• 한국의학물리학회지:의학물리
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• 제25권3호
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• pp.185-192
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• 2014

이 연구의 목적은 본원이 보유하고 있는 선형가속기들의 출력 특성을 Shewhart-type Chart, EWMA Chart, 공정능력지수 $C_p$와 $C_{pk}$을 이용한 통계적 분석으로 품질보증에 대한 결과를 평가하고자 한다. 측정값은 의학물리사에 의하여 2012년 9월부터 2014년 4월까지 매월 측정된 각각 치료기들(21EX, 21EX-S, Novalis Tx)의 출력측정값을 사용하였다. 치료기들의 출력 특성은 IAEA TRS-398의 가이드라인을 따랐으며, 측정 에너지는 광자선 6 MV, 10 MV, 15 MV와 전자선 4 MeV, 6 MeV, 9 MeV, 12 MeV, 16 MeV, 20 MeV였다. 매월 측정하여 교정한 출력특성에 대한 통계학적 분석이며, 가중인자와 측정값의 관리한계의 폭은 ${\lambda}=0.10$, L=2.703로 계산되었으며, 공정능력 $C_p$와 $C_{pk}$는 모든 선형가속기(21EX, 21EX-S, Novalis Tx)의 모든 에너지에서 1이상이었다. Shewhart-type Chart를 통하여 출력선량의 측정값의 큰 변화점을 찾을 수 있었고, EWMA Chart를 통하여 출력선량의 측정값의 미세한 변화점을 알아 볼 수 있었다. 본원의 치료기의 공정능력지수 $C_p$와 $C_{pk}$를 통하여 21EX가 2.384와 2.136, 21EX-S가 1.917과 1.682, Novalis Tx가 2.895와 2.473으로 Novalis Tx가 가장 안정적이고 정확한 출력특성을 나타내고 있었다.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.289-301
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• 2003

In this paper, we propose Shewhart control chart and EWMA control chart using the autocorrelated data which are common in chemical and process industries and lead to increase the number of false alarms when conventional control charts are applied. The effect of autocorrelated data is modeled as a autoregressive process, and canonical analysis is used to reduce the dimensionality of the data set and find the canonical variables that explain as much of the data variation as possible. Charting statistics are constructed based on the residual vectors from the canonical variables which are uncorrelated over time, and the control charts for these statistics can attenuate the autocorrelation in the process data. The charting procedures are illustrated with a numerical example and simulation is conducted to investigate the performances of the proposed control charts.

• 품질경영학회지
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• 제12권2호
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• pp.15-24
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• 1984

Since W.A. Shewhart (1931) developed the quality control method using the control chart, many theoretical and empirical works about such an analytical method have been done. However there are two major methods relating to the control chart analysis; the conventional 3 sigma control method and the warning limit method which has been suggested as a modification of the former. The conventional 3 sigma method requires to take a remedial action only when a quality characteristic is beyond the control limit (3 sigma). However, once a quality characteristic is over the control limit, searching and repairing an assignable cause requires time consuming job and high costs. Therefore if we set the warning limit between the central line and the control limit, we will be able to take remedial measures before too late. In spite of its advantage, much attention has not been paid to use the control chart with warning limit in Korean industries. The main object of this study is to examine improvement of quality and productivity when the control chart with warning limit is used.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2007년도 춘계학술대회
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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.

• 대한안전경영과학회지
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• 제8권6호
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• pp.119-137
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• 2006

This paper seeks to Combination for Efficient Application of SPC/EPC minimize variability by transferring the output variable to a related process controllable variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. In the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with autoregressed disturbance. We compare three control systems; EPC, Cp, SPC combined system with EWMA, CUSUM and Shewhart. This paper shows through simulation that the performance of the integrated model of EPC and SPC, Cp is more preferable than that of EPC.

• 품질경영학회지
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• 제32권4호
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• pp.156-168
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• 2004

This research investigates the statistical efficiency of variable sampling size & sampling interval(VSSI) $\bar{X}$ charts under two assignable causes. Algorithms for calculating the average run length(ARL) and average time to signal(ATS) of the VSSI $\bar{X}$ chart are proposed by employing Markov chain method. States of the process are defined according to the process characteristics after the occurrence of an assignable cause. Transition probabilities are carefully derived from the state definition. Statistical properties of the proposed chart are also investigated. A simple procedure for designing the proposed chart is presented based on the properties. Extensive sensitivity analyses show that the VSSI $\bar{X}$ chart is superior to the VSS or VSI $\bar{X}$ chart as well as to the Shewhart $\bar{X}$ chart in statistical sense, even tinder two assignable causes.

• 산업공학
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• 제16권3호
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• pp.344-351
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• 2003

Critical dimension is one of the most important characteristics of up-to-date integrated circuit devices. Hence, critical dimension control in a semiconductor wafer fabrication process is inevitable in order to achieve optimum device yield as well as electrically specified functions. Currently, in complex semiconductor wafer fabrication processes, statistical methodologies such as Shewhart-type control charts become crucial tools for practitioners. Meanwhile, given a critical dimension sampling plan, the analysis of variance technique can be more effective to investigating critical dimension variation, especially for on-chip and on-wafer variation. In this paper, relating to a typical sampling plan, linear statistical models are presented for the analysis of critical dimension variation. A case study is illustrated regarding a semiconductor wafer fabrication process.

• Industrial Engineering and Management Systems
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• 제16권1호
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• pp.109-117
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• 2017

In this article, a control chart based on multiple dependent (or deferred) state sampling for the gamma distributed quality characteristic is proposed using the gamma to normal transformation. The proposed control chart has two pairs of control limits, which can be determined by considering the in-control average run length (ARL). The shift in the scale parameter of a gamma distribution is considered and the out-of-control ARL is evaluated. The performance of the proposed chart has been shown for different levels of the parameters of the proposed control chart. It is also shown that the proposed chart is better than the Shewhart chart in terms of ARLs. A case study with a real data has been included for the practical usage of the proposed scheme.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
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• pp.15-18
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• 1996

Shewhart control chart is a basic technique to monitor the state of a process. We observe observations of a group of size four or five in a rational way and plot some statistics (e.g., means and ranges) on the chart. When setting up the control chart, the control limits are calculated based on preliminary 20-40 samples, which were supposedly obtained from stable operating conditions. But it may be hard to believe, especially at the beginning of constructing the chart for the first time, whether the process is stable and hence all samples were generated under the homogeneous operating conditions. In this report we suggest a mechanism to obtain robust control limits under self-criticism. When outliers are present in the sample, we obtain tighter control limits and hence increase the sensitivity of the chart. Examples will be given via simulation study.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.825-833
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• 2003

Knowing the time of the process change could lead to quicker identification of the responsible special cause and less process down time, and it could help to reduce the probability of incorrectly identifying the special cause. In this paper, we propose a maximum likelihood estimator of the process change point when a Shewhart $\bar{X}$ chart with variable sample size (VSS) scheme signals a change in the process mean. Also we build a confidence interval for the process change point by using the likelihood function.