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The purpose of this work is to develop an experiment applied to milling process cutting parameters optimization based on data collection from the factory shop floor.
Hardened steel dies currently used for forging process were milled to generate data to be used during the experiments.
From data in Table 5 is possible to compare the cut parameters found during the experiences with the cut parameters previously the optimization procedure.
Cutting conditions before and after the experiences Data before the Experiences Data after the Experiences Cutting Speed [m/min] 155 162 Depth of Cut [mm] 0.3 0.6 Feed Rate [mm/rot] 0.1 0.1 The consequence of the adoption of these new cut conditions was an increase of 64 % in the process efficiency.
It is important to note that the use of both procedures can occur in the factory shop floor using the same data collected during the machining process.

Step 1: Experiment data generation.
Select testing points in terms of different load masses within the range, and conduct swept frequency test at each point to acquire FRF data set.
Using to represent the true parameter sets of the system, the noisy FRF data can be described as (7) where is the frequency of the sinusoidal input signal, is the measured frequency response, and is the number of frequencies.
The experiment FRF data (solid blue line) and the computational FRF data calculated by the identified LPV model set (dashed red line) are compared in Fig. 2(b).
Model reduction for control system design.

Dysprosium is produced mainly by the calcium thermal reduction of DyF3, alloy distillation of Dy-Mg based the calcium thermal reduction of DyF3 or magnesium thermal reduction of DyCl3, or distillation of Dy-La based lanthanum thermal reduction of Dy2O3 [1,2], as well as the electrolyzing Dy3+, sourcing from DyCl3 or the dissolved Dy2O3 in molten salts [3].
As known from Fig. 2(a), the reduction current obviously enhanced after the negative direction scan potential across -2.25V, and there was no reduction peak between -0.5 and -2.25V.
(1) Thermodynamics calculation data [17] about reaction (1) and decomposition of CaCl2 at 850℃ are list in table 1.
The reduction progress of solid Dy2O3 were directly completed from Dy2O3→Dy.
Che: Handbook of Inorganic Thermodynamic Data (Northeastern University Press, Shenyang 1993).

The data is taken from Ref. [7].
(2) Results Figure 3 shows the Hall plots for the 6C-ARB samples, where the lines are the results obtained by fitting Eq. (1) to the data.
The obtained data are scattering, but the tendency of 1− Q can be obtained by fitting the equation f L f Q π Λ π α = − 4 t1 ~ (2) N S N S Magnetic field Solenoidal coil Lorentz force Permanent magnet Sample N S N S Magnetic field Solenoidal coil Lorentz force Permanent magnet Sample Fig. 2 EMAR setup for measuring the ultrasonic resonance and attenuation coefficients.
to the data, where we have the relation 24 t ~ fLΛα by following the Granato-Lucke theory[10,11]; Λ is the "mobile (or effective)" dislocation density and L the segment length of dislocations.
The amount of reduction was evaluated by the before and after thicknesses.

In order to optimize data layout in the architecture, processor data reuse should be considered.
Decision factor for data cluster selection: if some data have T>1, then they might be a data cluster under satisfying a condition that the data should be consecutively located in address space.
Since the number of the elements is excessively large, we transfer the data elements onto the SM as a data cluster.
Data cluster is a union of data elements that have consecutive location and similar lifetime in a loop nest.
We examined the effect of using the SM on the reduction of traffic to main memory as well as on the energy spent in the memory subsystem.

In today's Internet, there are large amounts of data spreading over many physical distributed nodes which makes it impractical to send all the data to one central node for query processing.
Suppose the data stream S is divided into m sub-streams S1,S2,…,Sm.
Let's use the reduction to absurdity to prove its correctness.
Let's use the reduction to absurdity to prove its correctness.
Manjhi, et al., Finding (recently) frequent items in distributed data streams, Proc.

The activation energy of the reduction process was found to be 47.21±2.83 kJ/mol.
For kinetics study, taking data from Figure 1 and plotting the function f(x) = 1-2X/3-(1-X)2/3 agent time where X is the overall fractional decomposition of zinc ferrite gave results that confirmed the existence of pore-diffusion control as shown in Figure 7.
No previous report can be found concerning the activation energy values of zinc ferrite reduction by iron powder or EAF dust reduction by iron powder.
Zinc oxide product peak could be clearly seen from Figure 9 (b) to indicate the reduction process.
The activation energy of the reduction process was found to be 47.21±2.83 kJ/mol.

Observed data were utilized to obtain various performance parameters such as brake power, brake specific fuel consumption, brake thermal efficiency at various loads and throttle settings.
The collected test data are used to calculate brake power, brake specific fuel consumption and brake thermal efficiency.
Observed data in this table indicate that specified rated output of this diesel engine lies between best power & best economy zone at wide throttle opening (for the maximum duration of injection set in existing injection timing of the engine).
Minimum bsfc and maximum output for both the fuels reduces with the reduction in throttle (% reduction in duration of injection).
Percentage reduction in maximum output with the use of blend from their values with diesel reduces with reduction of throttle (duration of fuel injection).

The generated analytical data can be related to the mechanical properties of the sintered steels if the size of the specimen is large enough.
For both reduction processes, the main product is CO as indicated by mass 28.
In hydrogen the reduction mechanism is slightly different (Fig. 1b).
The reduction of the surface oxides occurs at fairly low temperatures (Fig. 4b).
The reduction processes of surface and internal oxides can be distinguished.

Dimension reduction.
In order to overcome this difficulty, Rahman and Xu [12] proposed the dimension reduction (DR) method which converts a multi-dimensional integral to multiple one-dimensional integrals: (2) Youn et al. [13] developed an enhancement of the DR method which is referred to as the enhanced dimension reduction (EDR) method.
The accuracy of the response approximation increases as the number of data points.
However, dealing with a large number of data points it is not economically reasonable.
Geometric covariance matrix Principal variance matrix Test pieces A Test pieces B In order to investigate the efficiency of the presented methodology, a set of 1000 assemblies were also simulated and the results are compared and evaluated with experimental data as represented in Table 3.