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This paper started with analyzing the performance data of the plastic sheet molding material, using nonlinear finite element method and multi-physics coupling method to simulate the plastic sheet forming process, and the result gives the required parameters for product design and quality control.
And mechanical property constant C10, C01 is determined by the experiment, which is obtained by multiple testing and data fitting. 2) The geometric model and mesh configuration Simulation studies were focused on the common forming characteristics, such as cup drawing parts of different drawing ratio.
Based on the data obtained by photographic surveying the formed specimen with deformed grids, the ASAME grid Strain Measurement System gives the thickness distribution of specimen cup blow, as is shown in Figure 5 and then the thickness distribution of specimen cup blow can be obtained by data procession through ASAME grid Strain Measurement System, as is shown in Figure 6.
Reference： [1] Adolf Illig and Peter Schwarzmann, in: Thermoforming: A Practical Guide, Chemical Industry Press (2007), p.14 [2] A.Attar, N.Bhuiyan and Vince Thomson: Manufacturing in blow molding: Time reduction and part quality improvement, Journal of Materials Processing Technology (2008), Vol. 204, p.284-298 [3] WANG Wen-bo and PIAN Xiao-peng: Seal Design of Box Edges Based on Nonlinear FEA, Mechanical Engineer (2009) Vol. 10, p.104-106 [4] LAI Jia-mei, LIU He-sheng, HUANG Han-xiong and BAO Zhong-xu: 3D Numerical Simulation of Parison Inflation in Extrusion Blow Molding, China Plastics (2004), Vol. 18, p.85-87 [5] LV Xiao-dong and WAN Min: Application research on microscope strain testing system of sheet metal, Forging & Stamping Technology(2007), Vol 8, p.101-104

For this purpose, a phase-field model has been extended to allow for complex 3D anisotropic interfacial energies and interfacial mobilities, calibrated by data from molecular dynamics studies.
The anisotropy coefficients are calibrated with data from molecular dynamics studies on pure Mg recently published by Xia et al
For the assumed anisotropy functions, based on molecular dynamics data for pure magnesium, dendrite arms are found to evolve only in <1120> orientations.
For the assumed anisotropy functions, based on molecular dynamics data for pure magnesium, dendrite arms are found to evolve only in <1120> orientations.
Figure 4: Simulations for two different process velocities in comparison with experimental data by Mirkovic [9].

Hardening/softening curves, cyclic stress-strain curve and fatigue life data of coated and uncoated material were obtained.
Experimental data were approximated by the power law log σa = log K´ + n´ log εap
was used to fit experimental data.
The Coffin-Manson law log 2Nf = (1/c) log εap - (1/c) log (3) was fitted to experimental data and material parameters were evaluated using non-linear regression analysis.
Fig. 6 shows that experimental data of both materials are close to each other.

Experimental data are used to formulate multisubstrate degradation and fungal growth influenced by the IMC.
TKN conversion into a dry weight of fungus follow equation conducted by Shi et al (2012) [6], but based on a preliminary data from pure Phanerochaete chrysosporium in this research that a linear correlation existed between fungal biomass and presence of TKN with a conversion coefficients (kT) of 10.8 g fungal biomass.g TKN-1.
The results of the evaluation of the application of the effects of IMC model to data of research by Wan et al (2011) can be seen in Figure 3.
Results of Curve Fitting Application of Moisture Content Model for Research Wan et al. 2011 Implementation of the experimental data of Wan et al. 2011 on the effects of IMC model gives Sum of Square Error (SSE) results of 0.000507, the relative error below 5% and R square above 0.8.
Curve Fitting Results of experimental data of Wan et al.2011, using IMC effects models result in some constants.

As a consequence of the many reports of superplasticity in aluminum-based alloys, it is appropriate to compare these data by examining an earlier diagram which showed the occurrence of superplasticity in aluminum alloys fabricated using several different processing techniques [26].
An early study on ECAP processing of tungsten showed that increasing the die angle allowed a reduction of the processing temperature [31].
also this will increase the strain rate sensitivity and permit a reduction of temperature in subsequent passes.
From the data recorded in these measurements of the sliding offsets, it was possible to estimate the sliding contributions to the total strain.
Since separate data sets of sliding offsets and grain sizes were recorded for the different types of grain boundaries and phases, it was possible to determine not only the overall sliding contributions but also the individual contributions of sliding to the total strain estimated separately for the different types of interfaces.

