• Corrosion Science and Technology
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• v.21 no.5
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• pp.360-371
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• 2022

Electropolishing has various parameters because an electrochemical reaction is applied. Accordingly, experiments to determine factors and levels of electropolishing conditions are in progress for various materials. The purpose of this investigation was to optimize conditions for electropolishing using the taguchi method for UNS S31603. Factors such as electrolyte composition ratio, electrolyte temperature, and electropolishing process time were selected. Electropolishing was optimized using analysis of variance (ANOVA), signal-to-noise ratio (the smaller the better characteristics), and surface analysis. Results of ANOVA revealed that only the electrolyte composition ratio among factors was effective for surface roughness. As a result of statistical analysis of the signal-to-noise ratio, the highest signal-to-noise ratio was calculated under electropolishing conditions with sulfuric acid and phosphoric acid ratio of 4:6, an electrolyte temperature of 75 ℃, and electropolishing process time of 7 minutes. In addition, the surface roughness after electropolishing under the above conditions was 0.121 ㎛, which was improved by more than 88% compared to mechanical polishing.

• International Journal of Quality Innovation
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• v.7 no.3
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• pp.107-115
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• 2006

Control charts are important tools of statistical quality control. In 1956, Duncan first proposed the economic design of $\bar{x}-control$ charts to control normal process means and insure that the economic design control chart actually has a lower cost, compared with a Shewhart control chart. An moving average (MA) control chart is more effective than a Shewhart control chart in detecting small process shifts and is considered by some to be simpler to implement than the CUSUM. An economic design of MA control chart has also been proposed in 2005. The weaknesses to only the economic design are poor statistics because it dose not consider type I or type II errors and average time to signal when selecting design parameters for control chart. This paper provides a construction of an economic-statistical model to determine the optimal parameters of an MA control chart to improve economic design. A numerical example is employed to demonstrate the model's working and its sensitivity analysis is also provided.

• Proceedings of the Korean Society for Quality Management Conference
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• 2007.04a
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• pp.302-309
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• 2007

To conduct statistical process control needs the assumption that the process data are independent. However, most of chemical processes, like a semi-conduct processes do not satisfy the assumption because of autocorrelation. It causes abnormal out of control signal in the process control and misleading process capability. In this study, we introduce that Shore's method to solve the problem and to find the optimal subgroup size to estimate variance for AR(l) model. Especially, we focus on finding an actual subgroup size for small samples using simulation. It may be very useful for statistical process control to analyze process capability and to make a Shewhart chart properly.

• Journal of the Optical Society of Korea
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• v.12 no.3
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• pp.170-173
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• 2008

In this study, we simulated a statistical model of FCS(fluorescence correlation spectroscopy) based on a Poisson process to understand and explain observations of the experiment performed on molecules of ultra-low concentration by the home-built laser-scanning confocal microscope. The statistical model confirmed that the relative mean square amplitude of fluctuations is shown to be inversely proportional to the average number of molecules, even in the ultra-low concentration, if some conditions are satisfied. Signal-to-noise ratio and the variability of dwelling time under the confocal volume were found to be effective conditions for the experiment.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.6
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• pp.824-831
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• 2014

In this study, structural variations and overlay errors caused by multiple patterning lithography techniques to print narrow parallel metal interconnects are investigated. Resistance and capacitance parasitic of the six lines of parallel interconnects printed by double patterning lithography (DPL) and triple patterning lithography (TPL) are extracted from a field solver. Wide parameter variations both in DPL and TPL processes are analyzed to determine the impact on signal propagation. Simulations of 10% parameter variations in metal lines show delay variations up to 20% and 30% in DPL and TPL, respectively. Monte Carlo statistical analysis shows that the TPL process results in 21% larger standard variation in delay than the DPL process. Crosstalk simulations are conducted to analyze the dependency on the conditions of the neighboring wires. As expected, opposite signal transitions in the neighboring wires significantly degrade the speed of signal propagation, and the impact becomes larger in the C-worst metals patterned by the TPL process compared to those patterned by the DPL process. As a result, both DPL and TPL result in large variations in parasitic and delay. Therefore, an accurate understanding of variations in the interconnect parameters by multiple patterning lithography and adding proper margins in the circuit designs is necessary.

