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The main factor of plasticity for optimal warm temperature selection from examined temperature interval is value of reduction of area that was determined by tensile test.
On the basis of thermal course of plasticity characteristics (reduction of area Z, ductility A) we are able to observe reduction of area decline at the temperature 750 °C.
Fig. 3 Courses of graphic relations of parameters resulted from the tensile test For the purpose of optimal warm temperature selection from examined temperature interval the crucial indicator of steel 16MnCr5 plasticity is value of reduction of area Z.
As reduction of area Z achieves its maximum value at the temperature 700 °C, the same will be recommended as optimal temperature of steel 16MnCr5 for warm forming.
For starting a simulation of spur gear it is necessary to properly define the input data – these data were determined as follows: · process - closed die forging · material of billet DIN 17210 (1.7131) · material of the tool ASTM A 681 (H13) · temperature of billet 700 °C · temperature of the tool 250 °C Fig. 4 Closed die model and correct material flow in closed die cavity Computer simulation results of warm forging at the recommended temperature 700 °C describes fig. 4. where it is possible to see correct plastic flow and flawless filling of closed die cavity.

The crystalline phases were identified by the JCPDS data bank.
Fig. 1 shows the XRD patterns of samples prepared at reduction temperatures of 45 °C and 50 °C respectively.
These peaks, according to JCPDS data bank (06-0344), are the fingerprints of CuCl.
At the reduction temperature of 50 °C, the prepared CuCl powder starts to sinter (Fig. 2b). 
When the reduction temperature is increased, the surface area and pore volumes become smaller.

The renewal of data in each computation is limited because of the time of data transfer unlike the shared memory type workstation.
This brings about the delay of data renewal.
Data Transfer.
Data transfer Imbalanced stress <0.0001MPa Subroutine 1 Subroutine 1 Data transfer All steps ?
Data transfer Imbalanced stress <0.0001MPa Subroutine 1 Subroutine 1 Data transfer All steps ?

The data collection is to recognize and calculate the performance of FR.
The required weather data are maximum wind velocity and temperature.
Tolerance calculation Average Avg. error Result Temperature (°C) 27.95 7% 27.95 ± 7% Wind velocity (m/s) 4.63 43% 4.63 ± 43% Data Averaging and Tolerance Data averaging is variable in calculating the spin ratio and other FR calculations after collecting the wind velocity and temperature data.
The calculation will combine all the weather data from any station.
Moreover, the tolerance calculation estimates the error of the weather data.

At present, based on the current functions such as data collection, monitoring, energy supply and demand balance analysis, energy assessment and management, EMS will develop and tap its own capabilities to realize the real-time forecast, allocation optimization and smart scheduling against energy to meet the actual needs.
Main functions of Energy Management System The functions of EMS can be divided into three levels generally: firstly, the control of energy management equipments and data acquisition from energy equipment; secondly, energy monitoring and scheduling system; last, energy analysis and management.
(1)Data acquisition function.
Collect the production data required for the purpose of monitoring, data calculation, statistics and analysis to achieve energy management in the works
Collect energy consumption data and establish a database according to it and gather the purchase and consumption of all energy and the product and process energy consumption to make up a table of actual energy balance

Many ideas were put forward to solve this problem, weight reduction seems to be the most feasible solution [1, 2].
Material replacement is the most direct way to realize weight reduction, which uses light metals like Al to take place heavy materials like steel.
Engineering stress-strain curves were got after tensile tests, then transformed engineering stress-strain data into true stress-strain data by calculating.
Table 2 RMSEs between fitting result data and experiment data Strain rates [s-1] 0.0003 0.003 0.03 Temperature [℃] 360 430 500 360 430 500 360 430 500 RMSE 1.70 0.23 0.10 2.48 0.44 0.15 7.41 3.15 0.26 Conclusions In order to investigating flow stress behavior of material Al A5005 at high temperature, twelve hot tensile tests at temperatures 360℃, 430℃, 500℃ and strain rates 0.0003s-1, 0.003s-1, 0.03s-1 were set up.
A constitutive model and data for materials subjected to large strains, high strain rates, and high temperatures[J].

The high recovery of iron and nickel in the alloy indicates the highly reducing condition prevailing in the smelting reduction experiments.
The amount of nickel content in the metal at the initial stage of reduction was higher compared to the chromium content.
The reduction of iron was an important control factor, since iron dilutes nickel and chromium and thus lowers the Ni and Cr concentration in the alloy.
A typical plot is shown in Fig. 6 for the data in all the series of experiments at 1550°C under an argon atmosphere.
The high recovery of iron and nickel in the alloy indicates the highly reducing condition prevailing in the smelting reduction experiments.

The investigation leads to a reduction of the process forces by minimizing the springback and to an extension of the forming limits.
The use of lightweight constructions leads, e.g. in the automotive industry, to a reduction of fuel consumption.
It leads to a significant extension of the forming limits and a reduction of springback effects [6].
The flow curve of the blank is determined by fitting data from the tensile test.
Achimas, Springback Reduction for V Bended Parts through Elastic Pads, International ESAFORM Conference on Material Forming (2005), 497-502

The future challenges for SiC device technology are cost reduction and increased reliability.
Introduction As the market share of SiC devices in power electronic application grows continuously, cost reduction and reliability improvement of SiC power devices becomes essential.
The defect density data show that the new epitaxy process results continually in a BPD to TED conversion rate ranging from 99.995 to 100% with an edge exclusion of 2.5 mm.
Summary Cost reduction and increased reliability are the main future challenges for SiC devices and the epitaxial layer growth is a key process in order to accomplish this.
Yield increase during epitaxial growth is achieved by the reduction of structural defects such as basal plane dislocations and triangular defects and the increase of doping and thickness uniformities.

The study provides experimental data that could be used for the design and development of more efficient exchangers for refrigeration conditioning, heat pump and some other systems.
Experimental facility and data reduction Fig. 1 shows a schematic of the experiment facility and test section.
All the experimental signals are collected and processed by Agilent 34970A data acquisition system.
Comparisons of the experimental data with existing correlations were made for 96 experimental data, as shown in Fig. 2.
The following hold for the straight tube: in the laminar range, fc=64/Re, and fc=0.3164/Re0.25 in the turbulent range, Meyer [7] experimental data for the straight tube was plotted as well.