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Introduction Tube hydroforming has some advantages such as the reduction in the number of elements and it has been applied in the manufacturing of industrial products like tubular segments with complex cross-sectional shapes; used to form the structural component of automobiles and aircrafts [1].
It is caused by decreasing the number of grains [5], increasing the ratio between wall thickness and grain size (t/d) and increasing the effects of surface scalability [6].

At low water content «liquid bridges», i.e. water menisci, are formed between soil grains which restrains their rearrangement and can cause a decrease in dry density [1].
At that point the soil grains become as closely packed together as they can under the application of fixed compactive effort.
At this condition the volume of soil remains nearly constant as water and soil grains are relatively incompressible and air can be no longer expelled.
It was shown by a number of authors [5]–[7], that certain structure is imposed on soil by compaction, which depends on soil type, compaction method and energy, and water content.
In order to reduce the number of variables and to facilitate the control over soil granulometry it was decided to use artificial soil mixes.

The number of the inside parameters is equal to the number of phases occurring during the transformation.
FB1 can be one of the chemical compositions of the alloying elements and FZ1 can be the size of the grain boundary or internal stress value in one direction.)
The parameters Pi1 and Pi2 can be the volume fraction of the phases in the case of transformation diagrams, where the number of the inside parameter is equal to the number of the volume fractions and to the number of the phases, which are changing during the transformation.
The parameter of a property diagram can be grain size, chemical concentration, hardness, one of the mechanical properties, the thermal properties, the rupture properties, toughness properties, transition temperature, depth of different corrosion products, remaining stresses, hydrogen content, depth of decarbonisation, diffusion factors etc.

The research of Harrison and Farrart on HSLA steel weld deposits showed that oxide inclusion can refine microstructure and induce the formation of a large number of small acicular ferrite, resulting in the improvement of strength and toughness as well as the increasing crack resistance [1].
Soon afterwards, the Japanese experts put forward the concept of oxide metallurgy, which was that by controlling the size, distribution, composition and quantity of oxides, they can supply a great number of nucleating sites of solidification and at the same time accelerate the precipitation of sulfide, nitride or carbide, which can effectively prevent grain growth [2].
Several investigations have been conducted to characterize the role of those inclusions on grain refinement [3-6].
From Fig. 5, it can be seen that complex inclusions provide a large number of nucleation cores, which are helpful to the formation of fine acicular ferrite.
(3) Sub-micron complex inclusions with the mean size of 0.67μm were dispersed in TiO2 added weld metal, which result in the formation of a large number of fine acicular ferrite.

For a few number of cycles at fracture (Nf < 104 cycles), it is the low cycle fatigue domain; for intermediate number of cycles at fracture (10 4 number of cycles at fracture (Nf > 107 cycles), it is the very high cycle fatigue domain (gigacycle domain) [1].
For the smallest number of cycles at failure, the initiation sites are multiple and located on the surface.
For intermediate number of cycles at failure, there is only one surface initiation site, whereas in gigacycle fatigue domain, the initiation site may be located in an internal zone or at the surface.
One hypothesis is a grain of retained austenite, whose glide dislocation planes is in the maximum shear orientation.
This behaviour comes from the phosphore segregation in the prior austenite grain boundary during the carburizing process (austenitization)[6].

The fine aggregate was medium sand with a mean grain size of 0.25-0.5 mm.
The coarse aggregate used in this concrete mixture was gravel with a mean grain size of 5-25 mm.
The number of cracks and their length as well as width were measured after 24 hours.
Comparing with the reference specimen, the number of cracks of all the experimental specimens was reduced significantly.
iv) Compared with the pure mortar, the number as well as the maximum length and width of cracks in jute fiber reinforced mortar were reduced significantly.

