• Title/Summary/Keyword: statistical process control

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Comparison and Evaluation of Performance for Standard Control Limits and Bootstrap Percentile Control Limits in $\bar{x}$ Control Chart ($\bar{x}$ 관리도의 표준관리한계와 부트스트랩 백분률 관리한계의 수행도 비교평가)

  • 송서일;이만웅
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.22 no.52
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    • pp.347-354
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    • 1999
  • Statistical Process Control(SPC) which uses control charts is widely used to inspect and improve manufacturing process as a effective method. A parametric method is the most common in statistical process control. Shewhart chart was made under the assumption that measurements are independent and normal distribution. In practice, this assumption is often excluded, for example, in case of (equation omitted) chart, when the subgroup sample is small or correlation, it happens that measured data have bias or rejection of the normality test. A bootstrap method can be used in such a situation, which is calculated by resampling procedure without pre-distribution assumption. In this study, applying bootstrap percentile method to (equation omitted) chart, it is compared and evaluated standard process control limit with bootstrap percentile control limit. Also, under the normal and non-normal distributions, where parameter is 0.5, using computer simulation, it is compared standard parametric with bootstrap method which is used to decide process control limits in process quality.

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A Comparative Study of SPC and EPC with a Focus on Their Integration (통계적 공정 관리(SPC)와 엔지니어링 공정 관리(EPC)의 비교 조사 : 통합 방안을 중심으로)

  • Lee, Myeong-Soo;Kim, Kwang-Jae
    • Journal of Korean Society for Quality Management
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    • v.33 no.1
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    • pp.22-31
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    • 2005
  • With the common objective to improve process productivity and product quality, statistical process control (SPC) and engineering process control (EPC) have been widely used in the discrete-parts industry and the process industry, respectively. The major focus of SPC is on process monitoring, while that of EPC is on process adjustment. The emergence of the hybrid industry necessitates a synergistic combination of the two methods for an effective process control. This paper investigates the existing studies on SPC, EPC, and the integration of the two methods. This paper also presents future research issues in this field.

Discrimination of Out-of-Control Condition Using AIC in (x, s) Control Chart

  • Takemoto, Yasuhiko;Arizono, Ikuo;Satoh, Takanori
    • Industrial Engineering and Management Systems
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    • v.12 no.2
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    • pp.112-117
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    • 2013
  • The $\overline{x}$ control chart for the process mean and either the R or s control chart for the process dispersion have been used together to monitor the manufacturing processes. However, it has been pointed out that this procedure is flawed by a fault that makes it difficult to capture the behavior of process condition visually by considering the relationship between the shift in the process mean and the change in the process dispersion because the respective characteristics are monitored by an individual control chart in parallel. Then, the ($\overline{x}$, s) control chart has been proposed to enable the process managers to monitor the changes in the process mean, process dispersion, or both. On the one hand, identifying which process parameters are responsible for out-of-control condition of process is one of the important issues in the process management. It is especially important in the ($\overline{x}$, s) control chart where some parameters are monitored at a single plane. The previous literature has proposed the multiple decision method based on the statistical hypothesis tests to identify the parameters responsible for out-of-control condition. In this paper, we propose how to identify parameters responsible for out-of-control condition using the information criterion. Then, the effectiveness of proposed method is shown through some numerical experiments.

Implementation of Statistical Process Control Software developed by Object Oriented Tools (객체지향언어를 이용한 통계적 공정관리 소프트웨어의 구현)

  • 신봉섭
    • Journal of Korean Society for Quality Management
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    • v.27 no.4
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    • pp.256-265
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    • 1999
  • In this paper, we Present the implementation of statistical process control software by using XLISP-STAT which is a kind of object oriented language under Windows environment. This software can be used to generate the graphic objects for various control charts, histogram and plots using the full-down menu system. This software can also be used to calculate control limits, process capability indices and test procedures for normality.

