Search results

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(s) 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 ciscussed.

The purpose of this paper is propose a methodology for monitoring industrial processes that cannot be stabilized, but are nevertheless capable.

The proposed procedure uses the C_P(u,v) family of capability indices proposed by Vännman (including the indices C_PK, C_PM, C_PMK) combined with one‐sided two‐out‐of‐three and three‐out‐of‐four run rules strategies.

This paper introduces a new strategy, where capability indices are monitored in place of the classical sample statistics like the mean, median, standard deviation or range.

When doing a capability analysis it is recommended to first check that the process is stable, e.g. by using control charts. However, there are occasions when a process cannot be stabilized, but is nevertheless capable. Then the classical control charts fail to efficiently monitor the process position and variability. The approach suggested in this paper overcomes this problem.

The experimental results presented in this paper demonstrate how the new proposed approach efficiently monitors capable processes by detecting decreases or increases of capability level.

The process capability indices have been widely used to measure process capability and performance. In this paper, we proposed a new process capability index which is based on an actual dollar loss by defects. The new index is similar to the Taguchi’s loss function and fully incorporates the distribution of quality attribute in a process. The strength of the index is to apply itself to non‐normal or asymmetric distributions. Numerical examples were presented to show superiority of the new index against Cp, Cpk, and Cpm which are the most widely used process capability indices.

The purpose of this paper is to explore the relationship between multivariate process capability indices and loss functions for both nominal‐the‐best and smaller‐the‐better cases, so the likelihood and consequences resulting from the nonconforming of a manufacturing process or an environmental system can be evaluated simultaneously.

In this paper, the authors present a new approach of correlated risk assessment by linking the multiple process capability indices and loss functions, in which the multivariate process capability indices and multivariate loss functions describe the likelihood and consequences as a result of nonconformities in multivariate manufacturing or environmental system, respectively. Then, the associated relationship equations are developed using multivariate methods. Moreover, a step‐by‐step procedure is provided to facilitate the implementation of the correlated risk assessment.

Given the multivariate process capability indices, the authors show that the expected loss can be estimated by developed relationship equations and two numerical examples are also given, to demonstrate how the correlated manufacturing and environmental risks can be properly assessed by linking the multivariate process capability indices and multivariate loss function.

The risk information of likelihood and expected loss, classified in the four planning zones of a strategic planning matrix, provides practising managers and engineers with a decision‐making tool for prioritizing their quality improvement projects when conducting risk assessment for any multivariate process or environmental system.

Once the existing quality/environmental problems and their Key Performance Indicators are identified, one may conduct risk assessment by applying the relationship equations to evaluate the impact of correlated risk on manufacturing processes or multiple environmental emissions inside company; this can lead to the direction of continuous improvement for any industry.

In the past few years, several capability indices have been developed for evaluating the performance of multivariate manufacturing processes under the normality assumption. However, this assumption may not be true in most practical situations. Thus, the purpose of this paper is to develop new capability indices for evaluating the performance of multivariate processes subject to non-normal distributions.

In this paper, the authors propose three non-normal multivariate process capability indices (MPCIs) RNMC _p , RNMC _pm and RNMC _pu by relieving the normality assumption. Using the two normal MPCIs proposed by Pan and Lee, a weighted standard deviation method (WSD) is used to modify the NMC _p and NMC _pm indices for the-nominal-the-best case. Then the WSD method is applied to modify the multivariate ND index established by Niverthi and Dey for the-smaller-the-better case.

A simulation study compares the performance of the various multivariate indices. Simulation results show that the actual non-conforming rates can be correctly reflected by the proposed capability indices. The numerical example further demonstrates that the actual quality performance of a non-normal multivariate process can properly reflected by the proposed capability indices.

Process capability index is an important SPC tool for measuring the process performance. If the non-normal process data are mistreated as a normal one, it will result in an improper decision and thereby lead to an unnecessary quality loss. The new indices can provide practicing managers and engineers with a better decision-making tool for correctly measuring the performance for any multivariate process or environmental system.

Once the existing multivariate quality/environmental problems and their Key Performance Indicators are identified, one may apply the new capability indices to evaluate the performance of various multivariate processes subject to non-normal distributions.

Assert that capability indices quantify process improvement in a simple way and, when used correctly, provide relevant benchmarks. Considers it important that managers fully understand the power and limitations of this quality tool. Asserts the process is on target when Cp and Cpk are equal. Contends that the observed differences are due to sampling error and that any capability index is simply an estimate of an unknown value. Concludes that the measurement of process capability and the assessment on internal and external suppliers performance using indices, is now widespread. Despite any problems, process capability indices are preferable to many other measures of process or supplier performance.

When the process probability distribution is non‐normal or is unknown, the process mean and standard deviation may not properly describe the distribution’s shape. Consequently, the traditional process capability indices (PCI) C_p, C_pk, C_pm and C_pmk cannot express the actual process capability. This paper presents a procedure to construct lower confidence limits for PCIs when the process distribution is unknown. First, the order statistics are utilized to find the estimators of C_p, C_pk, C_pm and C_pmk. Bootstrap simulation method is then utilized to construct the lower confidence limits of PCIs, thereby allowing the process’s capability to be evaluated. A numerical example demonstrates the effectiveness of the proposed procedure.

The process capability indices are used to assess the ability of a process to meet the present specification limits. These indices provide an easily understood qualification of a process performance. A fixed value for an ongoing process capability index is generally used. Minimization of statistical process control costs can be achieved by selecting an optimal process capability index. Presents a procedure to find an optimal ongoing process capability index.

The aim of this paper is to consider the effect of gauge measurement capability on the multivariate process capability index (MC_p).

With respect to measurement capability, the paper investigates the statistical properties of the estimated MC_p and considers the effect of gauge measurement capability on the lower confidence bound, hypothesis testing, critical value and power of testing for MC_p at the mentioned state.

The results show that gauge measurement capabilities will notably change the results of estimating and testing the process capability index.

The research would help quality experts to determine whether their processes meet the required capability, and to make more reliable decisions.

The purpose of this paper is to introduce a modified MC_p by considering the effect of gauge measurement error on the multivariate process capability index.

In this paper, the effect of measurement system on the quality characteristics appears as coefficient matrix A which changes the variance‐covariance matrix of quality characteristics ∑, into A ∑. In this case, the authors investigate the properties of multivariate index and present adjusted confidence intervals and critical values for capability testing purpose of this index.

The results show that the simplicity of the obtained index, calculating the true process capability by using empirical process capability and also computing the critical value and power of the process capability testing is simpler in this modified approach.

The results presented in this paper would help practitioners to determine their actual process capability and see if their processes meet the preset capability requirement, and then make reliable decisions.