Quality requirements of manufactured products or parts are given in the form of specification limits on the quality characteristics of individual units. If a product is to meet the customer’s fitness for use criteria, it should be produced by a process which is stable or repeatable. In other words, it must be capable of operating with little variability around the target value or nominal value of the product’s quality characteristic. In order to maintain and improve product quality, we need to apply statistical process control techniques such as histogram, check sheet, Pareto chart, cause and effect diagram, or control charts. Among those techniques, the most important one is control charting. The cumulative sum (CUSUM) control charts have been used in statistical process control (SPC) in industries for monitoring process shifts and supporting online measurement. The objective of this research is to apply Taguchi's quality loss function concept to cost based CUSUM control chart design. In this study, a modified quality loss function was developed to reflect quality loss situation where general quadratic loss curve is not appropriate. This research also provided a methodology for the design of CUSUM charts using Taguchi quality loss function concept based on the minimum cost per hour criterion. The new model differs from previous models in that the model assumes that quality loss is incurred even in the incontrol period. This model was compared with other cost based CUSUM models by Wu and Goel, According to numerical sensitivity analysis, the proposed model results in longer average run length in in-control period compared to the other two models.

이 논문은 공정변화를 보다 잘 감지할 수 있는 관리도의 개발동향에 주안점을 두고 있으며, 경제적 접근법으로 관리모수를 설정하여 공정관리 메카니즘을 사용하는데 있어서 최소의 비용을 가지도록 하여 품질 향상 비용을 절감시킬 수 있는 설계 접근방법을 조사 연구했다. 또한 CUSUM 관리도를 기존의 다양한 관리도와 결합하여 개발된 새로운 관리도를 비교했다. 비교된 관리도의 경제적 모형설계를 통하여 공정 품질에서의 경제적인 영향의 최적화를 위한 관리모수를 제시했다. 이는 공정평균의 이동을 감지하기 위한 결합 관리도를 개발하는 경제적설계 절차를 제시했다.

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

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.

현대의 산업은 점차 분야가 다양해지고 기술이 첨단화되며, 고객의 요구사항이 복잡해지고 있다. 이에 따라 현대의 첨단산업에서 제조파트는 제조기술의 초정밀, 극소불량, 고신뢰도가 요구되어지고 있는 실정이다. 이런 제조파트의 핵심 기술인 SPC기법 중에서 누적합(CUSUM) 관리도는 공정의 작은 변화에 대해서 민감하다는 장점 때문에 첨단 산업인 반도체나 화학공정 등에서 활용도가 높은 관리도 기법이다. 하지만 복잡한 이론 체계로 인하여 사용편리성이 떨어진다는 단점이 있어서 널리 사용되지는 못 하고 있는 실정이다. 본 논문에서는 누적합 관리도의 이론적 전개에 관한 체계적인 동향 분석을 통해 누적합 관리도의 복잡한 이론 체계를 이해하는데 도움을 주고 더 나아가 앞으로의 제조 기술의 방향성을 제시하고자한다.

현대의 산업은 점차 분야가 다양해지고 기술이 첨단화되며, 고객의 요구사항이 복잡해지고 있다. 이에 따라 제조업에서는 초정밀, 고신뢰도가 요구되어지고 있는 실정이다. 제조업 분야의 핵심 기술인 SPC기법 중에서 누적합(CUSUM) 관리도는 공정의 작은 변화에 대해서 민감하다는 특징 때문에 첨단 산업인 반도체나 화학공정 등에서 활용도가 높은 관리도 기법이다. 하지만 복잡한 이론 체계로 인하여 사용편리성이 떨어진다는 단점이 있다. 본 논문에서는 누적합 관리도의 이론적 전개에 관한 체계적인 조사연구를 통해 누적합 관리도의 복잡한 이론 체계를 이해하는데 도움이 되고자 한다.

Complex Products may present more than one type of defects and these defects are not always of equal severity. These defects are classified according to their seriousness and effect on product quality and performance. Demerit systems are very effective sy

CUSUM control charts are widely used in process with small shifts since they have reasonable detection capability for small shifts. CUSUM control charts, however, are less effective in detection for recurring cycles or frequent small shifts in process. With Shewhart control charts, we have applied the variety of run rules to check the stability of process in addition to the situations that some points fall outside the control limits. In this paper, we propose the Z-CUSUM control chart.

The CRL(Conforming Run Length) control chart can perform for high yield processes and define the number of conforming items between two consecutive non-conforming ones. But the CRL control chart is not appropriate for lower yield processes because it is difficult to detect small shift in processes with the CRL control chart. This paper presents a combined CRL-CUSUM control chart and this is more efficient than CRL control chart for detecting small shift and uses the Markov Chain approach to calculate ARL(Average Run Length) values.