The RPV internal structure is a high radio activated part and has very complex geometry. Therefore, it needs to be cut remotely with an automated cutting system to minimize the worker exposures. To do so, we made up the remote laser cutting system with a laser cutter, robot manipulator and control software system and the laser cutter is moved by the robot manipulator based on the command from the control software system. A laser cutter is required to keep the desired standoff position between the nozzle of the laser cutter and surface of the cut target model to cut properly. Moreover, in the remote cutting process, an exact time and sequence control of the air supply and the laser emission is required for the cutting quality and the process safety. In this study, we proposed the PERT chart-based process execution and control methodology. The PERT chart is a graph which is represented by nodes and edges. The node of the PERT chart has the information about the activity details such as activity type, execution time and related device. Using the edge we make the sequence of the desired activity execution. A PERT chart of the cutting scenario is compiled in the control software system to creates data and thread structure to operate the physical device. We built software architecture to interpret and execute the PERT chart efficiently in the digital simulation platform which enables us to use existing pre-built simulation scenario for the laser cutting process. In addition, we have tested various laser cutting test cases in our test bed to verify the performance of our system. The test bed environment has the shape of the RPV internal structure and is placed under water.

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.

Control Chart is a graph which dots the characteristic values of a process. It is the tool of statistical technique to keep a process in controlled condition. It is also used for investigating the state of a process. Therefore many companies have used Control Chart as the tool of statistical process control (SPC). Products from a production process represent accidental dispersion values around a certain reference value. Fluctuations cause of quality dispersion is classified as a chance cause and a assignable cause. Chance cause refers unmanageable practical cause such as operator proficiency differences, differences in work environment, etc. Assignable cause refers manageable cause which is possible to take actions to remove such as operator inattention, error of production equipment, etc. Traditionally x-R control chart or x-s control chart is used to find and remove the error cause. Traditional control chart is to determine whether the measured data are in control or not, and lets us to take action. On the other hand, RNELCC (Reflected Normal Expected Loss Control Chart) is a control chart which, even in controlled state, indicates the information of economic loss if a product is in inconsistent state with process target value. However, contaminated process can cause control line sensitive and cause problems with the detection capabilities of chart. Many studies on robust estimation using trimmed parameters have been conducted. We suggest robust RNELCC which used the idea of trimmed parameters with RNEL control chart. And we demonstrate effectiveness of new control chart by comparing with ARL value among traditional control chart, RNELCC and robust RNELCC.

초고온가스로는 고온의 원자로 열을 이용하여 대량의 청정 수소와 고효율의 전기를 생산할 수 있는 제 4세대 원자로이다. 초고온가스로는 0.5mm 직경의 우라늄을 세라믹으로 3중 코딩해 직경 약 0.9mm의 TRISO라고 불리 는 피복입자를 사용한다. TRISO는 크기가 작을 뿐 아니라 특수 코팅 처리가 되어 있어 우라늄이 직접 공기 중 에 노출될 일이 없다. 핵연료 품질 유지 측면에서 TRISO 제작시 핵연료의 동일한 구형성, 밀도 및 피복층 두께 를 유지하는 것이 중요하다. 본 논문에서는 X-선 래디오그래피 기술을 이용한 비파괴 방법을 적용하여 측정한 TRISO 피복입자의 피복층 두께 자료를 바탕으로 피복입자핵연료의 대량 제조 관리를 가정하였고, 이 경우 X-R 관리도를 이용하여 생산 공정의 공정 이상 여부를 시뮬레이션하였다.(한글초록 300자 내외)

Control chart is representative tools of statistical process control (SPC). It is a graph that plotting the characteristic values from the process . It has two steps (or Phase). First step is a procedure for finding a process parameters. It is called PhaseⅠ. This step is to find the process parameters by using data obtained from in-controlled process. It is a step that the standard value was not determined. Another step is monitoring process by already known process parameters from PhaseⅠ. It is called Phase Ⅱ. These control chart is the process quality characteristic value for management, which is plotted dot whether the existence within the control limit or not. But, this is not given information about the economic loss that occurs when a product characteristic value does not match the target value. In order to meet the customer needs, company not only consider stability of the process variation but also produce the product that is meet the target value. Taguchi’s quadratic loss function is include information about economic loss that occurred by the mismatch the target value. However, Taguchi’s quadratic loss function is very simple quadratic curve. It is difficult to realistically reflect the increased amount of loss that due to a deviation from the target value. Also, it can be well explained by only on condition that the normal process. Spiring proposed an alternative loss function that called reflected normal loss function (RNLF). In this paper, we design a new control chart for overcome these disadvantage by using the Spiring’s RNLF. And we demonstrate effectiveness of new control chart by comparing its average run length (ARL) with x-R control chart and expected loss control chart (ELCC).

