Search results

Thermal equilibrium deviation based on heat transfer amount at water side: （3） Thermal equilibrium deviation will be calculated during experiment and data acquired are valid if thermal equilibrium variation is within 5%; otherwise data are invalid which requires experiment again.
Data processing for heat transfer experiment Heat transfer coefficient for air-cooled heat exchanger is as below： （4） refers to overall heat transfer coefficient, ; the average value of measured heat transfer amount at both sides is selected to represent heat transfer amount due to the existing deviation in thermal parameter measurement at both sides, which is ,; refers to heat transfer area, ; refers to log mean temperature difference, .
The coefficient, and index m, n in (7) and (9) can be determined after multiple experiment data fitting.
Pressure drop will increase 1.5 times with 0.4mm reduction of air passage spacing in corrugated plate air-cooled heat exchanger Pressure drop of hot water side is shown in Figure 4, whereas resistance expression will be acquired as per experimental data fitting[5]: （11） Hot water flow velocity ranges from 0.09m/s-0.9m/ in correlation expression (11).
Pressure drop will increase 1.5 times with 0.4mm reduction of air passage spacing.

With this analysis tool, real production and process data are compared with results from scientific cryogenic tests and research and examined with regards to their applicability.
In the course of the analysis it is therefore also possible to achieve more transparency by the collection of profound operation data.
Moreover, the transfer of the manufacturing data from the CAM software is currently not completely automated.
A standardized process to extract the data in an automated process with few interfaces has to be developed reducing the risk of transmission errors.
Especially in small and medium sized companies this data is not available in a short period of time.

In order to obtain the aims of weight reduction, efficiency increase and quality assurance of thin-walled parts of aircraft engine, the titanium alloy thin-walled parts are machined by precision machining method in the experiment.
The data of surface roughness is chosen as constraint conditions, and machining parameters will be optimized.
The data of surface roughness is collected under different cutting condition for various combination of cutting speed, feed rate and cutting depth.
The data obtained from the experiment is collected and processed, using Eq. (1) to get the theoretical predictive model.

The work was started with the data collection of production and rejection report of the industry.
Current State Mapping: The production and rejection rate data was collected for three months of a particular component.
From the observed data it was found that, the product consists of around 15% as the total rejection for three months as given in Table 1.
Based on the location analysis the data was collected to know about the number of defects for eight days and given in Table 3.
Balakannan et al., Selection and evaluation of supplier by decision model of hybrid data envelopment analysis, Int.

Curves obtained by mathematical processing of the experimental data are shown in Fig. 4.
The data of Fig. 4 prove significant reduction in the intensity of wear of carbide tool with designed NMCC Ti-TiN-(ZrNbTi)N as compared with the same figure for an uncoated tool or a tool with standard coating TiN.
As it can be seen from the above data, the nature of development of wear centres on rake and flank faces of CI with NMCC Ti-TiN-(ZrNbTi)N is characterized by evenly-balanced pattern, with no visible chips and cenres of brittle fracture.
Following the analysis of the data presented in Fig. 9, it is possible to note that the pattern of change in the content of the main chemical elements (N, O, Zr, Nb, Ti, W) in NMCC Ti-TiN-(ZrNbTi) relates to a significant growth of oxygen content and reduction of nitrogen content in the areas of through cracks in zones 1, 2 and 3 (see Fig. 8).
Following the analysis of the data obtained, it is possible to note the following.

Introduction The use of lightweight materials such as aluminium alloys can lead to a mass reduction of up to 40% in the body structure of a vehicle (which constitutes about a quarter of a car’s total mass) [1].
The direct link between the mass of a vehicle and its energy consumption means that as lightweight alloys are more widely used, improved fuel efficiencies can be achieved; studies have shown that every 10% reduction in mass results in an 8% increase in fuel efficiency [2].
The equations outlined above were calibrated using the results of uniaxial tension tests conducted on the material, and the 21 different material constants determined by fitting to the experimental data using optimization techniques.
Fig. 3 also shows a comparison between the numerical and experimental thickness distribution, with the solid line representing the simulated data and the symbols representing the experimental data.
Lutsey, Review of technical literature and trends related to automobile mass-reduction technology.

Overall Equipment Effectiveness Measure OEE can provide topical information for daily decision making by utilizing largely existing performance data, such as preventive maintenance, material utilization, absenteeism, accidents, labours recovery, conformance to schedule, set-up and changeover data [4].
However, OEE measurement may differ amongst applications and authors as data may not be available or feasible to collect in the form required for each formula [9].
Consequently, manufacturing can be advanced by efficient use of natural resources, consumption of energy and reduction of production rejects and waste so as to prevent harmful and irreversible effects on the environment.
The net effect of reduction in machine downtime, escalation productivity of labours, and minimisation of defects is the ability to achieve higher production levels with the same amount of resources.
Reduction of scrap and rejects directly improve production quality.

(5) Where P is the survival rate, P 'probability of failure, take twice the natural logarithm get: (6) Assume that , ，，then: (7) Failure probability is estimated using the average rank, the probability of failure of the sample points: (8) The fatigue life Np corresponding to the survival rate p can be expressed as: (9) Where: K is the total number of fatigue test data
obtained under a certain stress level, i is the fatigue test data from small to large serial number in ascending order.
In summary, if the regression analyses of experimental data showed that the ln [ln (1 / p)] and lnNp showing good statistical linear relationship (correlation coefficient r higher) on the material fatigue life obeys two-parameter Weibull distribution.
Fatigue life under different survival distribution law can be found: (1) With the reduction of survival, the fatigue life gradually increase , indicating that the fatigue life and survival is inversely related
Mainly engaged in the study of the theory of mountain hazards and disaster reduction technology.

In recent years, river water quality monitoring data indicates that our country’s surface water pollution is still serious.
The specific data is shown in Table 1.
Gradual reduction in contaminants but a great gap still exists for water quality target.
According to the survey data on pollution source of the Weihe River in central Shaanxi, the sewage discharge outlets along main streams of the Weihe River and wastewater quantity discharged directly into the river and its contamination level (counted with COD) in 2009 are shown in Table 3 [4].
Furthermore, the situation of further river flow reduction due to the increase in water consumption hasn’t been taken into account.

This explains why we have the tendency to use new method of classification to classify data.
PCA is one of the fundamental and effective methods in case of dimensional reduction [6].
This kind of transformation method is used to simplify data analysis.
Grgic, Independent comparative study of PCA, ICA, and LDA on the FERET data set.
LEE, Spectral Methods for Dimensionality Reduction, MIT Press, Cambridge, MA, 2005