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As shown in Fig.6, this frame is the basis for data management and process control, and also for the optimization method developed later.
Data points with ‘’ symbol denote general buckling modes, data points with ‘’ symbol denote local buckling modes, and data point with a ‘’ symbol denote the optimal design point.
The unitary form of mass results (0.01251t) under =1.5, (,) =(50,7) is taken in order to describe the varying traits of data points better.
It could be seen from the data points that: the optimal skin thickness and area ratio increases/decreases with increasing under fixed numbers of ribs, and smaller mass could be obtained.
The extent for mass reduction decreases when is larger than 4.5.

Instrument and fixture A thermocouple is welded in the area of the pin and connected to a Bluetooth transmitter, which feeds the data to a laptop - Fig. 4.
These data were used to construct a temperature variation diagram for a point on the weldment in contact with the center of the pin.
This characteristic peak was used to compare the experimentally obtained data and the results obtained by finite element models.
Using the mass scaling factor allows a reduction in CPU time.
Nevertheless, some inertia exists and the comparability of experimental data with those obtained by simulation is debatable.

So, the last image was chose to extracting the characteristic data.
Experiment and result Characteristic Data.
It should be measure along the outside contour and get the data.
The area’s measure tool was used to get three area’s data for each side of the tiger.
Parts of the testing data are in the table 2 and table 3.

At the end of the training process, the test data were used to check the accuracy result.
Adaptive learning can be defined as an ability to learn how to do tasks based on the data given for training or initial experience.
The input data is presented to the input layer of the network and propagated through the network until they reach the output units.
This forward pass produces the actual or predicted output data. 2.
From Fig 3 it is clear that the trained data values are coherent with the experimental values for different test values.

Shaping of the metal corresponds to the data of experimental and theoretical studies, while the deformation center has a characteristic appearance, including the gripping zones of the workpiece and its rolling, repressing and extrusion.
Table 1 Data for the calculation and simulation of combined machining.
It should also be noted that the calculation for large degrees of deformation (µ = 14.3) for the temperature Tb = 550 °C was not made, since for these process parameters no data on the rheological characteristics were obtained due to the destruction of the samples (Fig. 1).
The following data chosen as the initial data and boundary conditions for modeling: the material of the workpiece is an aluminum alloy, the material of the instrument is tool steel; billet material isotropic; the conditions of contact interaction between the workpiece and the rolls were accepted according to the Siebel law with the friction index at the metal deformation in the rolls ψr = 0.9 and on the die ψd = 0.3; friction on the contact of the rolls and the die were not taken into account; relative reduction during rolling 50 %; the initial temperature of the workpiece Tb = 400-550°C, heat exchange with the environment and the tool; the initial temperature of the die (Td) and of the rolls (Tr) varied from 100 to 200°C, and heat exchange with the environment and the workpiece takes place; the number of finite elements into which the workpiece is divided was 32 thousand pieces.
These data will be used in mastering the technology of combined processing of these alloys in industrial conditions.

The above datum was recorded by DEWETRON2010 data-acquisition system and was transmitted and saved in a computer.
Wheel with higher porosity can provide a larger storage space for the debris and the debris are easy be removed from the grinding zone, which would prevent the wheel from over-loading by the reduction of the friction between the debris and the wheel as well as the friction between the debris and the workpiece.
This is because wheel with too high porosity leads to the reduction of cross-sectional area of the bond bridge, which results in greatly decrease in the mechanical properties of metal bond as well as its diamond retention.

The mechanism leading to the formation of AuNPs is a reduction reaction that involves electron transport that goes on in the cell membrane of the bacteria.
The image suggests that the reducing agent responsible for the reduction of AuCl4- to Au0 is actually within the bacteria.
It also suggests that there was a complete reduction of AuCl4- to AuNPs.
Gold nanostructures can also be incorporated into macro-porous scaffolds to increase the matrix conductivity and enhance the electrical signal transfer between cardiac cells.[66] Researchers are now trying to apply gold nanoparticles within the electronics industry for use in applications that range from data storage and touch screens, to printed text and conductive films.
Sastry, Water-dispersible tryptophan-protected gold nanoparticles prepared by the spontaneous reduction of aqueous chloroaurate ions by the amino acid.

The use of TBCs, along with internal cooling of the underlying superalloy components, provides major reductions in the surface temperature (>500 ˚C) of the superalloy.
The first reduction can be achieved by lowering the density or elastic modulus of the coating, e.g. introducing pores, or increasing the mean atom weight of the coating materials, e.g. by doping; the second reduction can be achieved by creating more reflective sites in the coating.
For EBPVD TBCs, it is the intra-columnar nano-porosity that accounts for the reduction of thermal conductivity over that of bulk YSZ.
If the decomposition of the β-NiAl takes place locally or at a different time in different places, then the volume reduction will also be localised.
This curious result, although the reason is not clearly understood, suggests that caution must be taken to interpret the PLPS data.

Cement Chemical composition SiO2 19.22 Fe2O3 3.72 Al2O3 4.65 CaO 63.82 MgO 1.76 SO3 3.31 Na2O 0.40 K2O 0.43 Physical properties Bulk density (kg/m3) Specific gravity 1362 Specific gravity 3.15 Fineness 0.41 Figure 1: Sieve analysis data for fine and coarse aggregate.
This indicates a significant reduction (28.07%) in slump with the addition of coir fibres.
This reduction in densities can be attributed to the relative low densities of the coir-fibres which is around 1.20g/cm3 [3].
The reduction of unit-weight was attributed to the increase in porosity with the addition of coir fibre and the low density of the fibres.
However, varnish coating had a negative effect on the compressive strength causing a reduction of -2.32%.

Maiga et al. [23] developed the following models using the experimental data of Wang et al. [22] Early studies performed by Ahuja [3] which shows the change in fluid behavior by the addition of 50μm and 100μm diameter polystyrene spheres in glycerine.
Angue Mintsa, Viscosity data for Al2O3-water nanofluid-hysteresis: is heat transfer enhancement using nanofluids reliable?
Angue Mintsa, Temperature and particle-size dependent viscosity data for water-based nanofluids – Hysteresis phenomenon, Int.
Doucet, New temperature dependent thermal conductivity data for water-based nanofluids, Int.
Wang, Enhancement of thermal conductivity with Cu for nanofluids using chemical reduction method, Int.