Search results

Structural integrity and life of welded joints made of a micro-alloyed low-carbon fine-grained normalised high strength pressure vessel steel, P460NL1 is presented [1].
For this purpose, a number of specimens were tested via bending moment applied to them, using RUMUL Fractomat 7609/213 device, at the Military Technical Institute in Belgrade, Serbia.
While these results were correlated to a number of other factors, as described in [1], here the main focus will on da/dN-ΔK curves, which were used to determine C and m values.
In the following section, the stress intensity factors and the a-N (crack length vs. the number of cycles) for specimens 5 HAZ (figure 6) and 22 WM (figure 7) will be shown.
Crack length vs. number of cycles curve (left) and stress intensity factors (right), model 5 HAZ Figure 7.

This study verified that alpha-gypsum can be trouble free prepared by gypsum dehydration in solution of a number chloride salts, among others CaCl2, MgCl2 or NaCl.
Similarly, as in the X-ray, images taken by electron microscopy showed only massive, automorphic grains of this double sulphate salt. .

This was explained by the small grains size because the phase distribution is nanoscale in this coating.
Influence of the number of indentation on the depth of impression The increase of cycle number increases the maximum indentation depth.
So there is a more substrate effect that can produce the nucleation and growth of cracks by increasing the number of cycles.
Moreover, the influences of the number of cycles on the mechanical properties were plotted versus the loading rates, and are shown in Figure 4.
The variation of the hardness and the modulus as a function of cycle number for different loading rate was studied.

The reason that should urge ceramists and cement people to cooperate is that both ceramics and cementitious materials continue to be granular materials, whose properties are controlled by microstructure (grain size or porosity).
The grain-size distribution of aggregate is a key parameter to control packing efficiency.
In RPC, the improvement of rnicrostructure is obtained by eliminating coarse aggregates and increasing packing by carefully selecting the grain size of the different powders (cement, silica fume and quartz flour).
The answer to the fi rst problem is known: to work with an optimized grain-size distribution (coarse aggregates mixed with medium-size grains to fill the largest interstices and ultra-fine particles to fi ll the smallest interstices), to use a low water/solid ratio, and to add superplasticizer.
Chemical processings like precipitation, coprecipitation, hydrolysis, pyloysis and/or physical processings like evaporation, burning and spraying, might give larger supersaturation to bring about formation of a huge numbers of nuclei.

First, the odd numbered outer dies and inner dies press the trial tube.
Second, the even number outer dies and inner dies press the trial tube return to the starting position.
On the other hand, the test machine of manufacturing method 2 modifies the number of dies (Fig. 4).
The number of outer dies is four and the number of inner dies is two.
The reason to prepare for two types of machine is to investigate how the number of dies has an effect on strain and thickness of the tube.

Angella et al. [2] conducted hot torsion experiment and proposed an analytical model to describe the flow behavior of the 316L based on the unusual grain structure evolution.
The number of test data conforms to the general rule [5] that it should be 10-20% of the training data set to check the performance of the network.
Up to now, no theoretical guidance to the determination of the number of hidden layers in the neural network and the number of neurons in each hidden layer can be followed, while the accuracy of the predicted results varies greatly with different structures of the ANN.
For the choice of the number of neurons in each hidden layer, the principle is to choose a number as low as possible to simplify the network on the basis that the data from the input-to-output is mapped well.
The optimum architecture of the ANN is 3-5-6-1: the input includes , T and , the output is , the number of hidden layer is two with 5 neurons in first layer and 6 neurons in second layer.

Fig.1 Principle of the special spiral rods grinding process i i F l T = (1) Subject to the constraints: bPP0 << (2) max a RR0 << (3) maximin FFF << (4) In Eq.1, i is the section number of numerical control grinding interpolation program; il is the interpolation length of number i section which is only relevant to the workpiece outline and can not be changed; Fi is the feed rate of number i interpolation section.
The wheel cut depth ai of number i section is related to feed rate Fi as shown in Eq.5, where V is the principal axis velocity.
In the course of parameter optimization, the grinding power is the most important constraint, and can be calculated as follows [7]: ε/2 e εε-1ε)/2(1 i -2ε )(dVvakbωP − − = (8) Where ε is an empirical exponential, k is a constant, b the grinding width, ω the interval of the abrasive grain, de=(dw+ds)/dwds the equivalent diameter, dw the diameter of the workpiece and ds the diameter of the wheel, v the workpiece linear velocity and V the wheel linear velocity.
Numerous constraints and number of passes make the machining optimization problem more complicated.
Choose a maximum allowable number tmax.

The following results are observed: The number of the micro inclusions is 11.37 piece /mm2 under unsteady casting.
The number of the macro inclusions is 890.18 mg/(10 kg), and 275.4 mg/(10 kg) under steady casting.
The ratios of their respective share of the total number are tested: 6:3:1.
The number of micro inclusions is1.38 times comparing to that under steady casting.
The number of the macro inclusions is 3.23 times comparing to that under steady casting

But Lu et a1. [2] studies show that latent semantic representation model for the task of the performance of fine-grained information on text mining task is not prominent.
As can be seen from the figure, a document composed by a number of feature items, a feature items can be mapped to one or more of ontology concepts and their property values.
Reduce the dimension using Formula (3) and save the first k non-zero-dimensional vectors as a d×K real-number matrix D0S0. 3.
Text clustering: use the real-number matrix D0S0 to calculate the similarity between texts and cluster them using the improved k-means algorithm.
Recall =Number of Correct ClassificationNumber of Actual Classification Precision=Number of Correct ClassificationTotal Number of Texts TABLE 1 : Clustering results based on domain ontology-latent semantic analysis Domain Recall Ratio And Precision Ratio Recall Ratio Recall Ratio Automobile 86% 89% Finance 81% 79% Tourism 92% 82% Employment 79% 71% Education 84% 78% TABLE 2 : Clustering results based on VSM Domain Recall Ratio And Precision Ratio Recall Ratio Recall Ratio Automobile 80% 78% Finance 77% 75% Tourism 86% 82% Employment 76% 74% Education 85% 72% From the clustering results, it’s evident that the adoption of domain ontology helps improve the clustering.

ECAE process is a novel technology which allows us to obtain materials of sub-micrometric and/or nanometric grain size as a result of accumulating very high levels of plastic deformation in the presence of high hydrostatic pressure.
The remaining dimensions from Table 1 are defined as a function of the teeth number, in this case 14, and the module values chosen: 1 and 1.5.
In this way, an initial meshing with elements Hex 8 was achieved, where the number of elements ranged from 12000 to 18000.
A coarsening factor of 1.5 was taken so as to reduce the number of elements towards the inner part of the solid.
After remeshing, the number of total elements in the simulation increases where the maximum value of these ranges between 60000 and 90000.