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Simulation of Ironing Process for Earring Reduction In Sheet Metal Forming Agus Dwi Anggono1, a, Waluyo Adi Siswanto2,b 1Universitas Muhammadiyah Surakarta Jl.
If the punch radius is ri, the reduction ratio is defined as Reduction ratio (%) = t1-t2t1x100 (1) The reduction percentage, however, does not evaluate the strain of the material, Ei, which can be expressed as the ratio of thickness variation, or Ei=t1t2 (2) In industries of canmaking, the processes usually includes drawing, redrawing, and ironing operation [1,2].
The quarter model is then devided into 11 parts for collecting data.
Because of the reduction of material thickness of wall while the volume is constant.

Every 10 years (according to statistical data) the amount of ash and slag produced at thermal power plants doubles.
Average size of the powdered particles was identified according to data by laser analyzer LAZER MICRON SIZER PRO-700 (Japan).
As a result of electron microscopic studies of the morphology of the phases of ash and products of its enrichment according to the data of X-ray structural analysis, the features of the microstructure of their surface were revealed.
Analysis of the experimental data indicates the rupture of the bridging -CH2- and -CH2-CH2- bonds between the condensed aromatic fragments in the layer and the formation of CH3- groups (period of the g-band 4.44-4.70 Å).
As a result of experimental data, it was found that the flocculation of coals after vibro-shock loading takes place simultaneously in two directions: ionic-molecular and radical, which is a feature of the grinding of minerals in vertical vibrating mills.

A toolkit was developed based on web data to validate the algorithm.
It is indicated that this model and the toolkit decreases search effort of partners on looking for their interested business and improves the efficiency of using business data.
However with the increase of business information data at such a platform, no matter how accurate the information describes the business, for any companies, the time and cost spent on searching interested information in a traditional way like by keywords are increasing greatly.
To make the validation close to practice, most simulation business data were collected from past operation information of some companies, whose names have been changed for security.
As shown in Fig.3, a new piece of cargo information was added into the system through the defined data form, where the cargo business was characterized with the origin(Nanning), destination(Guangzhou), type(paper products), start date(May 28,2012), end date(May 31,2012), amount(1.5 ton), size(2×2×0.4 m), status(urgent) , and so on.

Rough intensive reduction algorithm.
(5) Output the attribute reduction results.
The author respectively used such as the frequency discrete method, minimum entropy discrete method, the overall discrete method for data discretization to get the decision table of data discretization (lack of space, not list)
(4) Use the rough intensive reduction algorithm for attribute reduction, after dispersing the data of the decision table.
Different discrete methods to get the different attribute combinations of the parameters, by repeated tests, {C1, C3, C4} attribute combination which is the results of data table processed by overall discrete method will be made as knock indicators eventually.

After theoretical calculation, a noise reduction of -6.04dB can be obtained.
To insure all photons around 1064nm leaving the OPO cavity originated from the parametric down-conversion process, fields at or near the fundamental wavelength of 1064nm should not be injected into the squeezed light source during data taking.
The amplitude noise reduction is -5.48dB if the electronic noise and low power of transmitted light are considered.
A theoretical noise reduction -6.04dB can be obtained by theoretical arithmetic.
Danzmann, and Roman Schnabel, “Observation of Squeezed Light with 10-dB Quantum-Noise Reduction,” Phys.

The results of simulation and faulty bearing show that the proposed methodology can achieve good effect of noise reduction, and be more suitable for the non-stationary characteristics of vibration signals.
Introduction Noise reduction is an important part of signal analysis and processing, and it is conducive to signal characteristics extraction.
The traditional noise reduction methods mainly use a certain bandwidth to filter in the frequency domain.
As a useful method, this method is widely applied in noise reduction [10,13].
The vibration signals generated from the bearings with faults are obtained from the bearing data center website of Case Western Reserve University (CWRU) [14].

The effects of cooling rate and austenite structure on bainite formation was investigated by means of electron backscatter diffraction analysis and processing of obtained orientation data.
The data on local orientations were treated using MTEX software [8].
To choose the OR, which corresponds better to the data of the present investigation, the relationships obtained in Refs. [7, 10] were compared.
Austenite deformation results in the packet size reduction.
Note that the packet refinement does not imply a reduction of the effective grain size.