The establishment of granular model The powder injection molding feedstock is typically contains 60% mass ratio of powder granulars, therefore, in the simulation process, it will dispose the powder injection molding feedstock into the grain flow consisted by powder granulars and treat binder’s impact on granular into the granular damping.
The computational formula of the partial damping model of granulars is: ; （i=1…3） （4） ; （i=1…3） （5） Where,is the total external force,is the quality of grain,is component of acceleration,is resistance, is the partial damping.
Fig.1 3D model of blade Fig.2 Simplified model Fig.3 Filling status of blade cavity In order to simplify the amount of calculation and reduce the number of powder, it should expand the radius of powder granular to 0.2 mm appropriately.
Tab.1 Process Simulation Parameters granular number granular radius/[mm] normal stiffness Kn/[N·m-3] tangential stiffness Ks/[N·m-3] gate diameter/ [mm] damping coefficient[Ns/m] 2×104 0.2 1×106 1×106 0.75 0.7 Fig.4 Velocity vector of granular Fig.5 Force chain of central cavity Fig.6 Force chain of posterior cavity From the distribution of the granular velocity vector of the blade shank in Fig.4, we can find that most of the granulars move forward along the injection direction and the granular density of ahead of the interface are significantly lower than the central region, moreover, there have not been fully filled by granulars in the corners.

For the 1C steel (Fig. 2a), a large number of lamellar cementite were observed when austenitized at 737oC (just 10 oC higher than A1), so the degree of spheroidization is lower (around 40%).
When the austenitizing temperature is too higher, the number of the cementite particle retained in austenite during austentizing is quite few, thus the lamellar mode eutectoid transformation is likely to prevail during cooling.
Some blocky cementite particle forms on the austenite grain boundary, while smaller cementite particles are within austenite grains.

Besides, nowadays there are a number of sludge depositories where the sludges were stored without any packing.
Average sample of dry sludge The main task of the particulate analysis is to determine the grain fineness of the sample and the evaluation of the nickel distribution by the size grades.
Such the analysis will make it possible to evaluate the fraction numbers with a low nickel content, which could be removed from the process during the material preparation for processing.
After the grain fineness, each fraction was analyzed for nickel content.

Table 2 Stress testing plan Load order σ3 [kPa] σd [kPa] σ1[kPa] Cycle Numbers preload 30 50 80 1000 1 60 30 90 100 2 45 30 75 100 3 30 30 60 100 4 15 30 45 100 5 60 55 115 100 s6 45 55 100 100 7 30 55 85 100 8 15 55 70 100 9 60 75 135 100 10 45 75 120 100 11 30 75 105 100 12 15 75 90 100 The haversine loading wave was imposed on the testing sample.
The loading cycle number is 100. the last 5-10 times data of recoverable trains were utilized to calculate MR.
Table 3 MR testing results Load order Stress MR values with different moisture contents [MPa] σd σ3 14.1 [%] 16.3 [%] 20.7 [%] 23.2[%] 25.6 [%] 27.5 [%] 1 30 60 129.13 111.17 92.08 79.53 61.61 28.98 2 30 45 116.56 99.29 82.32 68.46 56.19 25.27 3 30 30 108.10 91.75 71.57 59.67 47.83 21.51 4 30 15 100.99 70.71 62.51 43.77 38.09 18.46 5 55 60 107.53 81.00 71.97 58.73 48.63 25.93 6 55 45 92.10 74.27 63.11 52.75 44.38 23.27 7 55 30 86.48 66.87 53.54 47.86 38.99 20.17 8 55 15 74.61 57.63 47.19 38.73 31.56 15.20 9 75 60 88.57 81.18 60.66 50.13 40.20 20.07 10 75 45 81.28 69.89 55.28 47.76 38.28 18.65 11 75 30 76.20 62.74 46.59 44.14 33.70 14.76 12 75 15 64.62 53.40 40.66 36.45 25.50 9.97 Prediction Model Establishment It has been reported that MR of fine-grained subgrade soils typically decreases at increasing stress levels.
The bilinear model (Thompson and Elliott, 1985) was commonly used for fine-grained subgrade soils expressed by the resilient modulus- deviator stress relationship given in Eq. (3). (>0, >0, >0, >0) (3) Where σd is deviator stress, k1,k2,k3 and k4 are parameters.