Search results

The potential advantages of Al2O3-forming austenitic (AFA) stainless steels for use as hightemperature structural alloys have long been recognized, as evidenced by a number of alloy development attempts [e.g. 2-6].
All alloys were solution heat-treated in the 1200-1250°C range, cold worked 40-70%, and recrystallized at 1200- 1250°C to control alloy grain size (Table 1) [10].
GS is average alloy grain size rounded to the nearest 10 µm.
Alloy GS Composition wt.% Ni Cr Al Nb Ti V M o W Cu M n Si C P A 160 26 13.7 2.8 0.6 - - 1.3 0.15 ** 0.2 0.2 0.04 ** B 40 20 12 3.9 0.60 0.11 ** 2 .9 0.5 1.9 .13 0.08 0.03 C 40* 20 14.2 2.9 0.60 0.11 ** 2 1 0.5 1.9 .13 0.1 0.04 D 50 15 11.8 2.9 0.6 ** ** - 0.2 2.9 1.9 0.6 0.09 ** E 40 20 14.2 2.5 0.86 ** ** 2.5 ** ** 2 .15 0.08 0.04 F 50 20 14.4 2.9 1.48 0.11 ** 2 1 0.5 1.9 .13 0.09 0.04 G 60 21 14 2.3 3.01 ** 0.19 3.1 ** ** ** ** 0.02 ** * bimodal grain size distribution, grains in the ∼100-300 µm size range were also present.
The number shown is the average size of the smaller grain size range only. ** Cu, Si, Ti, V, W at ≤ 0.02 wt.%; P at ≤ 0.005 wt.% Results and Discussion SEM cross-sections of selected alloys after 100 h at 900°C in air are shown in Fig. 1.

The values of true density of clinker minerals and a number of additives are shown in table 1.
If the particle size of the additive is less than the binder particles, the dense packing of particles is achieved with lower coordination number, i.e. a smaller number of particles of the binder surrounding the particles of the additive.
The average particle diameter of the binder and additives should be determined from experimental values of the specific surface, grain size analysis data.
When the average volume grain size of the filler is between 30 and 40 microns (wollastonite, diopside) optimal concentration is 70 to 80 wt. %.
When the average volume grain size of 10 µm or less (limestone powder, silica fume) optimal concentration is reduced to 40 – 50 wt. %.

Building the system of particle model In order to make simulation more accurate,five real typical stone shapes from stone grain heap were found to build the particle model as a reference in EDEM.
(a) Spherical stone (b) Cubic stone (c) Triangle stone (d) Cone stone (e) Long stone Fig.1 Five real typical stone shapes (a)spherical particle (b)Cubic particle (c)Triangle particle (d)Cone particle (e)long particle Fig.2 Five simulation particles The number of particle size distribution was normal distribution, each particle size included five simulation particles.5050 particles were generated by particle factory in EDEM.
Table.1 The number of particle size distribution Particle size (mm) Totle Sphere Long Cube Cone Triangle 15 300 300 No No No No 20 400 80 80 80 80 80 25 500 100 100 100 100 100 30 600 120 120 120 120 120 35 700 140 140 140 140 140 40 800 160 160 160 160 160 45 700 140 140 140 140 140 55 450 90 90 90 90 90 60 250 50 50 50 50 50 70 150 30 30 30 30 30 80 100 20 20 20 20 20 90 50 50 No No No No 100 50 50 No No No No DEM simulation 20 groups test data shown in Table 2 were used to simulate screening processes in EDEM.
Screening processes were shown in Fig.3.Vibrating direction angles were random number between 5°and 70°, leaning angles of screen were random number between 3°and 20°, vibration frequencies were random number between 5Hz and 35Hz, amplitudes were random number between 3mm and 12mm.

Grain sizes of the diamond material used are d=2, 5, 10 and 25µm.
Resistivity was measured on the 4 kinds of EC-PCD having different grain sizes using four-point probe method.
Fig.8 shows transition of grinding force against number of passes.
(2) Figuring out of optimum grinding conditions: Wheel type, wheel grain size, grinding speed, feed rate, depth of cut, type of working fluid and grinding method (e.g. cup wheel), etc
(4) Investigation into effects of workpiece (EC-PCD) composition: Grain size of the diamond (single size and dispersed sizes) (5) Clarification of workpiece surface condition and its generating mechanism Acknowledgements The authors would like to express their cordial gratitude to Japan Resistor Mfg.

