지난 몇 십년동안 전 세계적으로 제조기술 분야의 급속한 성장으로 대부분의 제조 업에서 눈부실만한 품질향상과 생산성 극대화를 이루어 왔다. 하지만 현재 제조기술 분야는 새로운 문제에 직면하고 있다. 실제 현장에서 관리하고자하는 품질특성은 정규 분포를 따르지 않는 경우가 많은데, 대부분의 통계적 공정관리기술체계가 정규분포를 기반으로 하고 있다는 것이다. 또한 기존의 불량률 관리도로는 PPM/PPB 수준의 극소 불량률을 관리하는데 한계가 있다. 이러한 이유로 비정규 공정에서 극소불량관리, 미 세변동관리에 대한 연구가 시급한 실정이다. 본 논문에서는 비정규 공정에서 극소 불량률을 관리하기 위해서   통계량에 Burr 분포를 적용하는 방법을 제안하고자 한다.

With the advent of lean-six sigma era, an extensive use of analytic tools such as control charts is required in the field of manufacturing. In relation to statistical quality control (SQC) or process control (SPC), the Korean standards have undergone a meaningful change. In this study, the theoretic backgrounds for evaluating the control limits in connection with the variable control charts are examined in view of better understanding the related constants and coefficients. This paper is intended to help the quality control practitioners understand the mathematical backgrounds by comparing related quality control constants and also to encourage them to make use of and to take the advantage of the variable control charts which are very useful for implementing the concept of lean-six sigma in many industrial sites.

지난 몇 십년동안 전 세계적으로 제조기술 분야의 급속한 성장으로 대부분의 제조 업에서 눈부실만한 품질향상과 생산성 극대화를 이루어 왔다. 하지만 현재 제조기술 분야는 새로운 문제에 직면하고 있다. 실제 현장에서 관리하고자하는 품질특성은 정규 분포를 따르지 않는 경우가 많은데, 대부분의 통계적 공정관리기술체계가 정규분포를 기반으로 하고 있다는 것이다. 이러한 이유로 비정규 공정에서 극소불량관리, 미세변 동관리에 대한 연구가 시급한 실정이다. 본 논문에서는 비정규 공정에서 불량률을 관리하기 위해 다양한 비정규 분포를 대 표할 수 있는 Burr 분포를 선택하여 적용방법을 제안하고자 한다.

지난 몇 십년동안 전 세계적으로 제조기술 분야의 급속한 성장으로 대부분의 제조업에서 눈부실만한 품질향상과 생산성 극대화를 이루어 왔다. 하지만 현재 제조기술 분야는 새로운 문제에 직면하고 있다. 실제 현장에서 관리하고자하는 품질특성은 정규분포를 따르지 않는 경우가 많은데, 대부분의 통계적 공정관리기술체계가 정규분포를 기반으로 하고 있다는 것이다. 이러한 이유로 비정규 공정에서 극소불량관리, 미세변동관리에 대한 연구가 시급한 실정이다. 하지만 비정규 분포를 통계적으로만 해석하고 설계하기 위해서는 현실적으로 한계가 있으며, 경제적 설계의 접근방법이 또 하나의 좋은 대안이 될 수 있다. 본 논문에서는 비정규 공정에서 관리도의 경제적 설계를 위한 연구동향을 살펴보고 추후 연구방향에 대해 제시하고자 한다.

Control chart is most widely used in SPC(Statistical Process Control), Recently it is a critical issue that the standard control chart is not suitable to non-normal process with very small percent defective. Especially, this problem causes serious errors in the reliability procurement, such as semiconductor, high-precision machining and chemical process etc. Procuring process control technique for non-normal process with very small percent defective and perturbation is becoming urgent. Control chart technique in non-normal distribution become very important issue. In this paper, we investigate on research trend of control charts under non-normal distribution.

