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Hot ductility of tested samples was measured as the percent reduction in area.
Weld Metal Maximum Crack Length Material Threshold Strain (%) Saturation Strain (%) MCL (mm) Incoloy 903 BM 0.25 4.0 0.56 Kovar BM 2.0 2.0 0.12 Incoloy 903 WM 0.25 4.0 2.3 Kovar WM 2.0 4.0 0.58 Table 3 - Varestraint data summary for Kovar and 903 Gleeble Test Results.
Table 4 presents the compiled data.
Hot Ductility Test Results for Incoloy 903 Table 4 - Gleeble Hot Ductility data summary for Kovar and Incoloy 903 Material DRR NST-DRT (C) NST-NDT (C) Incoloy 903 0.82 130 44 Kovar 0.97 14 7 Conclusions The Varestraint weldability test and the Gleeble hot ductility measurements gave consistent results.

However typically GBE processing involves several iterations (e.g. 3-7) of cold work (e.g. 20%- 40% reduction) and annealing (e.g. above the recrystallisation temperature for a few minutes) which results in a fine grain size and certain property improvements [5].
Data were collected using an HKL Technology Channel 5 electron backscatter diffraction (EBSD) system interfaced to a Philips XL30 scanning electron microscope operated at 20kV.
The very large data sets acquired give confidence in the counting statistics.
Data from GBE brass have approximately the same number fraction of Σ3s (38%) than the GBE Cu, a lower number fraction of Σ9s and Σ27s but a markedly different microstructure, where twins are not incorporated into the grain boundary network and instead the network is modified indirectly via the modification to the boundary crystallography that results from twinning.

The shape of a moving GB is a source of very useful data with regard to • The orientation dependence of GB surface tension and GB mobility; • Interaction between a moving GB and different kinds of obstacles.
Experimental Only bicrystal techniques permit us to obtain reliable and reproducible data on GB mobility.
Despite the change of the GB shape we suppose that the driving force is provided by reduction of GBs energy and reads (per unit area): 500µm 618K a p γ2 = , (3) where γ is the surface tension of GB and a is the width of the shrinking grain.
Experimental data correlated well with predictions from the impurity drag theory [12, 13].

A lot of data can be obtained from reograph such as scorch time, tS2 as the premature of vulcanization occur, minimum torque, ML as the measurement viscosity of uncured compound, maximum torque, MH where the maximum viscosity of rubber compound can be obtained, optimum torque at 90%, M90, and also the optimum cure time, t90.
The minimum torque (ML), maximum torque (MH), scorch time (tS2) and optimum cure time (t90) data were been collected from rheographs for each rubber compounds.
For optimum torque (M90) and curing rate, the data were obtained by doing some calculation.
Meanwhile, the reduction was indicated to the decomposition of crosslink by thermal ageing [13].

Observations and force measurements on plant surfaces with fine roughness have demonstrated a strong reduction in the adhesive force exerted on insects [8].
Here we chose totally 20 female drosophilas to collect their adhesive force data.
Results and discussion The adhesive force data tested on the different friction plates was shown in Fig. 5.
Conclusion The self-designed lever-like testing equipment was invented to successfully test accurate adhesive force data.

From the data of Fig. 3b, the applied stress can be derived from the applied bending force and this can be plotted against the amount of flattening.
This is a reduction due to the softening process.
Firstly, it can be seen that the data points exhibit a bi-modal behavior.
The mechanical characterization data presented result from the complex interplay of heat transfer, cellular transformation, and viscoelastic mechanisms as verified by other investigators.

One formula describing the relationship under various conditions was proposed based on the experimental data, which would be helpful for the operation of PID in practice.
The reason for this phenomenon was that large volumetric flow rate led to large workload and reduction of the screening efficiency.
Based on the experimental data, the regression analysis could be easily got using SAS.
Fig. 4 Fitting results based on dimensional analysis when viscosity is 35 mPa·s As shown in Fig. 4 and 5, the curves fit well with the experimental data and the trend is that the loss rates of steel particle increase with the increasing volumetric flow rate, viscosity and steel particle ratio.

Navid et al [3] asserted that dynamic loads such as impact and drilling process caused significant reductions in the composites stiffness and strength.
Once all the data have been measured, the volume fraction was calculated using Eq.1.
Tensile stress-strain curves for woven and stitch bi-axial composites Flexural properties The average flexural properties data for woven and bi-axial composites are depicted in Table 3.
Flexible properties for different batch of GFRP specimens Sample Woven Fiber Bi-axial (±45°) Fiber Ultimate Flexural Strength (MPa) Flexural Modulus (GPa) Ultimate Flexural Strength (MPa) Flexural Modulus (GPa) 1 304.64 21.25 168.80 11.79 2 341.64 20.07 130.09 10.61 3 312.58 19.68 128.60 10.36 4 320.17 21.15 120.26 8.73 5 311.88 19.80 104.51 7.85 Mean 318.18 (±12.72) 20.39 (±0.67) 130.45 (±21.22) 9.87 (±1.40) Residual tensile properties The average residual tensile strength data for woven and bi-axial for both types of drill are shown in Table 4.

Table 1 Input data considered in Helical Gear design Parameters Values for Helical Gear Pair Density of the material : 40Ni2Cr1Mo 28 8.836×106 kg/mm3 Allowable bending stress / crushing stress 400 N/mm2 / 1100 N/mm2 Young’s modulus of the material 2.15x105 N/mm2 Co-efficient of friction 0.08 Ratio between thickness and center distance (ψ) & Ratio between the thickness and module (ψm) 0.3 / 10 Design constraints are to be considered for the design of above helical gear are, crushing stress, bending stress, center distance, normal module, gear ratio and number teeth on pinion represented in Eq. 8, Eq. 9, Eq. 10, Eq. 11, Eq. 12 and Eq. 13.
Results and Discussion The complete optimization problem of helical gear pair have been formulated in terms of design variables and the optimum values of the objective functions for Power, Weight, Efficiency and Center distance and the design variables (m, b, z and P) influencing the objective functions are obtained with respect to the minimum COF value by implementing the input data provided in Table 1.
Weight reduction reduces the amount of material consumed while manufacturing the component.
[10] PSG College of Technology: Design Data Book, (Kalaikathir Achchagam, Coimbatore, 2008) [11] Baskar N, Asokan P, Saravanan R and Prabhakaran G: International Journal of Advanced Manufacturing Technology Vol.25 (2005), p. 1078-1088

A reliable and reproduce characteristic fingerprint of coffee flavor was established [4] and a group data of retention time (RT) of coffee flavor were obtained based on this method.
Experimental section Data preparation.The experimental values for the retention time of coffee flavor compounds are taken from the literature [4].
The data set is randomly separated into a training set of 36 compounds and a test set of 16 compounds.
After the heuristic reduction, the pool of descriptors is reduced to 160.