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Bending and reverse bending are the dominant material deformations in roll forming, and hence property data derived from bend tests could be more relevant than tensile test data for numerical simulation of a roll forming process.
The skin passed strips are used in a pure bending simulation to record moment-curvature data and this data is then applied in an inverse analysis to predict the residual stress profile in the material.
The moment-curvature diagram can be obtained based on the load-extension data [21].
The same optimization parameters were used in both cases of 0.5% and 1.4% thickness reduction and just the target curves (moment-curvature data in Figure 5) are different.
The discrepancies observed for the 1.4% thickness reduction suggests that there may be multiple residual stress profiles that fit the reference moment-curvature data.

However, when SiAlON ceramic powders are needed, an inexpensive method of producing them is called as carbothermal reduction - nitridation (CRN) process.
The carbothermic reduction was carried out using a tube furnace at temperature ranges from 1300o C to 1475o C under the various rate of nitrogen gas (99.99 % pure) flow.
from Can - Canakkale [supplier's data] Phases SiO2 Al2O3 Fe2O3 CaO K2O TiO2 SO4 MgO Na2O LOI* Content [wt.%] 66.8 21.0 1.8 0.1 2.0 0.6 0.2 0.6 0.1 6.7 Mineralogical composition of clay used [wt.%] Quartz K-Feldspar Na-Feldspar Kaolinite Others 36.2 11.82 0.84 48.03 3.11 *: Loss on ignition at 1000 o C The identification of the crystalline phase composition of the products was carried out by X-ray diffraction (XRD) with Cu Kα (λ: 1.54056) radiation using a RIGAGU D/Max 2200 diffractometer with 2θ, 0-70 degrees.
Insufficient amount of carbon being in the mixture of starting powder results in an incomplete reaction during the CRN process yielding a low amount of SiAlON phase (data not shown here).
An increase in N2 flow rate to 1.8 lt/min while keeping temperature low (1350o C) at constant reaction time (4h) showed a similar effect in terms of reducing SiC formation (data not given here).

Rough Set (RS) theory as an important data mining method can be utilized to extract useful features and then to simplify the data sets [2].
In order to comprehensively treat the quantitative data and qualitative data, all quantitative data should be converted into qualitative data.
One of the feasible conversion methods is automatic segment-dividing based on the statistical distribution of quantitative data, and treats each segment as qualitative data.
If the value of quantitative data is very few, the quantitative data can be treated as qualitative data directly.
RS theory is applied to process the sample data of transformer fault history, and discretize the fault data.

In the proposed model, the input data consisted of quantitative microstructural feature parameters, including the volume fraction, thickness and Ferret ratio of α phase.
The input layer is used to receive data from outside, while the output layer sends the information out to users.
The widely used unification method is (1) Where Z is the original data, Zmin and Zmax are the minimum and maximum value of Z, respectively.
Z′ is the unified data of the corresponding Z.
Such pre-processing procedure can make the train of neural network more efficient. 45 data sets (as shown in Table 1) in total for the Ti-17 alloy have been used, in which 5 data sets were randomly selected to evaluate the predictive ability of the neural network and the remaining were used to train the model.

The developed data processing algorithm for the multi-element array system was tested in the MatLab software package.
The cost of devices that allow receiving and processing data from the array is very high, since it requires processing and transmission of large amounts of data simultaneously.
The realization of the hardware of partial data processing can significantly reduce the amount of information transmitted to a personal computer or display device, as well as reduce data processing time.
Each cycle of the ADT startup block increases the value of the row counter, which is an address for the data memory.
MATLAB software package is selected for data processing and constructing an image.

The main technical data of the continuous caster after the modification are shown below in Table 1.
Table 1 - Main Technical Data of 2,300mm Slab Caster at Baosteel Zhanjiang No.
Dynamic Soft Reduction.
Dynamic soft reduction is also one of the key factors to improve the slab quality.
The dynamic soft reduction models originally provided for the slab casters at Baosteel Luojing failed to achieve stable operation.

Numerical analysis and results Elasticity and plasticity data.
Finite element analysis was employed by use of ABAQUS /Standard to forecast the numerical data of residual stress developed in cold drawing [3].
Tensile test was carried out to obtain the data of actual material for finite element analysis
ABAQUS requires the true stress and true strain data for elastic- and plastic-analysis.
But the data obtained by test are nominal strain and nominal stress, so it is necessary to convert nominal strain and nominal stress data of material to plastic strain and true stress data as shown in Fig. 1 (b) [2].

Regarding this study, a head dummy was used for the 3D scanning process for the data acquisition.
From the point cloud data, horizontal plane was used to obtain sections for the head area.
The cloud data then transfer to CATIA for 3D modeling for the padding as shown in figure 1.
A section curve from the cloud data is use as an example for this study.
It was constructed from tangent curves with a low deviation from the original data.

Clinical data are inevitably incomplete, and most knowledge discovery algorithms lack the capability to contend with missing data.
Flow-graph confers some distinct advantages in data mining and knowledge discovery.
However, flow-graph methodology is not able to comprehensively solve the incomplete data problem.
Amy, Missing-data methods for generalized linear models: a comparative review.
Rough sets, Fuzzy sets ,Data Mining and Granular Computing, Springer, 2005:1-11

Clustering analysis [2] can divide data objects into groups.
The compute of similarity is established on mean value of data in each cluster.
The process of rules mining of TSA consists of the following, · Data for TSA generated by the simulation software PSASP7.0
For each load data, set five groups of the generator power change between 80%-120% of the benchmark power.
Vol.33 (2009), p. 88-93 [2] Pang-Ning Tan, Michael Steinbach and Vipin Kumar: Introduction to Data Mining (Pearson Education, USA 2006) [3] Chuanjian Yang, Hao Ge and Zhisheng Wang: Application Research of Computers.