Search results

Flow rate data from the amplifier into the second meter reading can be easily and rapidly read.
During the experiment, if the reduction of formation variation coefficient after ball blocking is less than 40%, it is defined bad effect.
If the reduction is from 40% to 60%, it is defined moderate effect.
If the reduction is greater than 60%, it is defined good effect.
Table 5 Effect of total flow rate on diversion Flow rate ratio Total flow rate (m3/h) Injected ball number Blocking ball number of layer Flow rate of layer (m3/h) Variation coefficient before ball blocking Variation coefficient after ball blocking Variation coefficient reduction (%) Diversion effect 1 2 3 1 2 3 6:3:1 3.5 42 12 5 4 1.37 1.29 0.84 0.62 0.20 67.74 good 4.2 48 14 4 0 1.62 1.42 1.16 0.62 0.13 79.03 good 5 48 14 4 2 2.01 1.76 1.23 0.62 0.11 82.26 good 1:3:6 3.5 54 1 4 12 0.92 1.15 1.43 0.62 0.18 70.97 good 4.2 54 1 4 13 1.15 1.46 1.59 0.62 0.13 79.03 good 5 48 2 6 14 1.47 1.66 1.87 0.62 0.10 83.87 good Conclusions (1) Simulation lateral well for ball sealer diversion was established.

In practice, the reliability of system is based on robust data capture.
The data valid signal (DVAL) is not used since the data rate is high, which is regarded as invalid.
The function of video capture is to determine when to start capturing the video data according to FVAL and LVAL, which are both valid (high level) when data is effective.
If the data is in the matching range, the next state is allowed to enter Step2; Otherwise, the data is error, and it should be recaptured till matching Model[n]; Step2: Check the number of collected data for each line, and detect whether Wcapture equaling Wn, which is defined by the current data model in Model[n]; if Wcapturedata is missing, and it should be replenished.
The results indicate that our approach detects the correctness of the video data by the synchronous signal, from which the dirty data is removed from the system when the loss or interruption of video data is encountered.

However, a higher model order results in a better fit to the experimental data.
The shear test data is predicted poorly for both model orders, c.f.
The study has shown a good correlation between the experimental compression test data and data obtained from the curve fitting process for higher strain regions.
It is shown that a combination of different material test data (compression and shear test data) will lead to a decreasing accuracy of the compression fit compared to a separate fit.
A higher accuracy can be further achieved with an increasing model order but comes with a reduction of model stability. [8] and [9] have reported difficulties in finding fitting parameters of the intrinsic ABAQUS hyperfoam algorithm when considering multiple test data.

The amount and quality of texture information contained in measured or available data sets can be directly controlled.
The amount of measured data is enormous in this case.
Estimates may be appropriate if the quality or quantity of measured data does not allow a precise calculation.
A sophisticated 2D-background model helps to improve data separation of different phases.
The component fit leads to a large reduction of data without loosing of relevant texture information.

The data regarding the tools those are being used in production lines can be entered manually or can be uploaded in bulk into the tool database using a predefined format in Microsoft Excel.
In the second case data is uploaded using macros.
The new tool introduction system is supposed to: — Reduce the inventory levels and excess purchasing in store, — Have simpler tool flow, — Shorten tool flow time, — Control of tool data, — Increase accuracy and reliability of tool data (Timely and up-to-date information of tools is produced), — Assure availability of relevant data to other departments like quality etc., — Have reliable determination of new orders, — Determine optimum sequence of execution of orders, — The number of tools lost, or misplaced is reduced, — Obsolete tooling can be identified and then eliminated — Ultimately increase the economy of production.
The concerned new tool introduction system is typically a system for storing tool related data, which are useful for further purchase of the cutting tools.
In overall, it can be said that the importance of tooling data and tool management in modern manufacturing firms is studied and applied in creation of tool database.

At last, the method is applied to continuous data instances, and achieves a good effect.
The data from ref. [10] is a turbine fault diagnosis example.
Efficient discretization algorithm can reduce the size of data and improve the adaptability of data.
Compared with the method in ref. [9], this algorithm discrete continuous data from internal rules of data itself and achieves classification prediction on the data.
Breault: Data mining diabetic databases: are rough sets a useful addition?

Based on it, the damage depth can be calculated with a series of single-sided ultrasonic measured data.
Through comparing the calculating results and measured data in different measured distances, the most likely damaged trend can be determined with least square method.
Find the T which is nearest to the measured data and then determine the m and c.
Fig.3 Flow diagram of numerical analysis program Example analysis In order to prove the rationality of this algorithm, a set of measured data of a concrete frame structure in a chemical factory and measured data from fire resistance lab of Tongji University by single-sided ultrasonic method was used.
Table 1 Comparison of detection data and matlab calculation results single-sided ultrasonic measured data of a chemical factory Calculation result by program single-sided ultrasonic measured data from lab Calculation result by program Measured distance (mm) Propagation time by measure (us) Modify velocity of sound (km/s) Propagation time by calculation (us) Velocity of sound by calculation (km/s) Measured distance (mm) Propagation time by measure (us) Modify velocity of sound (km/s) Propagation time by calculation (us) Velocity of sound by calculation (km/s) 145 74.4 1.949 55.2 2.629 100 81.8 1.223 86.5 1.156 245 90.0 2.722 84.4 2.902 200 143.0 1.399 125.7 1.591 345 118.0 2.924 112.0 3.080 300 164.6 1.823 159.4 1.883 445 135.0 3.296 138.6 3.210 400 192.6 2.077 190.6 2.098 545 159.0 3.428 164.7 3.310 500 212.6 2.352 220.5 2.267 645 183.0 3.525 190.3 3.390 600 252.6 2.375 249.5 2.405 Fig.4 Curves of velocity of sound and measured

In situ real-time data were collected using a Translating Imaging Plate System (TIPS) at the NSLS (USA).
A flat Translating Imaging Plate System (TIPS) (20x40 cm Fuji type IP) [6,7] detector was used for the data collection.
Data reduction and refinements.
Data were refined using the Rietveld method for a better understanding of the structure change with temperature.
It is possible that Na + has somehow frozen out during the quick ramp down for that data collection.

The reduction factor k is about 0.55~0.75 which varies with different tension-compression force ratio.
Table 2 Experiment data of ΔY and FY at the control points NO.
Table 3 Experiment data of ΔX and FX at the control points NO.
The reduction factor k is about 0.55~0.75 which varies with different tension-compression force ratio.

Thermo mechanical modeling of selective inhibition sintered thermoplastic parts Arunkumar P1,a, Balasubramanian E2,b* and Chandrasekhar U3,c 1Junior Research Fellow, *2Associate Professor Center for Autonomous System Research (CASR), Vel Tech University, Chennai 3Director, Engineering Staff College of India, Hyderabad aarunmech198@gmail.com, *besak.bala@gmail.com, crapidchandra@gmail.com Keywords: Selective Inhibition Sintering, Thermo-Structural, Polymers, Finite Element Analysis Abstract: Contemporary product design and development efforts of various engineering organizations have experienced the emergence of Additive Manufacturing (AM) or 3D printing technology as a competent fabrication option for converting digital data into physical parts without using part-specific tools or fixtures.
Introduction Additive Layer Manufacturing (ALM) refers to the process of layer-by-layer fabricating of physical parts directly from three-dimensional CAD data out of various materials that include thermoplastics, metals and wax.
Reduction of thermal stress by about 50% in polyamide parts suggests that these will exhibit much smaller residual stress and hence superior dimensional stability.
Mark, Polymer Data Handbook, 3rd Edition, Oxford University Press, 1999