Two keywords were used to search from the secondary data which are environmental education (EE.) and waste management.
Subjects and Methods Subjects: This is a reviewed article with the relevant literatures from secondary data resources with two keywords: environmental education and waste management.
The secondary data resources were from internet, textbook, online journal article; such as, Science direct, and research report.
Methods: After that, all research data was compiled, analysed, synthesised, and described respectively as follows.
Pengmuen (2006) [11] Learning activities (Training of waste, Field trip of waste management and waste reduction management project, Appreciation-Influence-Control (AIC) activity for waste reduction project, A summary of the learning activities and the cleanliness protection club - Knowledge: waste management - Awareness: waste management - Attitude: waste management - Participation: AIC activity about waste reduction and cleanliness protection club M.A.

An effect of the openings on the punching capacity was taken into account by the reduction of the control perimeter length.
Three ways for the length reduction were used.
However, results in Table 1 did not take into account the reduction of the control perimeter length due to the elongated column while results in Table 2 were obtained with this reduction.
This observation indicates that the reduction of the control perimeter lengths due to elongated column is conservative. 2.
Zsutty, Beam shear strength prediction by analysis of existing data.

The main train of thought of Method 1 is, the measured data at hub height is set to be Data Set 0, increase each wind speed value in Data Set 0 by 0.1m/s to form Data Set 1, increase each wind speed value in Data Set 0 by 0.2m/s to form Data Set 2, …, increase each wind speed value in Data Set 0 by 1.0m/s to form Data Set 10.
The main train of thought of Method 2 is, multiply each wind speed value in Data Set 0 with coefficient c1 to form Data Set 1 so that the MWS of Data Set 1 is larger than Data Set 0 by 0.1m/s, multiply each wind speed value in Data Set 1 with coefficient c2 to form Data Set 2 so that the MWS of Data Set 2 is larger than Data Set 1 by 0.1m/s, …, multiply each wind speed value in Data Set 9 with coefficient c10 to form Data Set 10 so that the MWS of Data Set 10 is larger than Data Set 9 by 0.1m/s.
Tab. 2 Fitting Results of Wind Farm A at 80m Under DPM 1 Data Set a b R2 Data Set 0 0.01155 2.227 0.9978 Data Set 1 0.01047 2.263 0.9981 Data Set 2 0.00951 2.296 0.9989 Data Set 3 0.008585 2.333 0.9990 Data Set 4 0.007742 2.369 0.9992 Data Set 5 0.007045 2.402 0.9994 Data Set 6 0.006565 2.425 0.9988 Data Set 7 0.00591 2.462 0.9988 Data Set 8 0.005322 2.498 0.9987 Data Set 9 0.004784 2.535 0.9987 Data Set 10 0.004306 2.57 0.9986 Tab. 3 Fitting Results of Wind Farm A at 80m Under DPM 2 Data Set a b R2 Data Set 0 0.01155 2.227 0.9978 Data Set 1 0.01207 2.195 0.9970 Data Set 2 0.01148 2.201 0.9938 Data Set 3 0.01100 2.204 0.9921 Data Set 4 0.01076 2.202 0.9930 Data Set 5 0.01022 2.211 0.9914 Data Set 6 0.00972 2.222 0.9926 Data Set 7 0.01008 2.193 0.9963 Data Set 8 0.00946 2.209 0.9950 Data Set 9 0.00910 2.207 0.9923 Data Set 10 0.00861 2.220 0.9922 In Tab. 2 and Tab. 3, R2 is known as square value of fitting correlation coefficient, and if this value is close to 1, it is illustrated
For easy of comparison and analysis, the combined reduction coefficient is set to be 0.667, which is corresponding to 1950.61h of EAD.
According to the method above, PG and EDA of Data Set 0-10 can be obtained, which is shown in Tab. 5, where denotes EAD corresponding to theoretical PG and denotes EAD with combined reduction coefficient of 0.667.