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This treatment yielded an average intercept grain size of 44.2 µm.
Cyclic softening of cold-worked materials occurs when the annihilation rate of the dislocations is greater than their generation rate, causing a net decrease in the dislocation density, or when a rearrangement of previously formed dislocation structure takes place with a number of cycles, resulting in an increase in the mean free path of dislocations.
However, with a number of cycles, anomalous cyclic behavior characterized by higher peak stress or retardation in the reduction of peak stress with increasing temperature was observed in the temperature range from 200 oC to 600 oC.
The positive temperature dependence of cyclic peak stresses during the initial few cycles appears to result from the effect of the initial dislocation structure formed by prior cold work, and this effect is weakened and disappears with a number of cycles due to the rearrangement of the initial dislocation structure.
LCF properties of 17% CW 316L stainless steel Peak stress ( MPa ) Temperature ( oC ) Strain rate ( s-1 ) Total strain amplitude ( % ) Plastic stain amplitude ( % ) Maximum Half-life Softening ratio 20 10-3 0.495 0.247 499 396 0.206 200 10-3 0.497 0.29 448 333 0.257 400 10-2 0.500 0.278 413 332 0.196 400 10-3 0.494 0.265 409 346 0.154 400 10-4 0.496 0.253 414 375 0.094 550 10-2 0.502 0.264 389 331 0.146 550 10-3 0.498 0.25 394 352 0.108 550 10-4 0.497 0.249 396 365 0.078 600 10-2 0.500 0.271 368 322 0.126 600 10-3 0.497 0.259 370 332 0.104 600 10-4 0.498 0.278 369 323 0.124 650 10-2 0.504 0.285 340 300 0.116 650 10-3 0.499 0.287 340 296 0.129 650 10-4 0.499 0.303 334 277 0.165 100 101 102 103 10 4 0 100 200 300 400 500 650 o C 600 o C 550o C 400 o C 200o C RT Peak stress (MPa) Number of cycles, N 17% CW 316L SS Strain amplitude = 0.5% Strain rate = 1*10 -3 /s RT 200 o C 400 o C

Introduction Components of thermal power plants have to fulfill a number of specifications, such as a high creep strength, oxidation resistance, sufficient ductility, economical manufacturing etc. [1].
The results (radius ri and number density Nv,i) were then imported into the creep equation framework to combine the two simulations.
Fig. 1 shows the result for 650°C, depicting the volume-weighted diameter and the number densities (nucleation at mart=martensitic matrix, aust=austenitic matrix, g=grain boundaries, d=dislocations, s=subgrain boundaries, VN_m_d/VN_m_s=on-particle nucleation at VN(mart,d)/VN(mart,s)).
Simulated volume-weighted diameter (left) and number density (right) of P92 with MatCalc for 650°C ageing Table 2.

Effluents often contain a large number of chemicals; it is obviously neither possible nor desirable to screen the concentration of all chemicals.
PEEP is calculated by using a battery of laboratory tests to assess the toxicity and it is given by the following equation: PEEP=log10[1+n1NTUaNQ] (4) Where N is the maximum number of obtainable toxic responses, 1NTUa is the total acute toxic value of the receiving water (receiving water body), being other 100/CE50 or 100/CE20 depending on the nature of the test n is the number of bioassays exhibiting toxic responses, and Q is the effluent flow (m3·yr-1) in the ambient water.
It considers both toxicity of effluents and natural eco-toxicity of local water resources instead of the assimilating capacity of receiving water only; (2) The proposed method avoids using empirical or uncertain formula and there is no need to use the local water quality standard (Cmax) for each pollutant and the natural concentration (Cnat) in the receiving water body; (3) The proposed method enable the comparison of WFG in different regions, regardless of local standards, and there is no need to detect the concentrations of a large number of organic and inorganic pollutants.
Wu, Engel, 2015.Comparison of volumetric and stress-weighted water footprint of grain products in China.

It is silicon killed but has fine grain and improved notch toughness.
Table 7: Details of ANN training 1 Number of layers 2 2 Number of hidden layer 1 3 Number of hidden neuron 20 4 Network Type FEEDFORWARD 5 Transfer function used TRANSIG, PURELIN 6 Number of epochs 1000 7 Training Tool nntool 8 Training function TRAINLM 9 Learning function LEARNGDM 10 Performance function MSE 11 Max fail 1000 Validation In order to conform the accuracy of prediction of the trained Artificial Neural Network software, a few experiments were carried out as per the experimental procedure and with a set of input parameters which are different from the input parameters used for training the software.