The online application of domestic state estimation in Supervisory Control and Data Acquisition (SCADA) has gone through from the bad data check to the real-time cross-section data services.
(1)Telemetry value incorrect There are three main causes that lead to telemetry incorrect data of dispatching system, data acquisition error and error, data conversion and transmission error and data processing
The change of power grid operation will result in the data of rapid mutation and even the whole network data, and data transmission delay will lead to poor consistency of data used in state estimation.
The main steps are as follows: Step 1) Simulation computer simulate the daily operation data of power grid; Step 2) Data emulation layer to get the whole network operational data, simulate disturbance scenario and form various types of disturbance data; Step 3) Send disturbance data, which is formed in data simulation layer, to the testing platform and the SCADA application; Step 4) State estimation calculated according to the real-time data in SCADA.
Different ramp rate of load make the data obtained in state estimation is not consistent with the real-time data.

the actual position of shield machine and the corresponding mileage design location comprehensively, shield machine position is to simulated data and chart two form of display in the control room computer screen.
yellow box Laser theodolite Computer Boring machine Prismatic target The pipe wall Rear view prism Data to office Controller Boring machine control Room Fig 1 Laser theodolite in the work situation 4.3, Mobile measuring bracket and a positioning In TBM development 1-2 day, because the total station instrument and prism distance too far away, or tunnel smaller turning radius, the need to move the measuring bracket.
In the construction of artificial measurement data and synchronization of laser automatic guiding system for measuring data were compared, the results are satisfactory.
Figure 2 is the attitude of shield machine artificial detection data and SLS-T data contrast map, from the 118 data comparison can be seen, the SLS-T synchronization laser automatic guiding system of shield machine attitude control is worthy of trust.
Manual testing & SLS-T test differentials Number of observations SLS-T test differentials Manual testing Deviation DL Fig 2 The TBM attitude artificial detection data and SLS-T data comparison chart

The radial velocity measurement is a very important data processing step for those big data.
Testing Data The spectra we used in this paper are all theory spectra, including 9593 MAFAGS spectra and 3490 MARCS spectra.
For 3490 MARCS data, use the 800 spectra picked to be the template.
IRAF was developed in 1986, and become a popular tool for astronomical data reduction.
Spectral lines are almost absorption lines in stellar spectra, we use XCSAO to compute radial velocity for the test data.

Data and Study Site We chose a SNOw pack TELemetry (SNOTEL) site, Ebbetts Pass, in the Sierra Nevada region in the United States, as our study site.
Some of the forcing data, such as shortwave radiation and pressure, not available from the SNOTEL site, is got in grided data from the North American Data Assimilation System (NLDAS) at 1/8th degree resolution and the North American Reginal Reanalysis (NARR) data products at 32 km resolution.
For consistence, the 3-hourly NARR data and the hourly DMIP2 precipitation data (at approximately 4 km resolution) are gridded into 1/8th degree resolution.
In summary, the forcing data, at a grid scale associated with a resolution of 1/8 degree, is used to run the snow models at a point scale.
Snow water equivalent and snow albedo First, the hourly calculated SWE data from models were aggregated into daily data, and then investigated the model behavior by comparing simulated SWE with observed ones.

It is concluded that the selective growth phenomenon following the <110>26.5deg misorientation relationship is strongly supported by the gathered orientation data, after appropriately normalizing these data with respect to a random misorientation distribution.
When Hutchinson et al. [5] compared the misorientation data from Ibe and Lücke [3] with a random distribution of misorientations, a remarkable resemblance was found.
The drawback of these representations is that they embody a 1D projection of the misorientation data, which are essentially of a 3D nature.
Figure 2) a similar analysis must be carried out on the initial misorientation data, before growth, between the nuclei orientations and the single crystals.
By making a comparison of different sets of misorientation data in three dimensions, a clear interpretation of these data is possible.