The design method for cumulative sum (CUSUM) control charts, which can be robust to autoregressive moving average (ARMA) modeling errors, has not been frequently proposed so far. This is because the CUSUM statistic involves a maximum function, which is in

In some manufacturing processes, the variance can change, depending on the process mean. For example, if the output value reflects process yield, an increased mean might naturally lead to an increase in variance. When the variance is a function of the mean, the coefficient of variation (CV) is an appropriate measure for process variability. Since the CV control chart was first introduced by Kang et al (2007), there were some trials to improve the performance of CV control charts depending on the shift size. In this research, we present some CV related control charts and compare the performance of those control charts for better use in the field. The CV control chart shows good sensitivity to large shift in CV. The CV-EWMA control chart(2008), the CV-DEWMA control chart(2011) and the CV-GWMA control chart(2011) were developed to control processes sensitively responding to small shifts of CV. The FIR CV-GWMA control chart is more effective control chart to detecting off-target processes in the stage of set-up or start-up of process.

Monitoring autocorrelated processes is prevalent in recent manufacturing environments. As a proactive control for manufacturing processes is emphasized especially in the semiconductor industry, it is natural to monitor real-time status of equipment throug

Control chart is most widely used in SPC(Statistical Process Control), Recently it is a critical issue that the standard control chart is not suitable to non-normal process with very small percent defective. Especially, this problem causes serious errors in the reliability procurement, such as semiconductor, high-precision machining and chemical process etc. Procuring process control technique for non-normal process with very small percent defective and perturbation is becoming urgent. Control chart technique in non-normal distribution become very important issue. In this paper, we investigate on research trend of control charts under non-normal distribution with very small percent defective and perturbation, and propose some variable-transformation methods applicable to CUSUM control charts in non-normal process.

The research developes short-run standardized control charts(SSCC) and short-run acceptance control charts(SACC) under the various demand patterns. The demand patterns considered in this paper are three types such as high-variety and repetitive low-volume pattern, extremely-high-variety and nonrepetitive low-volume pattern, and high-variety and extremely-low-volume pattern. The short-run standardized control charts developed by extending the long-run x-R, x-s and I-MR charts have strengths for practioners to understand and use easily. Moreover, the short-range acceptance control charts developed in the study can be efficiently used through combining the functions of the inspection and control chart. The weighting schemes such as Shewhart, moving average (MA) and exponentially weighted moving average (EWMA) can be considered by the reliability of data sets. The two types according to the use of control chart are presented in the short-range standardized charts and acceptance control charts. Finally, process capability index(PCI) and process performance index(PPI) classified by the demand patterns are presented.

The study investigates the various Acceptance Control Charts (ACCs) based on the factors that include process independence, data weighting scheme, subgrouping, and use of control charts. USL - LSL 〉 6Σ that used in the good condition processes in the ACCs are designed by considering user's perspective, producer's perspective and both perspectives. ACCs developed from the research is efficiently applied by using the simple control limit unified with APL (Acceptable Process Level), RLP (Rejectable Process Level), Type I Error α, and Type II Error β. Sampling interval of subgroup examines i.i.d. (Identically and Independent Distributed) or auto-correlated processes. Three types of weight schemes according to the reliability of data include Shewhart, Moving Average(MA) and Exponentially Weighted Moving Average (EWMA) which are considered when designing ACCs. Two types of control charts by the purpose of improvement are also presented. Overall, α, β and APL for nonconforming proportion and RPL of claim proportion can be designed by practioners who emphasize productivity and claim defense cost.

  Recently, the control chart is developed for monitoring processes with normal short production runs by the coefficient of variation(CV) characteristic for a normal distribution. This control chart does not work well in non-normal short production runs.

This paper deals with an efficient and effective method on measuring, monitoring and evaluating safety and environmental performances of a process using SPC control charts. We propose 7 safety control charts as a tool to monitor hazard dendritics, and we propose 15 environment control charts to monitor pollution emissions. We also propose useful guidelines that SPC(Statistical Process Control) control charts can be used for safely and environmental monitoring.

This paper presents several control charts for safety and environmental performance monitoring. We also propose guidelines that control charts for statistical process control can be used for safety and environmental performance evaluation.

The coefficient of variation(CV) which is a relatively dimensionless measure of variability is widely used to describe the variation of sample data. But little research is available to its distribution, confidence interval and interpret method etc. In this paper, we give an outline of statistical properties of coefficient of variation and design of control chart based on this statistic. Construction procedures of control chart are presented. The proposed control chart is an efficient method to monitoring a process variation for multiple product processes. Futhermore, the performance is evaluated by average run length(ARL).