• Title, Summary, Keyword: Statistical Process Control

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A Hybrid Approach to Statistical Process Control

  • Giorgio, Massimiliano;Staiano, Michele
    • The Asian Journal on Quality
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    • v.5 no.1
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    • pp.52-67
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    • 2004
  • Successful implementation of statistical process control techniques requires for operational definitions and precise measurements. Nevertheless, very often analysts can dispose of process data available only by linguistic terms, that would be a waste to neglect just because of their intrinsic vagueness. Thus a hybrid approach, which integrates fuzzy set theory and common statistical tools, sounds useful in order to improve effectiveness of statistical process control in such a case. In this work, a fuzzy approach is adopted to manage linguistic information, and the use of a Chi-squared control chart is proposed to monitor process performance.

Statistical Process Control Procedure for Integral-Controlled Processes

  • Lee, Jaeheon;Park, Cangsoon
    • Communications for Statistical Applications and Methods
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    • v.7 no.2
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    • pp.435-446
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    • 2000
  • Statistical process control(SPC) and engineering process control(EPC) are two strategies for quality improvement that have been developed independently. EPC seeks to minimize variability by adjusting compensatory variables in order to make the process level close to the target, while SPC seeks to reduce variability by monitoring and eliminating causes of variation. One purpose of this paper is to propose the IMA(0,1,1) model as the in-control process model. For the out-of-control process model we consider two cases; one is the case with a step shift in the level, and the other is the case with a change in the nonstationarity. Another purpose is to suggest the use of an integrated process control procedure with adjustment and monitoring, which can consider the proposed process model effectively. An integrated control procedure will improve the process control activity significantly for cases of the proposed model, when compared to the procedure of using either EPC or SPC, since EPC will keep the process close to the target and SPC will eliminate special causes.

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A study on the theory for Integrating of Statistical Process Control and Process Adjustmen (통계적 공정관리와 공정조절의 통합을 위한 이론에 대한연구)

  • Jung, Hae-Woon
    • Proceedings of the Safety Management and Science Conference
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    • pp.493-504
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    • 2005
  • Statistical Process Control and Process Adjustment theory is gaining recognition in the process industries where the process frequently experiences a shift mean. This paper aims to study, the theory difference between Statistical Process Control and Process Adjustment in simple terms and presents a case study that demonstrates successful integration of Statistical Process Control and Process Adjustment theory for a product in drifting industry.

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Development of Statistical Process Control System for Tobacco Manufacturing Process (담배 제조 공정의 통계적 관리시스템 개발)

  • 김영호;송정호
    • Journal of the Korean Society of Tobacco Science
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    • v.23 no.1
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    • pp.53-59
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    • 2001
  • To decrease of deviations from target specifications and excessive variability around targe, we exclusively designed statistical process control system involving general manager and expert tool for cigarette manufacturing process. This system is a unique programming environment for the development of total process control software including various control charts according to data type and process capability analysis. Also this system includes the statistical analysis module to analyze defective causes immediately when inferior products are made and the module to offer regular reports. This system is customized considering the manufacture environment based on the opinions of workers.

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A Combined Process Control Procedure by Monitoring and Repeated Adjustment

  • Park, Changsoon
    • Communications for Statistical Applications and Methods
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    • v.7 no.3
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    • pp.773-788
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    • 2000
  • Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for processes quality improvement. SPC reduces process variability by detecting and eliminating special causes of process variation. while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been needs for a process control proceduce which combines the tow strategies. This paper considers a combined scheme which simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an integrated moving average(IMA) process with a step shift. The EPC part of the scheme adjusts the process back to target at every fixed monitoring intervals, which is referred to a repeated adjustment scheme. The SPC part of the scheme uses an exponentially weighted moving average(EWMA) of observed deviation from target to detect special causes. A Markov chain model is developed to relate the scheme's expected cost per unit time to the design parameters of he combined control scheme. The expected cost per unit time is composed of off-target cost, adjustment cost, monitoring cost, and false alarm cost.

