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The level of influence that the process parameters exert on the surface roughness is analyzed by Taguchi method data analysis.
The final results will be used as data for future research.
Similar investigations were carried out by [6] using Taguchi and ANOVA methods to investigate which the cutting parameter has the greatest influence on the surface roughness and on the cutting time reduction, in the study of LM-26 aluminum alloy.
Thus, in order to determining the factors’ levels for quality criteria optimization, it is found that: - Factor C - feed per tooth - and more specifically the first level of factor C, is the most important in dispersion reduction, and also in achieving the best possible surface roughness; - The same thing can be said about the factor A - cutting speed - but to a lesser degree, in this case choosing the second level; - Also, in order to reduce the dispersion results, it’s essential to choose the first level of factor B - cutting depth; - Regarding the combination of factors, those that provide the most favorable interactions on dispersion reduction of the cutting process are: B1C1, A1C2 and A1B1.

It calcualted data that the air layer thickness has about 3.9 times more impact that the glass thickness in composition of double glass.
First of all, carbon emission trading is expected to begin from 2015, which will lead to an effective reduction in carbon emissions.
Therefore, this study aims to seek the most efficient model to increase energy efficiency by changing windows and doors that have a great impact on energy-saving in a combination of glass and to propose the necessity of remodeling for energy reduction by performing a comparative analysis of the insulation performance of the current windows and doors with those replaced.
The central library is situated at longitude 37.3°N, latitude 127°W and 120m altitude, and the weather data of Suwon was used, and the International Standard Time(Tokyo) +9:00 was also used.      
Setting data Item Data Clothing Light Business Suit 1.0 Humidity 60% Lighting Level Office Desk / Work shop 400lux Air Speed Barely Noticeable 0.3m/s Occupancy Reading - 55W Internal Gains Sensible Gain 40 / Latent Gain 60 Infiltraion Rate Air change Rate 0.5 / Wind Sensitivity 0.1 Active System Full Air Conditioning Efficiency 85% Thermostat Range Lower Band 18℃ / Upper Band 26℃ Hours of Operation On 09:00 / Off 22:00 Analysis It shows the cooling and heating load according to the combination of double glass.

Relying on experimental test data, the model which had the nonlinear relationship between the effective tensile force and anchorage segment increased stiffness coefficient was established.
Specification [2] suggest that the vertical prestressed calculation with the method of reduction coefficient of 0.6, and considering the construction could not effectively meet the design requirements.
The problem of vertical prestressed tension loss is largely caused by human factors, the nut is not tight, installation factors lead to the vertical prestressed tension reduction.
Fig.4 Measuring of testing field Based on test data and Eq. 7, the theory value of k can be obtained.
in table 1, the following conclusions can be obtained: (1) the k value increased along with the effective tension is nonlinear, the main reason is that nut bite more and more tight. (2) at the same level of tension, the action of a first order modal frequency w diminishing with the increase of the exposed section of the steel bar length is nonlinear. (3) the theory value k and the finite element solution k are all in the same rule, two groups of data are basic closed.

All the dots represent training data.
Training data that fall within this boundary, denoted by the blue-colored ones are considered good data.
Data Characteristics Location.
The data range analyzed is from January to March 2014, with almost 1000 data points being analyzed.
The equations applied are: (5a & b) Yt is the training data while Ft is the forecast data.

The as-received samples were tested and used as baseline data.
The system is equipped with a specialized data acquisition, data reduction and data plotting software.
The fracture strain however, experiences an abrupt reduction in value that starts after ½ month of outdoor exposure (Fig. 1).
This reduction reaches 38% after an additional ½ month of exposure.
As fracture strain is generally thought of as measure of material toughness, its reduction is an indication of lower toughness.