Recently, the production cycle in manufacturing process has been getting shorter and different types of product have been produced in the same process line. In this case, the control chart using coefficient of variation would be applicable to the process. The theory that random variables are located in the three times distance of the deviation from mean value is applicable to the control chart that monitor the process in the manufacturing line, when the data of process are changed by the type of normal distribution. It is possible to apply to the control chart of coefficient of variation too.  ,  estimates that taken in the coefficient of variation have just used all of the data, but the upper control limit, center line and lower control limit have been settled by the effect of abnormal values, so this control chart could be in trouble of detection ability of the assignable value. The purpose of this study was to present the robust control chart than coefficient of variation control chart in the normal process. To perform this research, the location parameter, xα, sα were used. The robust control chart was named Tim-CV control chart. The result of simulation were summarized as follows; First, P values, the probability to get away from control limit, in Trim-CV control chart were larger than CV control chart in the normal process. Second, ARL values, average run length, in Trim-CV control chart were smaller than CV control chart in the normal process. Particularly, the difference of performance of two control charts was so sure when the change of the process was getting to bigger. Therefore, the Trim-CV control chart proposed in this paper would be more efficient tool than CV control chart in small quantity batch production.

Control chart is a graph of plotting dot in the process characteristic values. It is a statistical technique that can be known whether or not the in-control state in this step. In many companies have use as a statistical process control(SPC) tool. Control chart is the management process quality characteristic value, which is plotted dot is whether the existence within the control limits. But, this is not given information about the economic loss that occurs when a product is produced characteristic value does not match the target value of the process. In that sence, expected loss control chart(EL control chart) is very effective process control tool. Because it is a process control chart in consideration to economic loss. The EL control chart is using the quadratic loss function of Taguchi. However, Taguchi’s quadratic loss function is simple quadratic curve. It is difficult to realistically reflect the increased amount of loss that due to a deviation from the target value. In this paper, we design a new control chart using the reflected normal loss function(RNLF). And we demonstrate its effectiveness by using the control chart performance comparison of EL control chart.

Control charts are generally used for process control, but the role of traditional control charts have been limited in case of a non-contaminated process. Traditional x-s control chart has not been activated well for such a problem because of trying to control processes as center line and control limits changed by the contaminated value. This paper suggests modified x-s control chart based on robust estimation. In this paper, we consider the trimmed mean of the sample means and the trimmed mean of the sample standard deviations. By comparing with ARL value, the responding results are decided. The comparison resultant results of traditional control chart and modified control chart are contrasted.

Control charts are generally used for process control, but the role of traditional control charts have been limited in case of a contaminated process. Traditional x control charts have not been activated well for such a problem because of trying to control processes as center line and control limits changed by the contaminated value. This paper is to propose robust x control charts which is considering a location parameter in order to respond to contaminated process. In this paper, we consider x, that is trimmed rate; typically ten percent rate is used. By comparing with p, ARL value, the responding results are decided. The comparison resultant results of proposed two control charts are shown and are well contrasted.

산업의 빠른 발전 속도에 따라 연구 개발도 함께 발전해야 한다. 따라서 현재 제조공정에 대한 품질 특성치의 분석방법으로 공정 모수의 작은 변화도 쉽게 탐지를 할 수 있는 EWMA 관리도와 Shewhart 관리도보다 공정 변화에 민감하게 탐지 가능한 CUSUM 관리도에 관한 연구가 많이 이루어지고 있다. 하지만 식스시그마 공정관리에 맞춘 평균, 불량률, 미세 분산을 동시에 감지할 수 있는 동시 관리 체계 연구는 많이 미흡하다. 본 연구에서는 기존의 CUSUM, EWMA 관리도 기법보다 빠른 이상 감지를 위해서 평균, 불량률, 분산 3가지가 동시에 관리되어질 수 있도록 Zp-s 관리도를 소개한다. Zp-s 관리도는 ARL을 통해 기존 관리도보다 민감함을 확인할 수 있다.

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.

Control chart is representative tool of Statistical Process Control (SPC). But, it is not given information about the economic loss that occurs when a product is produced characteristic value does not match the target value of the process. In order to manage the process, we should consider not only stability of the variation also produce products with a high degree of matching the target value that is most ideal quality characteristics. There is a need for process control in consideration of economic loss. In this paper, we design a new control chart using the quadratic loss function of Taguchi. And we demonstrate effectiveness of new control chart by compare its ARL with   control chart.

Control chart is a graph of plotting dot about the process characteristic values. It is a statistical technique that can be known whether or not the in-control state in this step. In many companies have use as a statistical process control(SPC) tool. Control chart is the management process quality characteristic value, which is plotted dot is whether the existence within the control limits. This is not given information about the economic loss that occurs when a product is produced characteristic value does not match the target value of the process. In order to manage the process, we should consider not only stability of the variation also produce products with a high degree of matching the target value that is most ideal quality characteristics. There is a need for process control in consideration of economic loss. In this paper, we design a new control chart using the quadratic loss function of Taguchi. Use the control chart performance comparison of x-R, which is a traditional control chart, we demonstrate its effectiveness.