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A Study on the Improvement Methods for Sausage Stuffing Process

  • Lee, Jae-Man;Cha, Young-Joon;Hong, Yeon-Woong
    • 한국데이터정보과학회:학술대회논문집
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    • 2005.04a
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    • pp.7-17
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    • 2005
  • Consider a stuffing process where sausage-casings are filled with sausage-kneading. One of the most important factors in the stuffing process is weights of stuffed sausages. Sausages weighting above the specified limit are sold in a regular market price for a fixed price, and underfilled sausages are reworked at the expense of reprocessing cost. In this paper, the sausage stuffing process is inspected for improving productivity and quality levels. Several statistical process control tools are suggested by using real data obtained from a Korean Vienna sausage company.

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A Study on the Improvement Methods for Sausage Stuffing Process

  • Lee, Jae-Man;Cha, Young-Joon;Hong, Yeon-Woong
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.2
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    • pp.391-399
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    • 2005
  • Consider a stuffing process where sausage-casings are filled with sausage-kneading. One of the most important factors in the stuffing process is weights of stuffed sausages. Sausages weighting above the specified limit are sold in a regular market price for a fixed price, and underfilled sausages are reworked at the expense of reprocessing cost. In this paper, the sausage stuffing process is inspected for improving productivity and quality levels. Several statistical process control tools are suggested by using real data obtained from a Korean Vienna sausage company.

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Rule-based Process Control System for multi-product, small-sized production (다품종 소량생산 공정을 위한 규칙기반 공정관리 시스템)

  • Im, Kwang-Hyuk
    • Journal of Korea Society of Industrial Information Systems
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    • v.15 no.1
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    • pp.47-57
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    • 2010
  • There have been many problems to apply SPC(Statistical Process Control) which is a traditional process control technology to the process of multi-product, small-sized production because a machine in the process manufactures small numbers, but various kinds of products. Therefore, we need the new process control system that can flexibly control the process by setting up the SPEC rules and the KNOWHOW rules. The SPEC rule contains the combination of diverse conditions to specify the characteristics of various products. The KNOWHOW rule is based on engineers' know-how. The study suggests the Rule-base Process Control that can be optimized to the multi-product, small-sized production. It was validated in the process of semiconductor production.

Multioutput LS-SVR based residual MCUSUM control chart for autocorrelated process

  • Hwang, Changha
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.2
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    • pp.523-530
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    • 2016
  • Most classical control charts assume that processes are serially independent, and autocorrelation among variables makes them unreliable. To address this issue, a variety of statistical approaches has been employed to estimate the serial structure of the process. In this paper, we propose a multioutput least squares support vector regression and apply it to construct a residual multivariate cumulative sum control chart for detecting changes in the process mean vector. Numerical studies demonstrate that the proposed multioutput least squares support vector regression based control chart provides more satisfying results in detecting small shifts in the process mean vector.

An Integrated Process Control Scheme Based on the Future Loss (미래손실에 기초한 통합공정관리계획)

  • Park, Chang-Soon;Lee, Jae-Heon
    • 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.

The Economic Design of VSS $\bar{x}$ Control Chart for Compounding Effect of Double Assignable Causes (두 가지 복합 이상원인 영향이 있는 공정에 대한 VSS$\bar{x}$관리도의 경제적 설계)

  • Sim Seong-Bo;Kang Chang-Wook;Kang Hae-Woon
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.27 no.2
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    • pp.114-122
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    • 2004
  • In statistical process control applications, variable sample size (VSS) $\bar{X}$ chart is often used to detect the assignable cause quickly. However, it is usually assumed that only one assignable cause results in the out-of-control in the process. In this paper, we propose the algorithm to minimize the function of cost per unit time and compare the economic design and the statistical design by use of the value of cost per unit time. We consider double assignable causes to occur with compound in the process and adopt the Markov chain approach to investigate the statistical properties of VSS $\bar{X}$ chart. A procedure that can calculate the control chart's parameters is proposed by the economic design.