• International Journal of Quality Innovation
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• v.3 no.2
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• pp.74-83
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• 2002

Process capability indices as an important kind of indices are intended to provide single-number assessments of the inherent process capability to meet specification limits on quality characteristic(5) of interest. In this paper the condition for the application of process capability indices is analyzed. On the basis of process capability indices, dynamic process capability indices as a new kind of indices to show the current process capability are discussed and the condition for the application of dynamic process capability indices is exhibited. Comparison between process capability index and dynamic process capability index and comparison between $D_p$ and $D_pk$ are made and the conclusions provide the approach for process control. According to the requirement of process capability indices provided by customer, quality control based on process capability indices dynamic process capability indices is discussed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.347-356
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• 1998

For assessing the capability of a process, the quantification of process location and variation is central to understanding the quality of units produced from the manufacturing process. Conventional process capability indices is insufficient to drive out the information for process condition, furthermore it is very difficult to evaluate the process capability accurately when the target value is not consistent with the center of specification, and/or the shape of distribution is changed, but the process incapability indices is enable to provide more detailed information to evaluate the process capability by dividing information about the process mean and variance. In this paper, we have a brief review and comparison about these indices, provide an understanding of the relationships between the process capability indices and the incapability indices. And we explore the strengths and weakness of these indices as they apply to normally distributed process, and to examine the effect that non-normality has on these indices.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.106-114
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• 2019

Recently, the manufacturing process system in the industrial field has become more and more complex and has been influenced by many and various factors. Moreover, these factors have the dependent correlation rather than independent of each other. Therefore, the statistical analysis has been extended from the univariate method to the multivariate method. The process capability indices have been widely used as statistical tools to assess the manufacturing process performance. Especially, the multivariate process indices need to be enhanced with more useful information and extensive application in the recent industrial fields. The various multivariate process capability indices have been studying by many researchers in recent years. Hence, the purpose of the study is to compare the useful and various multivariate process capability indices through the simulation. Among them, we compare the useful models of several multivariate process capability indices such as $MC_{pm}$, $MC^+_{pm}$ and $MC_{pl}$. These multivariate process capability indices are incorporates both the process variation and the process deviation from target or consider the expected loss caused by the process deviation from target. Through the computational examples, we compare these process capability indices and discuss their usefulness and effectiveness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.4
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• pp.182-189
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• 2007

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 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 process deviation from target and $Cpm^+$ considers economic loss for the process deviation from target In the previous studies, only one designated location on each part is measured. System process capability index even though in single process, multiple measurement locations on each part are required to calculate the reliable process capability. In manufacturing industry, there is growing interest in quantitative measures of process variation under multivariate quality characteristics. The multivariate process capability index incorporates both the process variation and the process deviation from target or considers expected loss caused by the process deviation from target. In this paper, we compare various process capability indices and propose the application method of PCIs.

• Journal of Korean Society for Quality Management
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• v.26 no.1
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• pp.88-98
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• 1998

Process capability indices are used to determine whether a production process is capable of producing items within a specified tolerance. Using conditional distribution, we study some process capability indices ${\hat{C}}_{Gp}$, ${\hat{C}}_{Gpk}$, ${\hat{C}}_{Gpm}$ under conjugate prior distribution. We consider some process capability indices with Gibbs sampling method. Also, we examine some small sample properties related to these estimaters by some simulations.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.3
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• pp.349-355
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• 2013

Recently, there is an increasing need to produce more precise products with small deviations from defined target values. Machine capability is the ability of a machine tool to produce parts within a tolerance interval. Capability indices are a statistical way of describing how well a product is machined compared to defined target values and tolerances. Today, there is no standardized way to acquire a machine capability value. This paper describes a method for evaluating machine capability indices in machining centers. After the machining of specimens, the straightness, roundness, and positioning accuracy were measured by using CMM (coordinate measuring machine). These measured values and defined tolerances were used to evaluate the machine capability indices. It will be useful for the industry to have standardized ways to choose and calculate machine capability indices.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.2
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• pp.1-10
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• 2002

Process capability indices are widely used in industries and quality assurance system. In past years, process capability analysis have been used to characterize process performance on the basis of univariate quality characteristics. However, in actual manufacturing industrial, statistical process control (SPC) often entails characterizing or assessing processes or products based on more than one engineering specification or quality characteristic. 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 $MC_{pm}^+$ 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.6
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• pp.17-22
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• 2002

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 those basic indices and other generalization in measuring process capability. We compared an estimation methods by Clements with based on sample percentiles WVM to calculate the proposed generalization as an example The results indicated that Clements method is more accurate than percentile method, WVM in measuring process capability But the calculations of percentile method are easy to understand, straightforward to apply, and show be valuable used for applications.

• Proceedings of the Safety Management and Science Conference
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• 2005.05a
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• pp.503-521
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• 2005

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 $MC_{pm}^{++}$ 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,

• Proceedings of the Safety Management and Science Conference
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• 2004.11a
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• pp.69-79
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• 2004

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 $MC_{pm}^{++}$ 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.