• Title, Summary, Keyword: statistical process control

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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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A Study on Process Control Modeling for Precision Guided Munitions Quality Control (정밀유도무기 품질관리를 위한 공정관리 수행모델에 관한 연구)

  • Kim, Si-Ok;Lee, Chang-Woo;Cha, Sung-Hee
    • Journal of the Korean Society for Quality Management
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    • v.41 no.3
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    • pp.487-494
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    • 2013
  • Purpose: In this study, we propose the precision guided munitions verification methodology using the statistical analysis method has been proposed. and it can be applied to the precision guided munitions quality assurance work. Methods: This modeling is based on Failure Mode and Effects Analysis, Statistical Process Control, Defense Quality Managerment System, Production Readiness Review, Manufacturing Readiness Assesment and so on. Results: The Process Control Modeling that has the following procedures ; searching the critical to quality, statistical analysis by process, verify process. Moreover, the effectiveness of the methodology is verified by applying to the precision guided munitions. Conclusion: To achieve a analysis methods of statistical process control and verify process for precision guided munitions.

The Use of Local Outlier Factor(LOF) for Improving Performance of Independent Component Analysis(ICA) based Statistical Process Control(SPC) (LOF를 이용한 ICA 기반 통계적 공정관리의 성능 개선 방법론)

  • Lee, Jae-Shin;Kang, Bok-Young;Kang, Suk-Ho
    • Journal of the Korean Operations Research and Management Science Society
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    • v.36 no.1
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    • pp.39-55
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    • 2011
  • Process monitoring has been emphasized for the monitoring of complex system such as chemical processing industries to achieve the efficiency enhancement, quality management, safety improvement. Recently, ICA (Independent Component Analysis) based MSPC (Multivariate Statistical Process Control) was widely used in process monitoring approaches. Moreover, DICA (Dynamic ICA) has been introduced to consider the system dynamics. However, the existing approaches show the limitation that their performances are strongly dependent on the statistical distributions of control variables. To improve the limitation, we propose a novel approach for process monitoring by integrating DICA and LOF (Local Outlier Factor). In this paper, we aim to improve the fault detection rate with the proposed method. LOF detects local outliers by using density of surrounding space so that its performance is regardless of data distribution. Therefore, the proposed method not only can consider the system dynamics but can also assure robust performance regardless of the statistical distributions of control variables. Comparison experiments were conducted on the widely used benchmark dataset, Tennessee Eastman process (TE process), and showed the improved performance than existing approaches.

AN INVESTIGATIVE STUDY ON THE COMBINING SPC AND EPC (SPC와 EPC 통합에 관한 조사연구)

  • 김종걸;정해운
    • Proceedings of the Safety Management and Science Conference
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    • pp.217-236
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    • 1999
  • Engineering process control (EPC) is one of the techniques very widely used in process. EPC is based on control theory which aims at keeping the process on target. Statistical process control (SPC), also known as statistical process monitoring. The main purpose of SPC is to look for assignable causes (variability) in the process data. The combined SPC/EPC scheme is gaining recognition in the process industries where the process frequently experiences a drifting mean. This paper aims to study the difference between SPC and EPC in simple terms and presents a case study that demonstrates successful integration of SPC and EPC for a product in drifting industry. Statistical process control (SPC) monitoring of the special causes of a process, along with engineering feedback control such as proportional-integral-derivative (PID) control, is a major tool for on-line quality improvement.

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A Technique and software of analysis and control for measurement process

  • Zhao, Fengyu;Xu, Jichao;Bergman, Bo
    • International Journal of Quality Innovation
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    • v.1 no.1
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    • pp.97-105
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    • 2000
  • In this paper, a two-section method for measuring is introduced and the variation sources of measurement process are analysed. Measuring is a special process in general process. Various variation source must be firstly decomposed so that the statistical distribution law of measuring process can be established, and then implement monitoring control of the measuring process. A special method to obtain the measuring variation is discussed, and a monitoring control technique for measuring process is studied based statistical distribution. Towards the end, we briefly introduce software design for the analysis and control of a measurement process.

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A Statistical Control Chart for Process with Correlated Subgroups

  • Lee, Kwang-Ho
    • Communications for Statistical Applications and Methods
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    • v.5 no.2
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    • pp.373-381
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    • 1998
  • In this paper a new control chart which accounts for correlation between process subgroups will be proposed. We consider the case where the process fluctuations are autocorrelated by a stationary AR(1) time series and where n($\geq1$) items are sampled from the process at each sampling time. A simulation study is presented and shows that for correlated subgroups, the proposed control chart makes a significant improvement over the traditionally employed X-bar chart which ignores subgroup correlations. Finally, we illustrate the proposed chart by comparing the standardized residuals and X-bar chart on a data set of motor shaft diameters.