Figure .1 Geologic map of lead-zinc-iron polymetallic deposit terrain in Luziyuan, Zhenkang Town, Yunnan Province 1-The second segment of Miaopu group, Ordovician;2-The first segment of Baoshan group, Cambrian system;3-The third segment of Shahechang group, Cambrian system;4-The second layer, second segment of Shahechang group, Cambrian system;5-The first layer, second segment of Shahechang group, Cambrian system;6-Diabase dikes;7 -Observed geologic boundary;8-Measured Faults and their serial number;9-Speculate fault and their serial number;10-Lead and zinc iron polymetallic ore body and their serial Numbers.
A Mt B Gn Py Ccp C Sph+Gn Cal D Cal E Cal Gn Sph F Sph+Gn Figure.2 The typical ore textures and structures of Luziyuan Pb-Zn-Fe polimetallic Deposit A-Anhedral-subhedral granular magnetite (off-white) distributing along fissures forms stockwork construct, 200X; B- Galena (white) and chalcopyrite (yellow) filling in gangue mineral grains are formed a interstitial structure,200X;C-Galena and sphalerite filling along the fissure, form disseminated structures;D-Displacement fracture occurs in twin lamellae of calcite, orthogonal; E-Finely veined sphalerite (gray) filling in marble fissure forms vein structure, orthogonal 80; F-Galena-sphalerite close symbiosis continuous or discontinuous distribution along the bedding surface, into a ribbon Ore minerals mainly include magnetite, chalcopyrite, galena, sphalerite, pyrite, smithsonite.
In transporting process, thermal metamorphism and hydrothermal metasomatism of different degrees occurred in the early formed rocks, forming rocks dominated by the schists, marbles, diopside skarn rocks, accounting for a large number of minerals.

However it is of many advantages too, such as single-axes stress, fitting for big grain-size aggregate
Therefore the total cracks number and the width of fiber reinforced mortar decrease effectively compared to the mortar without fiber and the Cemfiber reinforced mortar has the best result.
As shown in Figs. 1 ~ 3, the Cemfiber reinforced concrete has the best performance of cracking resistance and the number and width of cracks both decrease visibly.
In other words, the effectiveness of cracking resistance of uncontinuous distributing fibers in mixture Fig.4 Results of cracks of HPC depends on the average interval of fibers and the numbers of fibers per unit volume mixture.
And when the length and diameter of fibers are similar, the more the dosage of fibers is, the more the number of fibers is.

It can be seen that the microstructure of the annealed strip consist of equiaxed grains with annealing twins, whereas the cold rolled strip contains number of deformation bands which form network in few regions.
Fig. 3 Bright field image and corresponding diffraction pattern of solution treated sample In order to generate a large number of electron diffraction patterns corresponding to various electron beam directions the double tilt specimen stage of TEM was used.
Bright field image and corresponding diffraction pattern of solution treated sample In order to generate a large number of electron diffraction patterns corresponding to various electron beam directions the double tilt specimen stage of TEM was used.
So, if the thick dimension of the plate (i.e. the habit plane) is parallel to the electron beam (and the thin dimension is therefore perpendicular to it), streaks will be recorded on the Bright field image and corresponding diffraction pattern of solution treated sample In order to generate a large number of electron diffraction patterns corresponding to various electron beam directions the double tilt specimen stage of TEM was used.

This measured value is designated as Rebound Number or Q-value.
The estimation accuracy is measured by mean absolute percentage error (MAPE) as calculated by the following equation: (1) where A is actual compressive strength, O is model output and N is the total number of data For the ANFIS model development, the training data (in excel file) are first loaded into the Matlab environment.
After loading the training dataset into the Matlab, model parameters, such as number of membership functions and membership function types for the input variables, can then be adjusted.
The proposed ANFIS model has two input variables and the researchers developed three models, ([3 3], [5 5], and [8 8]), with different number of membership functions.
R.: ANFIS:Adaptive-Network-Based Fuzzy Inference System, IEEE Transactions on Systems, Man and Cybernetics 23 (3) (1993), p. 665-685 [12] Abraham, A.: Adaptation of Fuzzy Inference System Using Neural Learning, Studies in Fuzziness and Soft Computing 181 (2005), p. 53-83 [13] Khoshnevisan, B., Rafiee, S., Omid, M., & Mousazadeh, H.: Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs.

Although a number of Fe-rich intermetallic compounds have been identified in aluminium alloys, α-AlFeMnSi phase and β-AlFe phase are the two important intermetallics in the Al-Mg-Si-Mn alloy processed by HPDC [5].
Meanwhile, the fine intermetallics were associated with fine α-Al phase and segregated in the primary α-Al grain boundaries, which were identified by SEM/EDX quantification to be the same α-AlFeMnSi phase with the typical composition of α-Al12(Fe,Mn)3Si.
Fig. 4 The accumulation of Fe content in the recycled alloy as a function of the number of recycles.
Fig. 6 shows the mechanical properties of the recycled alloys as a function of the number of recycle.
(c) The prediction of recycle times for the recycled alloy In order to find out the maximum number of recycles for the alloy to satisfy the mechanical properties, a regression of the existing data is shown in Fig.7.

Because of these conflicting objectives, it gives way for a number of optimal solutions to be generated, generally called as Pareto-optimal solutions.
In recent years, a number of multi-objective evolutionary algorithms (MOEAs) have been formulated and applied because of their ability to find multiple Pareto-optimal solutions in one single simulation run.
They have described cellular GA as a fine grained model of parallel GA implementation which derives from a cellular-automata-like computation.
The following are the values of the parameters of NSGA-II algorithm used in this study: Variable type = Real variable, Population size = 50, Crossover probability = 0.8, Real-parameter mutation probability = 0.01, Real-parameter SBX parameter = 10, Real-parameter mutation parameter = 100, Total number of generations = 100.
The algorithm performs better in our problem since the number of design variables is minimum and also the ability of scalar operator of SBX crossover to create new generations.