• 제목/요약/키워드: c control chart

검색결과 53건 처리시간 0.025초

카즈분포족에 대한 누적합 관리도 (CUSUM control chart for Katz family of distributions)

  • 조교영
    • Journal of the Korean Data and Information Science Society
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    • 제22권1호
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    • pp.29-35
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    • 2011
  • 결점수를 모니터링하기 위한 통계적 공정관리는 생산공정에 널리 사용된다. 결점 수를 모니터링 하는데는 c-관리도가 사용된다. 전통적인 c-관리도는 표본에서 결점의 발생은 포아송분포를 따른다는 가정 하에서 만들어진다. 포아송분포에 대한 가정이 맞지 않을 때에는 X-관리도가 사용될 수 있다. 누적합 관리도는 공정의 작은 변화를 찾는데 유용한 것으로 알려져 있다. 본 논문에서는 다양한 Katz 분포족으로부터 생성된 계수자료에 대하여 3시그마 X-관리도와 누적합 관리도의 효율을 평균런의길이에 근거하여 비교 한다. 즉, 자료가 어떤 분포로부터 생성되었는지 알 수 없을 때, X-관리도와 누적합 관리도를 비교하는 것이다.

붓스트랩 $C_{pp}$ 다공정 수행분석차트 (Bootstrap $C_{pp}$ Multiple Process Performance Analysis Chart)

  • 장대흥
    • 품질경영학회지
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    • 제38권2호
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    • pp.171-179
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    • 2010
  • Pearn et al.(2002) supposed the $C_{pp}$ multiple process performance analysis chart. This chart displays multiple processes with the process variation and process departure on one single chart. But, this chart can not display the distribution of the process variation and process departure and is inappropriate for processes with non-normal distributions. With bootstrapping method, we can display the distribution of the process variation and process departure on the $C_{pp}$ multiple process performance analysis chart.

카즈분포족에 대한 지수가중이동평균관리도 (EWMA control chart for Katz family of distributions)

  • 조교영
    • Journal of the Korean Data and Information Science Society
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    • 제21권4호
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    • pp.681-688
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    • 2010
  • 통계적 공정관리에서 결점수를 모니터링 하는데는 c-관리도가 사용된다. 전통적인 c-관리도는 표본에서 결점의 발생은 포아송분포를 따른다는 가정 하에서 만들어진다. 포아송 분포에 대한 가정이 맞지 않을 때에는 X-관리도가 사용될 수 있다. 지수가중이동평균관리도는 공정의 작은 변화를 찾는 데 유용한 것으로 알려져 있다. 본 논문에서는 다양한 카즈분포족으로부터 생성된 계수자료에 대하여 3시그마 X-관리도와 지수가중이동평균관리도의 효율을 평균 런의 길이에 근거하여 비교한다. 즉, 자료가 어떤 분포로부터 생성되었는지 알 수 없을 때, X-관리도와 지수가중이동평균관리도를 비교하는 것이다.

가변추출간격을 이용한 c 관리도의 최적설계 (Optimal Design of c Control Chart using Variable Sampling Interval)

  • 박주영
    • 대한안전경영과학회지
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    • 제9권2호
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    • pp.215-233
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    • 2007
  • Even though the ad hoc Shewhart methods remain controversial due to various mathematical flaws, there is little disagreement among researchers and practitioners when a set of process data has a skewness distribution. In the context and language of process control, the error related to the process data shows that time to signal increases when a control parameter shifts to a skewness direction. In real-world industrial settings, however, quality practitioners often need to consider a skewness distribution. To address this situation, we developed an enhanced design method to utilize advantages of the traditional attribute control chart and to overcome its associated shortcomings. The proposed design method minimizes bias, i.e., an average time to signal for the shift of process from the target value (ATS) curve, as well as it applies a variable sampling interval (VSI) method to an attribute control chart for detecting a process shift efficiently. The results of the factorial experiment obtained by various parameter circumstances show that the VSI c control chart using nearly unbiased ATS design provides the smallest decreasing rate in ATS among other charts for all experimental cases.

