Multi-attribute Control Charts Research Articles

Erratum: "Improved Alternated Charting Statistic Multi-Attribute Control Chart to Identify Process Deterioration"

International Journal of Reliability, Quality and Safety EngineeringVol. 29, No. 01, 2192001 (2022) ErratumFree AccessErratum: "Improved Alternated Charting Statistic Multi-Attribute Control Chart to Identify Process Deterioration"is erratum ofImproved Alternated Charting Statistic Multi-Attribute Control Chart to Identify Process DeteriorationM. P. Gadre and Savita S. VenegurkarM. P. GadreCorresponding author.Department of Statistics, Savitribai Phule Pune University, Pune, Maharashtra, 411007, India and Savita S. VenegurkarRamkrishna More Arts, Commerce and Science College, Akurdi, Pune, Maharashtra, 411044, Indiahttps://doi.org/10.1142/S0218539321920017Cited by:0 Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail [International Journal of Reliability, Quality and Safety Engineering, Vol. 28, No. 4, Article ID: 2150023 (2021) DOI: 10.1142/S0218539321500236]Published: 11 September 2021 FiguresReferencesRelatedDetailsRelated articlesImproved Alternated Charting Statistic Multi-Attribute Control Chart to Identify Process Deterioration26 Mar 2021International Journal of Reliability, Quality and Safety Engineering Recommended Vol. 29, No. 01 Metrics Downloaded 29 times History PDF download

Improved Alternated Charting Statistic Multi-Attribute Control Chart to Identify Process Deterioration

Leoni and Costa [R. C. Leoni and A. F. B. Costa, Monitoring bivariate and trivariate mean vectors with a Shewhart chart, Quality Reliab. Eng. Int. 33 (2017) 2035–2042] developed the ‘Alternated Charting Statistic’ (ACS) control chart to control the mean vector of a bivariate and trivariate processes and verified that the chart is easier to operate and faster than the classical Hotelling’s [Formula: see text] chart in signaling changes in the mean vector. Gadre and Rattihalli [M. P. Gadre and R. N. Rattihalli, Some group inspection based multi-attribute control charts to identify process deterioration, Econ. Quality Control 20(2) (2005) 191–204] developed some multi-attribute control charts for process deterioration. For bivariate processes, Gadre and Venegurkar [M. P. Gadre and S. S. Venegurkar, Multi-attribute control chart to identify process deterioration, Submitted to Journal of Indian Statistical Association (JISA) (*) after a minor revision] developed ‘Alternated Charting Statistic Multi Attribute control chart for number of defectives in a sample of size [Formula: see text]’ (ACS-MA-np) chart. For multi-attribute processes, here, we propose a control chart by using the ACS along with the test procedure related to the exact distributions of the number of defectives in multi-attribute cases. It is illustrated that the developed control chart is efficient to the related available control charts.

A Multi-attribute Control Chart for Monitoring Friction Stir Welding Process Considering Small Sample Sizes

Abstract Often, welding processes used in the industry affect the mechanical properties of materials and quality of a manufactured product. There is, however, an alternative process named Friction Stir Welding (FSW), which is an solid state welding process developed to weld light alloys without compromising their mechanical properties. It is of interest to monitor the performance of FSW process to detect loss of quality. In practice, superficial and internal defects can be found; they can be identified through simple visual inspection and through visual recognition on destructive testing respectively, both procedures represent inspection by attributes. Therefore a multi-attribute control chart is assessed to monitor the process. Commonly, multi-attribute control charts involve high sampling rates to ensure accurate monitoring. In this paper, a multi-attribute control chart is proposed, considering the use of empirical control limits, instead of the theoretical ones, in order to improve its accuracy and lessen the small sample sizes effect. The performance of proposed approaches is analyzed by means of Monte Carlo simulation. The results suggest that the performance of the empirical designs is better than the theoretical ones in all tested cases. Finally, the results of monitoring FSW process data are detailed.

Identifying the time of a step change in bivariate binomial processes

Control charts are one of the most applicable tools in statistical process control. The time in which the control chart signals an out-of-control alarm is not the actual time in which the change has occurred. In other words, control chart detects the change with some delay. The actual time of the change taking place is referred to as change point. Change point estimation facilitates the identification of cause(s) of change and reduces the corresponding time and cost. There are many processes in which the control of two correlated attributes is necessary. Multiattribute control charts are used in such cases due to correlation between attributes. In this paper, two methods including maximum likelihood estimation (MLE) and clustering are proposed for estimating change point in nonconformity ratio vector of a process with bivariate binomial distribution. Also, the performances of these methods are evaluated and compared by Monte Carlo simulations.

Multiatributte Double Sampling Control Chart

In recent years, multiattribute control charts have received an increasing attention. These charts are able to monitor two or more attributes in the same chart. In addition, there are many applications of multiatributte control charts in a wide variety of manufacturing processes and services. In this article, a multiattribute double sampling (DS D2) control chart is proposed. Double sampling is a methodology used to improve the efficiency of a control chart to detect quality issues without increase the sampling. Results of comparative studies via simulation indicate that the proposed control chart significantly outperforms in most of the supposed sceneries, in terms of the Average Run Length.

