Process Capability Indices Based on the Highest Density Interval

Yang, Jun; Gang, Ting-Ting; Cheng, Yaqi; Xie, Min

doi:10.1002/qre.1665

Cited by 4 publications

(3 citation statements)

References 28 publications

(43 reference statements)

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“…and Peng gave a unified form of PCIs named C p ( u , v ). Yang et al . modified the natural tolerance and proposed indices based on the highest density interval.…”

Section: Introductionmentioning

confidence: 99%

Process Capability Analysis via Continuous Ranked Probability Score

Shi

Lin

2016

Quality & Reliability Eng

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Process capability analysis is an important aspect of quality control. Various process capability indices are proposed when the distribution is normal. For non-normal cases, percentile and yield-based indices have been introduced. These two methods use partial features of a process distribution, such as key percentiles and the proportion of non-conforming (PNC) to estimate the process capability. However, these local features may not reflect the uniformity of a process appropriately when the distribution is non-normal. In this paper, continuous ranked probability score (CRPS) is introduced to process capability analysis and a CRPS-based approach is proposed. This method can assess the dispersion of process variation across the overall distribution and is applicable to any continuous distribution. An example and simulations show that CRPS-based indices are more stable and accurate indicators of process capability than the existing indices in reflecting the degree of process fluctuation.In the same way, C pl(s) = C pl . Then C pk(s) = min{C pu , C pl } = C pk .

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“…and Peng gave a unified form of PCIs named C p ( u , v ). Yang et al . modified the natural tolerance and proposed indices based on the highest density interval.…”

Section: Introductionmentioning

confidence: 99%

Process Capability Analysis via Continuous Ranked Probability Score

Shi

Lin

2016

Quality & Reliability Eng

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“…Yang et al [34] showed that the interval dened by 0.135 and 99.865 percentiles may not include the highest probability density interval when dealing with non-symmetric distributions. Yang et al [34] suggest describing the PR for non-normal distributions as the interval [P h1 ; P h2 ] that satises equation 15 and f(P h1 ) = f(P h2 ), where f(x) is the probability density function. Figure 7 illustrates the intervals dened by Zwick [33] and Yang et al [34] with a non-symmetric probability density function.…”

Section: Pcis For Non-normal Measuresmentioning

confidence: 99%

“…Yang et al [34] suggest describing the PR for non-normal distributions as the interval [P h1 ; P h2 ] that satises equation 15 and f(P h1 ) = f(P h2 ), where f(x) is the probability density function. Figure 7 illustrates the intervals dened by Zwick [33] and Yang et al [34] with a non-symmetric probability density function.…”

Section: Pcis For Non-normal Measuresmentioning

confidence: 99%

A review of univariate and multivariate process capability indices

de-Felipe

Benedito

2017

Int J Adv Manuf Technol

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This paper oers a review of univariate and multivariate Process Capability Indices (PCIs). PCIs are used in the industry to quantify how well a process can meet customer requirements. Univariate PCIs describe the capability of one single product characteristic. Multivariate PCIs deal with the multivariate case in which the measures of all multiple product characteristics must be within specication limits to be conforming. When analyzing the capability of processes, decision makers of the industry may choose one PCI among all the PCIs existing in the literature, depending on dierent capability criteria. The aim of the review is to describe, cluster and discuss univariate and multivariate PCIs. This review may help researchers and decision makers to identify univariate and multivariate PCIs that can be used when monitoring univariate and multivariate production processes. On the one hand, the authors of this article suggest using PCIs obtained through the alternative denition for the C pk index when analyzing the capability of production processes, in which the estimation of the proportion of nonconforming parts is rated as crucial. On the other hand, all other multivariate PCIs presented in the literature can be applicable in capability analysis of production processes in which a direct relation to the proportion of nonconforming parts is not needed.

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Monitoring high complex production processes using process capability indices

de-Felipe

Benedito

2017

Int J Adv Manuf Technol

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The increasing demand and the globalization of the market are leading to increasing levels of quality in production processes, and thus, nowadays multiple product characteristics must be tested because they are considered critical. In this context, decision makers are forced to interpret a huge amount of quality indicators, when monitoring production processes. This fact leads to a misunderstanding as a result of information overload. The aim of this paper is to help practitioners when monitoring the capability of processes with a huge amount of product characteristics. We propose a methodology that reduces the amount of data in capability analysis by structuring hierarchically the multiple quality indicators obtained in the quality tests. The proposed methodology may help practitioners and decision makers of the industry in three aspects of statistical process monitoring: To identify the part of a complex production process that presents capability problems; to detect worsening over the time in multivariate production processes; and to compare similar production processes. Some illustrative examples based on dierent kinds of production processes are discussed in order to illustrate the methodology. A case of study based on a real production process of the automotive industry is analyzed using the proposed methodology. We conclude that the proposed methodology reduces the necessary amount of data in capability analysis; and thus, that it provides an added value of great interest for managers and decision makers.

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Process Capability Indices Based on the Highest Density Interval

Cited by 4 publications

References 28 publications

Process Capability Analysis via Continuous Ranked Probability Score

Process Capability Analysis via Continuous Ranked Probability Score

A review of univariate and multivariate process capability indices

Monitoring high complex production processes using process capability indices

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