관리도 선정 및 해석을 위한 전문가시스템 개발 (An Expert System Development for Control Chart Selection and Interpretation)

  • 유춘번;이태규
    • 산업경영시스템학회지
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    • 제21권45호
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    • pp.265-277
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    • 1998
  • The control chart has been used widely and importantly as a tool for statistical process control(SPC). Most companies are concerned with improving the quality and the productivity as well as reducing the cost, especially in today's highly competitive environment. Though SPC is known as a technique for consistent quality, it is not used properly due to lack of knowledge about it. It is required to develop a support system for control chart selection and interpretation that can be utilized by non-specialist without hard training or experiences. The support system was developed by applying the expert system tool to popular control charts. Though some researches on this area has been performed, the implemented results expose many problems in field applications due to the unsatisfactory explanation of the selected control chart and limited knowledge base for resolving the problems. This thesis presented an expert system for control chart as solution for these problems. The expert system for the control chart selection and interpretation is developed by using Turbo C and EXSYS which is an expert system development tool.

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붓스트랩 $C_{pp}$ 다공정 수행분석차트 (Bootstrap $C_{pp}$ Multiple Process Performance Analysis Chart)

  • 장대흥;김대학
    • 한국품질경영학회:학술대회논문집
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    • 한국품질경영학회 2007년도 춘계학술대회
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    • pp.287-296
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    • 2007
  • Pearn et al.(2002) supposed the $C_{pp}$ multiple process performance analysis chart. This chart display multiple processes with the process variation and process departure on one single chart. But, this chart can not display the distribution of the process variation and process departure. With bootstrapping method, we can display the distribution of the process variation and process departure on the $C_{pp}$ multiple process performance analysis chart.

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PL시대에 있어서 품질보증을 위한 공정관리기법 (Process Control Techniques for Quality Assurance in the Product Liability Age)

  • 정영배;김연수
    • 산업경영시스템학회지
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    • 제20권42호
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    • pp.73-85
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    • 1997
  • In the product liability age the demand on quality is extremely high and inspection and test are automated. The process capability indices $C_p, {\;}C_{pk}$ and p control chart widely used to provide unitless measure of process performance and process control. Traditional process capability indices $C_p, {\;}C_{pk}$ do not represent the process variation from target value. The convention p chart for control of fraction nonconforming becomes inadequate when the fraction nonconforming becomes very small such as PPM level production system. This paper proposes process performance measure considering quadratic loss function and cumulative counts control chart for control of PPM level production system.

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공정비능력지수를 이용한 통계적 공정관리와 조정 (Statistical Process Control and Adjustment using Process Incapability Index)

  • 구본철
    • 산업경영시스템학회지
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    • 제24권63호
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    • pp.45-54
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    • 2001
  • The process capability indices have been widely used in manufacturing industries to provide numerical measures of process potential and performance. This study is concerned with process controls and adjustments by incapability index $C_{pp}$ and its sub-indices. A monitoring for $\^{C}_{pp}$ would provide a convenient way to monitor changes on process capability after statistical control is established, since $C_{pp}$ simultaneously measures process variability and centering. Further, we can separate charting of process location and variability by sub-indices of $C_{pp}$, ($C_{ia}$, $C_{ip}$), without returning to $\={x}$-R chart, even though an out-of-control signals on $\^{C}_{pp}$ control chart is found.

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Pre-Control의 수행도에 관한 소고 (A Note on the Performance of Pre-Control)

  • 서순근
    • 품질경영학회지
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    • 제44권3호
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    • pp.587-600
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    • 2016
  • Purpose: This paper evaluates the performance of the pre-control(PC), an alternative to statistical process control techniques and compares with a control chart considering the tolerance of process. Methods: The previous studies for PC have drawbacks that PC with two linked stages, qualification and running, are discussed separately and independently. Hence this paper analyzes the performance of PC by integrating two stages. Results: Average outgoing quality limits to grasp the outcome of PC are provided by computational results for two process capability indexes, $C_p$ and $C_{pk}$ and the usefulness of PC from comparative experiments with modified control charts is commented. Conclusion: Helpful guidelines for quality managers to apply PC in practice and areas of process for PC to be more benefit are presented.

관리도의 민감도와 공정능력 분석 (The Sensitivity of ${\bar{X}}$ Control Chart and Process Capability Analysis)

  • 이종성
    • 산업기술연구
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    • 제28권A호
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    • pp.149-153
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    • 2008
  • $C_p$ and other process capability indices are used extensively in industry, However, They are inadequate and widely misused. In a practical application, process average ${\mu}$ is almost always drifted by various assignable causes in process. And control charts will not detect these shifts in process average. In this study, incorporating these undetected shifts, a new capability analysis method is introduced.

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