Philippe Castagliola

An EWMA chart for monitoring the process standard deviation when parameters are estimated

The EWMA chart for the standard deviation is a useful tool for monitoring the variability of a process quality characteristic. The performance of this chart is usually evaluated under the assumption of known parameters. However, in practice, process parameters are estimated from an in-control Phase I data set. A modified EWMA control chart is proposed for monitoring the standard deviation when the parameters are estimated. The Run Length properties of this chart are studied and its performance is evaluated by comparing it with the same chart but with process parameters assumed known.

The synthetic [Xbar] chart with estimated parameters

A synthetic [Xbar] chart consists of an integration of a Shewhart [Xbar] chart and a conforming run length chart. This type of chart has been extensively used to detect a process mean shift under the assumption of known process parameters. However, in practice, the process parameters are rarely known and are usually estimated from an in-control Phase I data set. The goals of this article are to (i) evaluate (using a Markov chain model) the performances of the synthetic [Xbar] chart when the process parameters are estimated; (ii) compare it with the case where the process parameters are assumed known to demonstrate that these performances are quite different when the number of samples used during Phase I is small; and (iii) suggest guidelines concerning the choice of the number of Phase I samples and to provide new optimal constants, especially dedicated to the number of samples used in practice.

A synthetic double sampling control chart for the process mean

This article proposes a synthetic double sampling chart that integrates the Double Sampling (DS) chart and the conforming run length chart. The proposed procedure offers performance improvements in terms of the zero-state Average Run Length (ARL) and Average Number of Observations to Sample (ANOS). When the size of a mean shift δ (given in terms of the number of standard deviation units) is small (i.e., between 0.4 and 0.6) and the mean sample size n= 5, the proposed procedure reduces the out-of-control ARL and ANOS values by nearly half, compared with both the synthetic and DS charts. In terms of detection ability versus the Exponentially Weighted Moving Average (EWMA) chart, the synthetic DS chart is superior to the synthetic or even the DS chart, as the former outperforms the EWMA chart for a larger range of δ values compared to the latter. The proposed procedure generally outperforms the EWMA chart in the detection of a mean shift when δ is larger than 0.5 and n= 5 or 10. Although the proposed procedure is less sensitive than the EWMA chart when δ is smaller than 0.5, this may not be a setback as it is usually not desirable, from a practical viewpoint, to signal very small shifts in the process to avoid too frequent process interruptions. Instead, under such circumstances, it is better to leave the process undisturbed.

Monitoring the Coefficient of Variation Using EWMA Charts

The coefficient of variation (CV) is a quality characteristic that has several applications in applied statistics and is receiving increasing attention in quality control. A few papers have proposed control charts that monitor this normalized measure of dispersion. This paper suggests a new method to monitor the CV by means of two one-sided EWMA charts of the coefficient of variation squared γ2. Tables are provided for the statistical properties of the EWMA-γ2 when the shift size is deterministic or unknown. An example illustrates the use of these charts on real data gathered from a metal sintering process.

Effect of measurement error and autocorrelation on the X¯ chart

Measurement error and autocorrelation often exist in quality control applications. Both have an adverse effect on the X¯ charts performance. To counteract the undesired effect of autocorrelation, we build-up the samples with non-neighbouring items, according to the time they were produced. To counteract the undesired effect of measurement error, we measure the quality characteristic of each item of the sample several times. The charts performance is assessed when multiple measurements are applied and the samples are built by taking one item from the production line and skipping one, two or more before selecting the next.

The Variable Sample Size X¯ Chart with Estimated Parameters

The VSS chart, dedicated to the detection of small to moderate mean shifts in the process, has been investigated by several researchers under the assumption of known process parameters. In practice, the process parameters are rarely known and are usually estimated from an in-control Phase I data set. In this paper, we evaluate the (run length) performances of the VSS chart when the process parameters are estimated, we compare them in the case where the process parameters are assumed known and we propose specific optimal control chart parameters taking the number of Phase I samples into account.

A combined synthetic&X chart for monitoring the process mean

The Shewhart X chart (or X chart) is widely used to monitor the mean of a quality characteristic x. This chart decides the process status based on the magnitude of the sample mean x and is effective for detecting large mean shifts. The synthetic chart is also a Shewhart type chart for monitoring the process mean, but it utilises the information about the time interval between two nonconforming samples. Here a sample is nonconforming if its x value falls beyond the predetermined warning limits. Unlike the X chart, the synthetic chart is more powerful to detect small shifts. The applications of the X and synthetic charts cover a wide variety of manufacturing processes and production lines, e.g., the monitoring of the mean values of the inside diameter of a piston-ring, the viscosity of aircraft paint, the resistivity of silicon wafers. This article proposes a combined scheme, the Syn-X chart, that comprises a synthetic chart and an X chart. The results of the performance studies show that the Syn-X chart always outperforms the individual X chart and synthetic chart under different conditions. It is more effective than the X chart and synthetic chart by 47% and 20%, respectively, over the wide range of mean shift values in different experiment runs.

Capability Indices Dedicated to the Two Quality Characteristics Case

Abstract The aim of this paper is to define two new capability indices BCP and BCPK dedicated to two quality characteristics, assuming a bivariate normal distribution and a rectangular tolerance region. These new capability indices are based on the computation of the theoretical proportion of non-conforming products over convex polygons. This computation is achieved by a new method of integration based on Green’s formula. The efficiency of the proposed capability indices is demonstrated by comparing our approach with others proposed previously, on simulated and real world industrial examples.

THE EXACT RUN LENGTH DISTRIBUTION AND DESIGN OF THE S2 CHART WHEN THE IN-CONTROL VARIANCE IS ESTIMATED

When monitoring the process variability, it is a common practice that a Phase I data set is used to estimate the unknown in-control process standard deviation σ0 or variance to set up the control limits, then monitoring proceeds. Once the process is considered to be in-control, the estimated control limits are assumed as fixed. This practice ignores the effect of estimating the unknown in-control process variance . In this paper, we derive the exact run length distribution of the S2 control chart when the in-control process variance is estimated and find that m = 200 or more Phase I samples are needed to neglect the effect of using estimated control limits. New control limits when m is small are also derived.

On t and EWMA t charts for monitoring changes in the process mean

The performance of an X-bar chart is usually studied under the assumption that the process standard deviation is well estimated and does not change. This is, of course, not always the case in practice. We find that X-bar charts are not robust against errors in estimating the process standard deviation or changing standard deviation. In this paper we discuss the use of a t chart and an exponentially weighted moving average (EWMA) t chart to monitor the process mean. We determine the optimal control limits for the EWMA t chart and show that this chart has the desired robustness property. Copyright