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The tailored blanks are hereby analyzed by their geometrical dimensions, like sheet thickness, and their mechanical properties, e.g. hardness distribution.
The mild deep-drawing steel DC04 and the high-strength steel DP600 with the mechanical properties determined in a tensile test and depicted in Figure 3 are used.
In addition to the geometric properties, the Vickers hardness was measured using the Fischerscope HM2000 system.
This difference can be explained by the material properties of the DP600.
In addition to the geometric dimensions of the manufactured tailored blanks the mechanical properties, like the hardness, are another field of interest for the evaluation of the tailored blanks.

Advances in electronics and electrical engineering now enable measurement and analysis of the specimens being tested and evaluating their material properties.
In civil engineering, investors and designers constantly require better properties of building materials, especially those properties that guarantee the material’s quality.
Great caution must be taken during the design of the composites because their final physico-mechanical properties are determined by the properties of the components and their uniform distribution.
Some studies show that the final distribution of the fibers is affected by the placement process [6, 7].
The properties of fresh SFRC are measured by standard tests, identical to those used for testing ordinary concrete, i.e. the set of standards ČSN EN 12350 [8].

Multi Objective Optimization of Turning Operation Parameters for Aluminium Alloy Using Grey Relational Analysis Method Bhanodaya Kiran Babu Nadikudi Mechanical Engineering Department, Sreenidhi Institute of Science and Technology, Hyderabad, India uday.nadikudi@gmail.com Keywords: Aluminium alloy, machining parameters, orthogonal array, surface roughness.
In this contrast, the primary lightweight materials like aluminium and their alloys are placed in first in holding high strength to weight ratio favourable property.
Grey relational coefficients establish the relation between the turning process and their factors [7,8].
Rahul Sharma et al. [18] revealed the effect of CNC turning parameters with AA6262-T6 aluminium alloy material and concluded that the both feed rate and depth of cut are affecting the material removal rate.
Sivaraman et al. [20], Siva surya et al. [21] and Verma and Saranya [22] were realized that the cutting speed is dominating parameter that affects in turning of aluminium alloy materials.

It may affect the particle deposition rate in electronic devices.
Inclusion of these terms not only generalize the earlier studies but also justify the real world problem accounting for the thermal energy due to external body forces such as electromagnetic force and internal resistive forces due to viscosity, affecting the momentum transport and hence the flow model.
Flow Stability In addition to free stream turbulence which has an adverse effect on the stability of laminar flow past a flat plate, other factors affecting transition from laminar flow to a turbulence are: (a) For the accelerated flow the critical Reynolds number , increases, whereas for retarded flow , decreases.
Further, Table 2 reveals that all the physical parameters affect the heat and mass fluxes at the plate surface in the same way except the destructive chemical reaction parameter, which affects adversely to enhance the rate of heat transfer and to decrease the mass transfer rate.
Enhancing thermal conductivity of fluids with nanoparticles, in: The Proceedings of the 1995 ASME International Mechanical Engineering Congress and Exposition, San Francisco, USA, AMSE, FED 231/MD, 66, (1995)

As a new technology of neural networks applied to a wider variety of fields, because it is compared with the conventional neural network control technology has its unique properties, can in theory be any non-linear mapping approach can be an effective solution nonlinear complex systems modeling, forecasting and other issues.
And fault-tolerant neural networks are relatively strong, even if part of the neural network is not functioning, it will not affect the final calculation case.
Learning BP neural network is composed of two back-propagation process forward propagation and error signal composed in the forward propagation, input samples from the input signal into the network layer, and finally sent to the output layer through the hidden layer processing, if the output does not match the expectations, put the back-propagation errors, according to changes in the above weight adjustment factor to adjust the weights until the output of the output layer of the network to meet the requirement.
Patra: Mechanical Systems and Signal Processing, Vol.21 (2007), p.466-479 [5] O.

The characteristics of macro gather some structure and micro porous structure and the mechanical behavior and durability are connected closely.
Many researches show that pore structure characteristics strongly influence the impermeability, air tightness, frost resistance, corrosion resistance and other physical properties and the strength, stiffness and toughness and other mechanical behavior of concrete material.
Table 2 shows the physical property of cement.
The vitreous content is 95%, quality factor>1.2.Table 5 shows the chemical composition of slag powder.
In a early multi-layered adsorption, when the absorption between absorbent and gas is mainly affected by the Van der Waals’ force (S<1/3), S=(Ds-3)/3.

Table 3 presents the physical properties of the cement.
The physical properties of each admixture are listed in Table 5 and 6.
The physical properties of each polymer are listed in Table 7.
Namely, low diameter and length of PVA-F made the distance between each fiber closer in cement matrix, which is helpful to enhance axial tensile capacity as well as mechanical properties of cement matrix due to bridging effect of PVA-F. [10] Spalling properties.
Kim et al. : Strength Properties of Polymer Modified Cement Mortar, Proc. of KCI Fall Conference, Vol.17(2005), pp.571-574 [10] L.R.

The solution method of our numerical model is integral finite difference and in the model the width and length of each simulated block grid is 35m and 70m respectively.Because the heat exchange between caprock and reservoir rock is little and has little impact on heat extraction rate to CO2-EGS for two-dimensional system[8],the model is regard as closed system. lateral boundaries are zero flow boundaries. 2.2 schematics with different CO2 injection temperature and simulation tool There are many factors influence the heat withdrawal in CO2-EGS.
And the key factors include three aspects, injected CO2 temperature, pressure setting of injection well and production well, and the distance between wells.
CO2-EGS is a complex system within thermal, hydraulic and mechanical multi-field coupling.
In summary, the changes of injected CO2 temperature can affect the heat extraction rate of EGS significantly.
[6] Fu Jinhua : A study of the Sealing Properties of the Palaeozoic Caprocks in Erduosi Basin.Atural Gas Industry Vol.6(1991), p.6-11 [7] KANG Ling,WANG Shi-long and LI Chuan: Reservoir technology in enhanced geothermal systems.

In a first part the microstructural mechanisms responsible for the cyclic softening of tempered martensitic steels and fatigue rupture are recalled, and their relationship with the mechanical properties are described.
In such conditions, steel may be subjected during its life to a continuous evolution of its microstructure and related properties
Nevertheless, probably due to experimental difficulties, microstructural parameters (connected with small secondary carbides and dislocations inherited from the quench) giving suitable mechanical properties after heat treatment and during fatigue are never totally investigated.
Die material development requires the optimisation of each elements in order to achieve special properties for each application.
Dwell times also introduce time dependent effects and thus, affect the lifetime.

Objectives The application of thin cutting tools for stone sawing processes is influenced by several factors.
Material properties which influence the machinability are the constituents and the composition that vary even for the same kind of rock [3].
The balance to be found here is additionally strongly influenced by the vibration patterns of the tool which relate segment properties to steel centre characteristics.
The other influence factors are given by the properties of the saw blade like: the diameter ds, the diamond concentration C, the partition of the segments at the circumference λ and the geometry of the diamonds grits r.
However it is necessary to investigate all influencing factors to be able to achieve the required industrial quality standard.