was initiated for the reason of sustainability and cost reduction.

Evaluation of the Rutting Deformation Data of Asphalt Mixtures during Continuous Cycle Testing based on Short-Cycle Wheel Tracking Testing Sigit Pranowo Hadiwardoyo Civil Engineering Department, Universitas Indonesia, Depok 16424, West Java, Indonesia sigit@eng.ui.ac.id Keywords: Rutting behavior, Permanent deformation, Wheel tracking, Asphalt mixtures Abstract.
Another effect of rutting is the reduction in pavement thickness, which increases the occurrence of pavement failure due to fatigue cracking.
On a particular type of machine, the wheel tracking test has limited capabilities, often leading to inadequate data collection for analysis.
The development of a method with the characteristics of short experimental cycles to achieve a long cycle of data was necessary to better analyze these data.
Table 4 The resume of deformation characteristic Material I (pen. 52) II (pen.46) Temperature 45 °C 35 °C 27 °C 27 °C [A] 0.649 0.591 0.494 0.083 [B] -0.803 -0.803 -0.802 -0.661 Using the data in Table 4, a rut depth curve for the load cycle prediction can be generated using the model equations (4), as seen in Fig. 3.

ERA is a modal analysis technique which generates a system realization using the frequency response given input and output data.
As a comparison, finite element model was built to obtain the environmental stimulus response data.
The modal parameters are identified precisely only to obtain the response data.
S An Eigensystem Realization Algorithm for Modal Parameter Identification and Model Reduction.
S. (1994b) “Determination of model parameters from ambient vibration data for structural health monitoring.”

Figure 3 shows an example of this dependency, where four clusters of data points can be clearly distinguished.
To assign data points to clusters, k-means was used.
The algorithm determined 4 clusters of data points.
Additionally, a cluster analysis were carried out on the recorded data.
Fig. 4: The best result of clustering algorithm for LEM dimensionality reduction method with 3 output components.

High speed data acquisition unit transmits the data to the memory unit through the USB data transmission.
When the count value is less than Δt, the comparator module comes out low level, A/D conversion and data receiving module does not work; When the count value equal toΔt, the comparator module comes out high level, A/D conversion and data receiving module start to receive, collect and convey the data to register, after the further processing the data is transmitted to the computer through the USB port.
The module mainly consists of the subtraction module, counter module, comparator module, A/D data conversion module and so on.
Clk is the clock of system, adin is the input data of FPGA through A/D converter, when aeb is generated, ADdata module converts the data and transmits the data to the USB interface module, adclk is the operating clock of A/D conversion chip, adcs is the chip select signal.
“A Digital Circuit for Jitter Reduction of GPS-disciplined 1-pps Synchronization Singnals, ” Advanced Method for Uncertainty Estimation in Measurement, 2007, pp.84-88