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Fig. 1 Experiments setup It is noteworthy that UAT resulted in a considerable reduction of both cutting force components and surface roughness compared with conventional turning as could be envisaged.
The reduction of cutting force can easily be evidenced from Fig. 2.
As mentioned earlier different experimental data have been used for training and testing the networks.
It can be evidenced from Fig. 4 that a good agreement exists between the networks prediction and the experimental data.
Fig. 4 Comparison between the results of the trained networks and the experimental data, a) for cutting force, b) for surface roughness Conclusions Neural networks have been developed in the present study for prediction of cutting force and surface roughness in ultrasonic-vibration assisted turning of 1.1191 steel.

To observe the performance of flood control, a proper hydrology-hydraulics model of reservoir routing should be applied by taking into account the latest condition data, both physical reservoirs including the change in storage characteristics and inflow flood hydrograph.
Reservoir characteristic data was obtained from echo sounding survey in 2005 in which sediment storage reservoir volume at the controlled water level (CWL) of +135.30 m MSL was 11×106 m3.
The rainfall data and catchment characteristics including the current land use and vegetation cover were analyzed to obtain the updated design flood hydrograph of 60 years return period (standard flood), 500 years return period (extraordinary flood), and PMF.
The data of the reservoir storage characteristics are the result of echo sounding measurement in the year 2008.
Acknowledgment Our gratitude’s are delivered to Jasa Tirta I Public Corporation and Bengawan Solo River Basin Organization for the permission to conduct a study in the Wonogiri Reservoir, as well as the data provision and intensive discussions related to the sedimentation issues and the development of the reservoir operating rule for flood control purpose.

Data Reduction.
Before calculation, smoothing of the contour date was extremely important to decrease data fluctuation.
It can be seen from Fig. 3 that a lot of excessive stresses exist in the center because of the unsmoothed data.
The data for Al 7075 was used instead of Al 7050 in the reference, and the data was also replaced in the verification model.
Altan, Prediction of residual stresses in quenched aluminum blocks and their reduction through cold working processes, Journal of Materials Processing Technology. 174(1) (2006): 342.

information pre-ordering Firstly, using the information receiver collect network data.
Continuous data are processed by using the normalization method to organize to reduce the impact of differences in data on the test results. 2）Data simplification In the network data decision-making table, the importance of single attribute is different.
Because some of the information is repeated, it will form a large amount of unrelated data groups, it is necessary to remove irrelevant or useless information data.
In consideration of the basic principles of rough set data reduction operation is based on the importance of data to make coding, if it wants to get more concise information, the steps are as follows: information data must be streamlined.
So, the use of this algorithm can simplify the data. 3）Selection and screening of rules the obtained data attributes setis selected to constitute to a rules set, the use of way is: the single attribute "attribute - value" of single sample data is corresponding to each other in the information network which has make streamline one by one.

For this purpose, a Gleeble 3800 machine has been employed to simulate the induction hardening process and provide dilatometric phase transformation data.
Grain maps of this martensite were initially assembled from the data sets with a grain boundary tolerance of 3-5 degrees.
Subsequently, the parent austenite orientation map was reconstructed from this data with a two-step reconstruction algorithm.
Fig. 2(b-f) shows a summary of how a prior austenite grain has been reconstructed from the EBSD data of a martensitic microstructure.
Fig. 4 presents the experimental data and also excellent nonlinear fitting results for the samples.

Compared to the field of composites, textile manufacturing is more mature regarding data generation.
Although available data is sufficient, all datasets were manually labeled by experts.
Applied on two feature vectors, the CD measures the similarity of two data samples.
The performance of both models pre-trained on CarboKlev and ImageNet decrease with the reduction of available training data.
Therefore, the model performance can be improved through e.g. data augmentation methods.

And this leads to a considerable reduction in the run-out error of spindle head.
Based on the data in Table 1, the radial run-out error and axial run-out error are calculated and the results are 3.3mm and 2.8mm, respectively.

It is indicated by the results that the residual stress in surface layer of slab material is the pressure stress and inside the plate is the tension stress under the smaller press quantity, the residual stress in surface layer is the tension stress and inside the slab is the pressure stress under bigger press quantity, the maximum residual tension stress in the slab becomes bigger with the increment of the reduction.
Data Input Established Model Material Property Work Piece Propertiy Rolling Process Status Loading Node Force Matrix Resolving Counting Stress Constringenc Counting Stop Results Post Next Increment Adaptive Mesh Result Output Load Increment N Y N Y Fig.1 Analysis flowchart Model Condition Fig2 shows the temperature and force coupling roll model.

This has great significances to the application of the polymer wool on noise reduction in automobile.
Sound absorption coefficient and its validation To ensure the simulation result more accurate, firstly, we use some known parameters to make a simulation and compare the simulation results with the experimental data which are measured carefully and precisely.

Figure 2 The absolute encoder Figure3 The signal processing circuit 4.Data communication 4.1 CAN Bus Being different from RS232, RS485, Industrial Ethernet and other buses, CAN Bus is based on data packet ID to carry out data transmission and reception rather than fixed points.
If data packet ID on the bus satisfies point ID table, data could pass, or will be discarded.
The data that system transmits can be divided into two parts.
One is periodic real-time data that are speed, conducting phases.
Other data like speed are transmitted in fixed region.