• Journal of the Korean Statistical Society
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• v.27 no.2
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• pp.165-187
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• 1998

Shin and Sarkar (1993, 1994) studied the problem of testing for a unit root in an AR(p) signal observed with MA(q) noise when the MA parameters are known. In this paper we consider the case when the MA parameters are unknown and to be estimated. Test statistics are defined using unit root parameter estimates based on three different estimation methods of Hannan and Rissanen (1982), Kohn (1979) and Shin and Sarkar (1995). An AR(p) process contaminated by MA(q) noise is a .estricted ARMA model, for which Shin and Sarkar (1995) derived an easy-to-compute Newton- Raphson estimator The two-stage estimation p.ocedu.e of Hannan and Rissanen (1982) is used to compute initial parameter estimates in implementing the iterative estimation methods of both Shin and Sarkar (1995) and Kohn (1979). In a simulation study we compare the relative performance of these unit root tests with respect to both size and power for p=q=1.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.247-264
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• 2008

This paper considers the integrated process control procedure for detecting special causes in an ARIMA(0,1,1) process that is being adjusted automatically after each observation using a minimum mean squared error adjustment policy. It is assumed that a special cause can change the process mean and the process variance. We derive expressions for the process deviation from target for a variety of different process parameter changes, and introduce a control chart, based on the generalized likelihood ratio, for detecting special causes. We also propose the integrated process control scheme bases on the future loss. The future loss denotes the cost that will be incurred in a process remaining interval from a true out-of-control signal.

• Journal of Biomedical Engineering Research
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• v.6 no.1
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• pp.1-4
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• 1985

Background EEG signals can be represented as the sum of a conventional AR process and an innovation process. It is know that conventional estimation techniques, such as least square estimates (LSE) or Gauasian maximum likelihood estimates (MLE-G) are optimal when the innovation process satisfies the Gaussian or presumed distribution. When the data are contaminated by outliers, however, these assumptions are not met and the power spectrum estimated by conventional estimation techniques may be fatally biased. EEG signal may be affected by artifacts, which are outliers in the statistical term. So the robust filtering estimation technique is used against those artifacts and it performs well for the contaminated EEG signal.

• Journal of the Korean Statistical Society
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• v.23 no.2
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• pp.403-420
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• 1994

An economic design of the $\bar{X}-R$ chart using variable sample size (VSS) scheme is proposed in this paper. In this design the sample size at each sampling time changes according to the values of the previous two sample statistics, sample mean and range. The VSS scheme uses large sample if the sample statistics appear near inside the control limits and smaller sample otherwise. The set of process parameters, such as the sampling interval, control limits and the sample sizes, are chosen to minimize the expected cost per hour. The efficiency of the VSS scheme is compared to the fixed sample size one for cases where there is multiple of assignable causes. Percent reductions of the expected cost in the VSS design are calculated for some given sets of cost parameters. It is shown that the VSS scheme improves the confidence of the procedure and performs statistically better in terms of the number of false alarms and the average time to signal, respectively.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.1
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• pp.70-78
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• 2004

This paper presents study of efficiency of wavelet transformation for on-line chatter detection during hard fuming process. From comparison with other time series and statistical methods such as fast fourier transformation (FFT), Kurtosis and standard deviation (STD), wavelet transform is better than others in on-line chatter detection. With using wavelet function with pseudo frequency corresponding to chatter frequency, chatter could be detected more sensitively. And for both force signal from dynamometer and displacement signal from capacitance type cylindrical sensor (CCS), wavelet transform with DB2 function on level 4 could be well used for chatter detection in hard turning process.