제조공정에서 사용되어 지는 SPC(Statistical Process Control)관리 기법은 가피원인을 탐지하여 변동을 감소시키는 통계적 공정관리 시스템이다. SPC의 대표적인 관리 기법으로는 Shewhart관리도, Cusum관리도, EWMA관리도가 있으며 이러한 관리 기법들은 공정을 보다 안정적으로 관리 할 수 있도록 유지 및 예측하는데 사용 되어 진다. 하지만 제조 공정의 유형에 따라 샘플링 방법, 관리한계선 등을 다양하게 설정하여 보다 효율적인 관리를 모색하고 있다. 공정 형태에 따라 다양한 관리 방법과 분석 결과가 나타난다. 일반적으로 Xbar-R 관리도와 같은 Shewhart 관리도를 사용하지만 Batch 단위의 공정, 연속 공정의 라인에서 사용되기에는 부분적인 한계를 보이고 있다. 본 논문에서는 일반적인 관리도와 공정 변화에 민감하게 반응 할 수 있는 누적합 관리도와 지수가중치이동평균 관리도를 비교해 보고 작은 변동에 대한 탐지 능력이 우수한 지수가중치이동평균 관리도에 대한 연구동향과 사례를 분석하여 제조 공정에 적합한 관리 방법을 모색하고자 한다.

이 논문은 미세공정변동에서 극소불량을 감지하는 관리도의 경제적 설계를 개발하기 위한 조사연구이다. 일반적인 관리도의 설계는 통계적 설계와 경제적 설계로 구분할 수 있다. 공정의 변동 원인에 따라 샘플의 간격(h), 샘플의 크기(n), 관리한계선(k) 등의 설계 모수를 최적접근방법으로 결정을 하는 경제적 설계의 모델을 조사하였다. 관리도의 경제적 설계는 공정의 관리이상상태를 효율적으로 감지하여 관리상태로 정상화 시키는 것에 대한 공정의 개선비용과 기대품질비용을 절약 할 수 있는 최적설계 방안이다. 그리고 Shewhart 관리도의 X-bar 통계량으로 극소불량을 검출 하는것에 한계가 있기 때문에 Zp 통계량과 분포를 설계하여 극소불량을 빠르게 감지할 수 있는 Zp 관리도의 설계를 적용하고, 미세공정변동을 정확하게 감지할 수 있는 CUSUM 관리도를 동시에 적용하였다. 따라서, 미세공정변동과 극소불량을 동시에 관리 할 수 있는 Zp-CUSUM 관리도의 통계적 설계 구조를 체계화 하였으며, 기존의 경제적 설계의 모델을 비교 분석하여 새로운 경제적 설계에 대한 모델을 제안하고자 한다.

제조공정에서 사용되어 지는 SPC(Statistical Process Control)관리 기법은 가피원인 을 탐지하여 변동을 감소시키는 통계적 공정관리 시스템이다. SPC의 대표적인 관리 기법으로는 Shewhart관리도, Cusum관리도, EWMA관리도가 있으며 이러한 관리 기법 들은 공정을 보다 안정적으로 관리 할 수 있도록 유지 및 예측하는데 사용 되어 진다. 본 논문에서는 일반적으로 사용되어 지는 Shewhart관리도와 공정 예측에 유리한 EWMA 관리도에 대해 연구해보고 공정변화에 민감하게 반응하는 EWMA 관리도의 적용 사례를 제시하고자 한다.

In the manufacturing process the most widely used x chart has been applied to control the process mean. Also, Accelerated Life Test(ALT) is commonly used for efficient assurance of product life in development phases, which can be applied in production reliability acceptance test. When life data has lognormal distribution, through censored ALT design so that censored ALT data has asymptotic normal distribution, ALTx control chart integrating x chart and ALT procedure could be applied to control the mean of process in the manufacturing process. In the situation that process variation is controlled, Zp control chart is an effective method for the very small fraction nonconforming of quality characteristic. A simultaneous control scheme with ALTx control chart and Zp control chart is designed for the very small fraction nonconforming of product lifetime.

전통적인 Demerit 관리도는 일반적으로 자동차, 컴퓨터, 핸드폰 등 하나의 복잡한 제품에서 다양한 유형의 결점들이 동시에 발생하는 공정을 모니터링하기 위하여 사용된다. 관리도에서 공정 평균의 작은 변동을 빠르게 감지하기 위한 방법으로 EWMA기법은 매우 효과적이다. 본 연구에서는 EWMA 기법 보다 공정 평균의 작은 변동에 더 좋은 성능을 보이는 GWMA기법과 전통적인 Demerit 관리도(1928)를 결합한 Demerit-GWMA(generally weighted moving average) 관리도를 제안하였으며 그 관리도의 수행도를 평가하였다. 그 수행도 평가 결과, Demerit-GWMA 관리도는 공정 평균의 작은 변동을 감지하는데 classical Demerit 관리도, Demerit-EWMA. 관리도에 비하여 더 민감하다.

This paper proposes a variable sampling interval multivariate T² control chart with sampling at fixed times, where samples are taken at specified equally spaced fixed time points and additional samples are allowed between these fixed times when indicated

When the production run is short and process parameters change frequently, it is difficult to monitor the process using traditional control charts. In such a case, the coefficient of variation (CV) is very useful for monitoring the process variability. Th