Reduction of heavy-metal content in overburden material by bacterial action I.
Reduction of heavy metals content of overburden is very important to minimise the heavy metal spread into environment and to recovery soils for filling the exhausted mining sites followed by reforestation.
About 60 % of cobalt and almost totally manganese (data not shown) were leached at both pulp densities.
kinetic by cultures of A. thiooxidans in medium 0K, 1 % w/v S, and different pulp densities of ore B. pH kinetic by cultures of A. thiooxidans in medium 0K and different % w/v S and pulp densities (pre cultured during 48 h). 1 2 4 0 20 40 60 extracted metal [%] sulphur [w/v %] C G I K M pulp density 5% pre-culture time 48h A b a d c a c b d 1 2 4 0 20 40 60 extracted metal [%] sulphur [w/v % ] C G I K M pulp density 7.5% pre-culture time 48h B b a c a c b e d d e g f f g i h h i 0 50 100 150 200 250 300 350 400 Fe i ii iii iv v vi vii 0.00 0.05 0.10 0.15 0.20 Co Before After i ii iii iv v vi vii 0 2 4 6 8 10 12 14 16 18 20 Cr 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Mn 0 1 2 3 4 5 6 Ni 0.0 0.5 1.0 1.5 2.0 2.5 Mg According to our results, it is clear that the bioleaching procedure allows important cobalt and manganese recoveries (even economically valuable) and a reduction

Illustrative size reduction data corresponding to ultrasonic frequencies of 20 kHz, 500 W are shown in Fig. 2(b).
Based on turbidity data, Fig. 4(a) illustrates the noticeable difference between the two decantation levels of the 30-minutes sono-fragmented sample, using 20 kHz, 500 W and 58 kHz, 500 W, respectively prior to start of blending.
Figs. 4(b) and 4(c) illustrate that good blending is obtained after 30 minutes, based on turbidity and LPC data, respectively, for the 20 kHz sono-fragmented particle sample followed by 132 kHz sono-blending.
After completion of several experiments using different combinations of fragmentation and blending frequencies (e.g., 20/33, 20/58, & 20/132), optimum time data obtained from turbidity measurements have been plotted as a function of ultrasonic frequency in Fig. 4(d).
By analyzing sono-fragmented and sono-blended alumina ceramic particles, which are a stratified mixture of nano/ sub-micron particles, optimum blending time was determined on the basis of turbidimetric data.

The hardness ANN used the same input data, but the output data were the hardness points (22) measured along the extrusion run-out.
Working scheme for handling the discarding ANN data.
Table 3 shows the production data for the discard ANN along with the estimated errors.
Average error between prediction and real value for production data of the discard ANN.
Average error between prediction and real value for production data of the hardness ANN.

Generated mainly by the burning of fossil fuels, their reduction can be achieved by changes in the conditions of refining processes, capable of adapting the legal specifications of fuel sulphur and nitrogen, or by treatment of gases exhausted.
About the reduction of NOx indices generated in diesel engines, it is possible to choose the technologies related to fixed bed treatment for removal of nitrogen compounds [2], injection systems much more efficient and improvement of combustion chambers and finally, the treatment of gases exhausted.
The main reactions involved in the process of reduction are described by Equations 1 and 2
(1) (2) As the gases exhausted from diesel contains quantities of NOx greater than 90%, the main reaction that occurs in the SCR (Selective Catalytic Reduction) is described by Equation 1, where ammonia and nitrogen monoxide are in the stoichiometric quantities of 1: 1.
The acceptable error was 5% and the data obtained in the analysis of samples are presented in Table 1.

The Fomichev plug is of characteristic producing equal unit-reduction of wall thickness along its perforation section during seamless tube piercing.
Li Shengzhi in paper [4] showed a 2-D coupled thermal-mechanical simulation of center-crack occurrence during 2-roll rotary rolling process assisted by FE code MSC.SuperForm, and obtained the critical percentage of diameter reduction by comparing the simulation results with experimental data.
Differences between the Fomichev plug and conventional plug The Fomichev plug is of characteristic producing equal unit-reduction of wall thickness aiming to make metal deformation well-distributed along its perforation section, and reduce the drag resistance to longitudinal flow of the metal at the inner surface of the workpiece.
The perforation section generatrix of the novel Fomichev plug discussed in this paper is represented by formula (1): (1) where Rp(x) is plug diameter of the point x in axial coordinate; R(x) is the roll opening at the point x in axial coordinate; SHE is the ideal wall thickness of hollow shell at the intersection between perforating and smoothing section of the plug; Cav is the average reduction ratio of wall thickness.
This kind of plug is somewhat universal, but its flaw is that the wall-thickness reduction of workpiece focuses on one part of the plug in piercing.