• Title, Summary, Keyword: statistical process control

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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 the 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.

Applying Expert System to Statistical Process Control in Semiconductor Manufacturing (반도체 수율 향상을 위한 통계적 공정 제어에 전문가 시스템의 적용에 관한 연구)

  • 윤건상;최문규;김훈모;조대호;이칠기
    • Journal of the Korean Society for Precision Engineering
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    • v.15 no.10
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    • pp.103-112
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    • 1998
  • The evolution of semiconductor manufacturing technology has accelerated the reduction of device dimensions and the increase of integrated circuit density. In order to improve yield within a short turn around time and maintain it at high level, a system that can rapidly determine problematic processing steps is needed. The statistical process control detects abnormal process variation of key parameters. Expert systems in SPC can serve as a valuable tool to automate the analysis and interpretation of control charts. A set of IF-THEN rules was used to formalize knowledge base of special causes. This research proposes a strategy to apply expert system to SPC in semiconductor manufacturing. In analysis, the expert system accomplishes the instability detection of process parameter, In diagnosis, an engineer is supported by process analyzer program. An example has been used to demonstrate the expert system and the process analyzer.

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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 the 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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    • 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 the Korea Industrial Information Systems Research
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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.

Adjustment of Control Limits for Geometric Charts

  • Kim, Byung Jun;Lee, Jaeheon
    • Communications for Statistical Applications and Methods
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    • v.22 no.5
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    • pp.519-530
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    • 2015
  • The geometric chart has proven more effective than Shewhart p or np charts to monitor the proportion nonconforming in high-quality processes. Implementing a geometric chart commonly requires the assumption that the in-control proportion nonconforming is known or accurately estimated. However, accurate parameter estimation is very difficult and may require a larger sample size than that available in practice in high-quality process where the proportion of nonconforming items is very small. Thus, the error in the parameter estimation increases and may lead to deterioration in the performance of the control chart if a sample size is inadequate. We suggest adjusting the control limits in order to improve the performance when a sample size is insufficient to estimate the parameter. We propose a linear function for the adjustment constant, which is a function of the sample size, the number of nonconforming items in a sample, and the false alarm rate. We also compare the performance of the geometric charts without and with adjustment using the expected value of the average run length (ARL) and the standard deviation of the ARL (SDARL).

An Economic-Statistical Design of Moving Average Control Charts

  • Yu, Fong-Jung;Chin, Hsiang;Huang, Hsiao Wei
    • International Journal of Quality Innovation
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    • v.7 no.3
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    • pp.107-115
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    • 2006
  • Control charts are important tools of statistical quality control. In 1956, Duncan first proposed the economic design of $\bar{x}-control$ charts to control normal process means and insure that the economic design control chart actually has a lower cost, compared with a Shewhart control chart. An moving average (MA) control chart is more effective than a Shewhart control chart in detecting small process shifts and is considered by some to be simpler to implement than the CUSUM. An economic design of MA control chart has also been proposed in 2005. The weaknesses to only the economic design are poor statistics because it dose not consider type I or type II errors and average time to signal when selecting design parameters for control chart. This paper provides a construction of an economic-statistical model to determine the optimal parameters of an MA control chart to improve economic design. A numerical example is employed to demonstrate the model's working and its sensitivity analysis is also provided.

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.