Each node is named a fixed number in turn, and these numbers can precisely help to identify the same name point between FE software and the experiments specimen.
This means that it is not only possible to track the longitudinal and transverse strains of different grains but also their shear strains and rotations during deformation.
Every node is named a fixed numbers, this number can help to identify the same name points between FE software and the experiments specimen.
The distance and arranging rule between points are equivalent to that of FE simulation; the numbers of experimental nodes are equal to the number of nodes of FE simulation.
The coordinate grids are painted on top and bottom bimetal surface of experiment and simulate specimen, each node is named a fixed number, and these number can help to identify precisely the same name point between FE software and the experiments.

In MMRPGA, MapReduce expresses the computation as two functions: Map and Reduce, there are several Mappers and one Reducer, We divide a large population into many small populations with the same number of individuals.
We still divide this new generation population into the small population with the same number of Mapper, which forms a loop.
Alg.1 pseudo-code of MMRPGA functionmapper() { calculate the fitness; eject the result withfitness; } function reducer() { select; crossover; mutation; eject the population; } main() { for (largest loop number) { //the largest loop number of program set input method; //set population as input file Fig. 2 MMRPGA model set output method; //set population as output file set Mappers; // set Mappers’ number set Reducer; // set Reducers’ number submit job; } } From the pseudo-code it can be seen that the programmers mainly implement two functions: mapper and reducer.
In experiment, the number of individuals is 100 and the number of loop is 5, table 2 shows the speed-up and running time with the increasing of the number of Mappers.
Yussof S, Razali R, See O, Ghapar A, Din M A Coarse-Grained Parallel Genetic Algorithm with Migration for Shortest Path Routing Problem. in: IEEE Computer Society(2009), p.615-621. 6.

The results show that rolling process result in effective grain refinement and mechanical properties improvement after several rolling passes.
The increasing number of rolling passes improves strength but reduces the elongation.
After annealing materials microstructure was shown in Fig.3, re-crystallization can be observed in fig.3-c),-d), in the boundary between feathered structure re-crystallization grain gathering can be seen in fig.3-c).

In the investigated sample clusters of MPs caused by a polytype switch in the beginning of the growth could be identified by both birefringence microscopy and the flat-bed scanner setup, as well as small angle grain boundaries and threading edge dislocation (TED) arrays.
In addition, small angle grain boundaries (SAGB) can be observed at the edge of the wafer.
Acknowledgment The funding by the DFG under the contract numbers WE2107-15, SA2292-6 and DA357-7 is greatly acknowledged.

The results showed that the sanitized sludge is a coarse-grained material and, its Atterberg’s limits showed the lack of plasticity.
Leached and solubilized chemical analyses were done by the Reuse of Produced Water and Residues Primary Processing Center, following the orientations of Brazil’s Regulator Standard (NBR) number 10004.
Table 1 Grain size distribution and USCS classification for the tested materials.

Discussion 1.Deformation change Fig.1 Curve of an average deformation with impact times change(quenching & tempering treatment samples) Fig. 2 Curve of an average deformation with impact times change(quenching treatment samples) According to the Fig. 1, Fig. 2, all samples’ deformation decrease with the increase of impact number of times.
The hardness change curve of quenching & tempering T10 steel changes smoothly, hardness values increase slowly with the number of collisions, and T10 steel quenching samples' hardness values is small, but the values change obviously in 0 ~ 231000 and 251000 ~ 261000, and then it becomes less noticeable with the impact times .
(a) (b) Fig. 4 Microstruture of quenching & tempering T10 steel samples: (a)original organization; (b)organization after experiment After impact tests, these white tiny granular carbide gradually transform the original equiaxial crystal into spun, or flattened along the deformation direction, and the number of granular carbide increase(Fig. 5(b)).
As the plastic deformation and dislocation density increase, the distance of the dislocation decreases, so the dislocation interaction enhance, form a large number of dislocation entanglement, fixed dislocation and dislocation cells.
According to the charts: specimen deformation reduce with the increase of number of the impact times. 2) According to hardness value statistics, the hardness value is turned from small to big in the process of impact test; The hardness values of two kinds of samples with different heat treatment appear softening and hardening alternately with the impact times. 3) After the experiment, compared with quenching & tempering T10 steel samples, T10 steel quenching samples' grain are relatively small.