Designers have to consider voice of customer, process capability, manufacturing standards & condition, manufacturing method, characteristics of products to decide tolerances. Especially, in case of position of hole and pin, designers have to consider process capability to decide tolerances. The traditional position tolerances used in a drawing are theoretical values which are allocated to position under the worst case assembling condition that both hole and pin are the maximum material condition(MMC). However, When the process capability is high, more exact product size can be produced under stable manufacturing condition. larger clearance of hole and pin can be allocated. In this point of view, manufacturer could increase the yield by allocating larger position tolerance than theoretical position tolerance of hole and pin considering process capability.

  Process Capability indices(PCIs) have been widely used in manufacturing industries to provide a quantitative measure of process performance. PCIs have been developed to represent process capability more exactly. The traditional process capability indice

  Process Capability indices (PCIs) have been widely used in manufacturing industries to provide a quantitative measure of process performance. PCIs have been developed to represent process capability more exactly. In the previous studies, only one design

It is necessary to deal with the process capability index carefully because it has been developed with certain assumptions. Companies make a decision on processes through the results obtained by using and treating data extracted from the processes. However if they have incorrect or wrong results, they cannot lead to proper outputs but also bring to loss of the competition in quality. Therefore, this study will show a method to analysis Cp (process capability ; CP) and an idea of mass-production on Pp (process performance ; PP) based on the Sigma Estimate which is one of the uncertainty in the process capability index and makes a lot of error. To apply this method, it is essential to understand and to analyze the processes exactly. Especially, it is required to establish the more accurate process capability index that can quickly and properly respond to changes on processes to recognize the small changes on the process which lies in specification in mass production system that the continual monitoring of quality managers is required.

The traditional process capability indices Cp, Cpk, Cpm, Cpm+ have been used to characterize process performance on the basis of univariate quality characteristics. Cp, Cpk consider the process variation, Cpm considers both the process variation and the p

Recently, a couple of process capability indices are used to evaluate that the outputs of the process satisfy the specifications. An assumption of those indices is that the specifications of the characteristics are given single constant value. The display

Process capability indices are widely used in industries and quality assurance system. When designing the parameter on the multiple quality characteristics, there has been a study for optimization of problems, but there has been few former study on the possible conflicting phenomena in considertion of the correlations among the characteristics. To solve the issue on the optimal design for muliple quality characteristics, the study propose the expected loss function with cross-product terms among the characteristics and derived range of the coefficients of terms. Therefore, the analysis have to be required a multivariate statistical technique. This paper introduces to multivariate capability indices and then selects a multivariate process capability index incorporated both the process variation and the process deviation from target among these indices under the multivariate normal distribution. We propose a new multivariate capability index MCpm using quality loss function instead of the process variation and this index is compared with the proposed indices when quality characteristics are independent and dependent of each other,

Process capability indices are widely used in industries and quality assurance system. When designing the parameter on the multiple quality characteristics, there has been a study for optimization of problems, but there has been few former study on the possible conflicting phenomena in considertion of the correlations among the characteristics. To solve the issue on the optimal design for multiple quality characteristics, the study propose the expected loss function with cross-product terms among the characteristics and derived range of the coefficients of terms. Therefore, the analysis have to be required a multivariate statistical technique. This paper introduces to multivariate capability indices and then selects a multivariate process capability index incorporated both the process variation and the process deviation from target among these indices under the multivariate normal distribution. We propose a new multivariate capability index MCpm using quality loss function instead of the process variation and this index is compared with the proposed indices when quality characteristics are independent and dependent of each other.

환경이란 우리주변을 둘러싸고 있는 물리적인 모든 것들을 뜻하는 것으로 주로 대기, 수질, 토양을 일컫는 말이다. 본 연구에서는 이러한 환경이라는 범위 안에서 가장 이슈가 되고 있는 오염 배출 물질 문제에 관한 것으로, 그 중 오염 배출 물질의 모니터링 방법에 관해서 다루고 있다. 환경 분야에서도 환경오염 배출 물질에 대한 비정상적인 변화나 시스템적인 패턴 등을 감지하는 목적으로 일반적으로 공정관리에서 주로 쓰이는 통계적 공정관리 기법 중 하나인 관리도를 사용하기 시작했다. 본 연구에서도 이러한 통계적 공정관리 기법을 환경관리 분야에 적용하여 공정관리에서 쓰이는 공정능력과 관리도의 개념을 환경문제에 적용시켜 오염 배출 물질에 대한 환경 관리 능력을 평가, 모니터링 하는 새로운 평가지표의 관리도를 제안, 설계하고자 한다.

As we understand it, Process Capability indices are intended to provide single-number assessments of ability to meet specification limits on quality characteristics of interest. As a consequence of the varied ways in which PCIs are used, there have been two natural lines of research work: ① studies on the properties of PCIs and their estimators in many different environments; ② construction of new PCIs purporting to have better properties in certain circumstances. The most of existing process capability indices are concerned with the single variable. But, in many cases, a quality characteristic is composed with several factors. In that case, we want to know the integrated process capability of a quality characteristic not those of each factor. In this paper, we proposed a new multivariate system process capability index called MSPCI:SCpsk which is the geometric mean of performance measure Cpsk'S, and will be used as the criterion to assess multiple response process designs. Numerical illustration is done for SCpsk, Cp(f), Cp, Cpk, Cpm, and Cpsk.

In this paper, We consider some generalization of these five basic indices to cover non-normal distribution. The proposed generalizations are compared with the five basic indices. The results show that the proposed generalizations are more accurate than t

The main objective of this paper to purpose a evaluating methods of process capability measures for exponential distributed quality characteristics. For correctly evaluating process capability , the first thing , exponential data is applied the Lilliefors test statistic to the null hypothesis of nornality. The next , exponential parameters is estimated in terms of MLE , ME , MME and then evaluated , respectively , process capability index based on exponential curved (Cpe). We consider simultaneously process capability messures based on Pearson system, Johnson system and Burr system.

A new display device development using CRT and CDT technology is required, which has concepts of flatness and slimness. Screen printing technology can be one of the solutions. In this case, good panel flatness is the precondition. Therefor, we did process capability analysis of panel flatness and regression analysis between panel flatness and BM(black matrix) position by experiments.

We propose, a new process capability index Cpsk(WV) applying the weighted variance control charting method for non-normally distributed. The main idea of the weighted variance method(WVM) is to divide a skewed or asymmetric distribution into two normal distributions from its mean to create two new distributions which have the same mean but different standard deviations. In this paper we propose an example, a distributions generated from the Johnson family of distributions, to demonstrate how the weighted variance-based process capability indices perform in comparison with another two non-normal methods, namely the Clements and the Wright methods. This example shows that the weighted valiance-based indices are more consistent than the other two methods in terms of sensitivity to departure to the process mean/median from the target value for non-normal processes. Second method show using the percentage nonconforming by the Pearson, Johnson and Burr systems. This example shows a little difference between the Pearson system and Burr system, but Johnson system underestimated than the two systems for process capability.

The main objective of this paper is to propose a measure of evaluation for non-normal process capability. If a process is not normally distributed, but normal-based techniques are used, serious errors can result. Our approach to solve this problem is that the Pearson system, the Johnson system, and the Burr system are selected for estimating a measure of process capability using the percentage nonconforming. In this paper, we found that the Pearson system and the Johnson system were a conparatively reasonable methods to calculate out of specification by example.

This paper considers an algorithm using Johnson transformation to calculate process capability index for non-normal distribution. Johnson transformation is well known as one of methods transforming the data with non-normal distribution to normal data.