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Statistical process control of dye solution stream using spectrophotometer

  • Lee, Won-Jae;Cho, Gyo-Young
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.6
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    • pp.1289-1303
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    • 2010
  • The need for statistical process control to check the performance of a process is becoming more important in chemical and pharmaceutical industries. This study illustrates the method to determine whether a process is in control and how to produce and interpret control charts. In the experiment, a stream of green dyed water and a stream of pure water were continuously mixed in the process. The concentration of the dye solution was measured before and after the mixer via a spectrophotometer. The in-line mixer provided benefits to the dye and water mixture but not for the stock dye solution. The control charts were analyzed, and the pre-mixer process was in control for both the stock and mixed solutions. The R and X-bar charts showed virtually all of the points within control limits, and there were no patterns in the X-bar charts to suggest nonrandom data. However, the post-mixer process was shown to be out of control. While the R charts showed variability within the control limits, the X-bar charts were out of control and showed a steady increase in values, suggesting that the data was nonrandom. This steady increase in dye concentration was due to discontinuous, non-steady state flow. To improve the experiment in the future, a mixer could be inserted into the stock dye tank. The mixer would ensure that the dye concentration of the stock solution is more uniform prior to entering the pre-mixer ow cell. Overall, this would create a better standard to judge the water and dye mixture data against as well.

Generalized Q Control Charts for Short Run Processes in the Presence of Lot to Lot Variability (Lot간 변동이 존재하는 Short Run 공정 적용을 위한 일반화된 Q 관리도)

  • Lee, Hyun Cheol
    • Korean Management Science Review
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    • v.31 no.3
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    • pp.27-39
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    • 2014
  • We derive a generalized statistic form of Q control chart, which is especially suitable for short run productions and start-up processes, for the detection of process mean shifts. The generalization means that the derived control chart statistic concurrently uses within lot variability and between lot variability to explain the process variability. The latter variability source is noticeably prevalent in lot type production processes including semiconductor wafer fabrications. We first obtain the generalized Q control chart statistic when both the process mean and process variance are unknown, which represents the case of implementing statistical process control charting for short run productions and start-up processes. Also, we provide the corresponding generalized Q control chart statistics for the rest of three cases of previous Q control chart statistics : (1) both the process mean and process variance are known (2) only the process mean is unknown and (3) only the process variance is unknown.

Economic Design of VSI $\bar X$ Control Chart for Decision to Improve Process (공정개선 의사결정을 위한 VSI $\bar X$ 관리도의 경제적 설계)

  • Song, Suh-Ill;Kim, Jae-Ho;Jung, Hey-Jin
    • Journal of the Korean Society for Quality Management
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    • v.35 no.2
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    • pp.37-44
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    • 2007
  • Today, the statistical process control (SPC) in manufacture environment is an important role at the process by the productivity improvement of the manufacturing systems. The control chart in this statistical method is widely used as an important statistical tool to find the assignable cause that provoke the change of the process parameters such as the mean of interest or standard deviation. But the traditional SPC don't grasp the change of process according to the points fallen the near control limits because of monitoring the variance of process such as the fixed sampling interval and the sample size and handle the cost of the aspect of these sample point. The control chart can be divided into the statistical and economic design. Generally, the economic design considers the cost that maintains the quality level of process. But it is necessary to consider the cost of the process improvement by the learning effects. This study does the economic design in the VSI $\bar X$ control chart and added the concept of loss function of Taguchi in the cost model. Also, we preyed that the VSI $\bar X$ control chart is better than the FSI $\bar X$ in terms of the economic aspects and proposed the standard of the process improvement using the VSI $\bar X$ control chart.

AN INTEGRATED PROCESS CONTROL PROCEDURE WITH REPEATED ADJUSTMENTS AND EWMA MONITORING UNDER AN IMA(1,1) DISTURBANCE WITH A STEP SHIFT

  • Park, Chang-Soon
    • Journal of the Korean Statistical Society
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    • v.33 no.4
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    • pp.381-399
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    • 2004
  • Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for process quality improvement. SPC re-duces 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 need for an integrated process control (IPC) procedure which combines the two strategies. This paper considers a scheme that simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an IMA(1,1) model with a step shift. The EPC part of the scheme adjusts the process, while the SPC part of the scheme detects the occurrence of a special cause. For adjusting the process repeated adjustment is applied according to the predicted deviation from target. For detecting special causes the exponentially weighted moving average control chart is applied to the observed deviations. It was assumed that the adjustment under the presence of a special cause may increase the process variability or change the system gain. Reasonable choices of parameters for the IPC procedure are considered in the context of the mean squared deviation as well as the average run length.