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Development of Fuzzy-Statistical Control Chart for Processing Uncertain Process Information (불명확한 공정정보 처리를 위한 퍼지-통계적 관리도의 개발)

  • 김경환;하성도
    • Journal of the Korean Society for Precision Engineering
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    • v.15 no.2
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    • pp.75-80
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    • 1998
  • Process information is known to have the continuous distribution in many manufacturing processes. Generalized p-chart has been developed for controlling processes by classifying the information characteristics into several groups. But it is improper to describe continuous processes with the classified process informal ion, which is based on the classical set concept. Fuzzy control chart, has been developed for the control of linguistic data, but it is also based on the dichotomous notion of classical set theory. In this paper, fuzzy sampling method is studied in order to process the uncertain data properly. The method is incorporated with the fuzzy control chart. Statistical characteristics of the fuzzy representative value are utilized to device the fuzzy-statistical control chart. The fuzzy-statistical control chart is compared with the generalized p-chart and both the sensitivity to the process information distribution change pared robustiness against the noise on the process information of the fuzzy-statistical control chart are shown to be superior.

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Copula modelling for multivariate statistical process control: a review

  • Busababodhin, Piyapatr;Amphanthong, Pimpan
    • Communications for Statistical Applications and Methods
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    • v.23 no.6
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    • pp.497-515
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    • 2016
  • Modern processes often monitor more than one quality characteristic that are referred to as multivariate statistical process control (MSPC) procedures. The MSPC is the most rapidly developing sector of statistical process control and increases interest in the simultaneous inspection of several related quality characteristics. Most multivariate detection procedures based on a multi-normality assumptions are independent, but there are many processes that assume non-normality and correlation. Many multivariate control charts have a lack of related joint distribution. Copulas are tool to construct multivariate modelling and formalizing the dependence structure between random variables and applied in several fields. From copula literature review, there are a few copula to apply in MSPC that have multivariate control charts, and represent a successful tool to identify an out-of-control process. This paper presents various types of copulas modelling for the multivariate control chart. The performance measures of the control chart are the average run length (ARL) and the average number of observations to signal (ANOS). Furthermore, a Monte Carlo simulation is shown when the observations were from an exponential distribution.

Statistical Process Control Software developed by MS-EXCEL and Visual Basic (MS-EXCEL과 Visual Basic으로 개발한 통계적 공정관리 소프트웨어)

  • Han, Kyung-Soo;Ahn, Jeong-Yong
    • Journal of the Korean Society for Quality Management
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    • v.24 no.2
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    • pp.172-178
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    • 1996
  • In this study, we developed a software for statistical process control. This software presents $\bar{x}$, R, CUSUM, EWMA control chart and process capability index. In this system, statistical process control methods are integrated into the automated method on a real time base. It is available in process control of specified type and can be performed on personal computer with network system.

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Real Time Process Control System under 100 PPM Management System (100 PPM 관리체제하의 실시간 공정관리 방안)

  • 조남호;신숙현
    • Journal of the Korean Society for Quality Management
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    • v.25 no.1
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    • pp.116-134
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    • 1997
  • The present automated manufacturing environment is very different when the classical statistical process control method based on batch processing were used. Therefore these must be replaced by automated statistical process control method. In this point of view, this paper intends to develop the automated statistical process control method which can be implemented in the present automated manufacturing environment. Specially this study developed the rules to identify the special causes of the manufacturing process in the aspect of the 100 PPM management, and a numerical example is demonstrated to verify the usefulness of these rules.

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Applying an Expert System to Statistical Process Control (통계적 공정 제어에 전문가 시스템의 적용에 관한 연구)

  • 윤건상;김훈모;최문규
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.411-414
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    • 1995
  • Statistical Process Control (SPC) is a set of methodologies for signaling the presence of undesired sources of variation in manufacturing processes. Expert System in SPC can serve as a valuable tool to automate the analysis and interpretation of control charts. In this paper we put forward a method of successful application of Expert System to SPC in manufacturing process.

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