Monitoring correlated variable and attribute quality characteristics based on NORTA inverse technique

In some statistical process control applications, quality of a process or a product is characterised and monitored based on a variable or an attribute quality characteristic. However, sometimes a vector of variables or attributes describes the quality of a process. Likewise, in some cases, quality of a process or a product is characterised by a combination of several correlated variables and attributes. To the best of our knowledge, there is no method in monitoring multivariate-attribute processes in spite of numerous studies in multivariate and multi-attribute control charts. This paper describes a method to monitor a process with multiple correlated variable and attribute quality characteristics. In the proposed method, we utilise NORTA inverse technique to design a scheme in monitoring multivariate-attribute processes. First, NORTA inverse method transforms the data to a multivariate normal distribution, and then we apply multivariate control charts such as T2 and MEWMA for transformed data. The performance of the proposed method considering both T2 and MEWMA charts is investigated by using simulation studies in terms of average run length criterion.

Monotonic change-point estimation of multivariate Poisson processes using a multi-attribute control chart and MLE

In this paper, a new multi-attribute control chart is initially proposed to monitor multi-attribute processes based on a transformation technique. Then, the maximum likelihood estimator of a multivariate Poisson process change point is derived for unknown changes that are assumed to belong to a family of monotonic changes. Using extensive simulation experiments, the performance of the proposed change-point estimator is compared to the ones derived for step changes and linear-trend disturbances, when the true change types are step change, linear trends and multiple-step changes. We show when the type of the change is not known a priori, the proposed estimator is an appropriate choice, since it accurately estimates the true time of the process changes, regardless of change type, shift magnitudes and process dimension.

An Efficient Novel Compensatory Multi-attribute Control Chart for Correlated Multinomial Processes

Monitoring multi-attribute processes is an important issue in many quality control environments. Almost all the priory proposed control charts utilize equal weights for each Attribute Quality Characteristics (AQCs). In such condition, there is no priority among AQCs. But in real-world, compensatory may exist. Hence due to some applied reasons such as function or efficiency, unequal weights for each AQC are possible. This study proposed a novel efficient control chart for simultaneous monitoring of weighted AQC when data expressed by linguistic terms. Correspondingly a new procedure to interpret out-of-control signals is presented. Performance and comparison advantage of the proposed control chart is measured in terms of Average Run Length (ARL) using a real case which priory was expressed. Consequences displayed that considering weight could efficiently extend the prior research for practical circumstancese.

The economic design of multivariate binomial EWMA VSSI control charts

Since multi-attribute control charts have received little attention compared with multivariate variable control charts, this research is concerned with developing a new methodology to employ the multivariate exponentially weighted moving average (MEWMA) charts for m-attribute binomial processes; the attributes being the number of nonconforming items. Moreover, since the variable sample size and sampling interval (VSSI) MEWMA charts detect small process mean shifts faster than the traditional MEWMA, an economic design of the VSSI MEWMA chart is proposed to obtain the optimum design parameters of the chart. The sample size, the sampling interval, and the warning/action limit coefficients are obtained using a genetic algorithm such that the expected total cost per hour is minimized. At the end, a sensitivity analysis has been carried out to investigate the effects of the cost and the model parameters on the solution of the economic design of the VSSI MEWMA chart.

Development of a multiattribute synthetic-np chart

Over the last few decades, multiattribute control charts have been widely recommended in practice. They outperform the simultaneous uniattribute charts for monitoring multiattribute processes in many applications. Jolayemi [A statistical model for the design of multiattribute control charts. Indian J Stat. 1999;61:351–365] developed a statistical model for the design of a multiattribute np (Mnp) chart. Based on this model, a multiattribute synthetic (MSyn) chart is proposed in this article. Furthermore, the main features of the MSyn chart and Mnp chart are integrated to build a multiattribute Syn-np (MSyn-np) chart. The results of the comparative studies indicate that the new MSyn-np chart significantly outperforms the Mnp chart and MSyn chart by 83% and 27%, respectively, in terms of the average number of defectives over a wide range of process shifts under different circumstances.

Estimating the change point of the parameter vector of multivariate Poisson processes monitored by a multi-attribute T 2 control chart

When a control chart signals an out-of-control condition, knowing when the process has really changed (the change point) accelerates the identification of the source of special causes and makes the corrective measures to be taken sooner. In this paper, a new multi-attribute T2 control chart based on two transformation methods is initially proposed to monitor the parameter vector of multi-attribute Poisson processes. Then, the maximum likelihood estimators (MLE) of the process change point designed for both linear trend and step change disturbances are derived. Next, using Monte Carlo simulation, we show the performances of the proposed estimators are satisfactory. Finally, through performance comparisons, we conclude the MLE of the change point designed for linear trends outperforms the MLE designed for step changes when a linear trend disturbance is present and conversely, the MLE of the change point designed for step changes outperforms the MLE designed for linear trend disturbances when the real change type is step change.

Chi-squared control chart for multiple attributes

In recent years, attribute control charts have been increasingly adopted in manufacturing processes and service sectors for monitoring the quality characteristics that cannot be numerically measured. The multiattribute control charts have been proved to be more effective than the simultaneous multiple individual charts for monitoring multiattribute processes. In this paper, a new multiattribute chi-squared (χ2) chart is proposed to monitor the processes in which more than one type of defects exist on the non-conforming item. Some practical examples are given to illustrate the effectiveness of the new chart in monitoring the multiattribute processes compared with that of the individual p charts and other multiattribute charts.

Review of multinomial and multiattribute quality control charts

AbstractAttribute control charts are very useful nowadays for monitoring processes where the quality characteristics cannot be measured in a continuous scale, which may be manufacturing processes from industrial settings, health‐care processes or processes from service industries and environments of non‐manufacturing quality‐improvement efforts. Many of the above cases, however, involve the monitoring of multiple attributes simultaneously, thus leading to the case of multinomial and multiattribute quality control methods, which are better than the simultaneous use of multiple uni‐attribute methods. In this study, an attempt to review the research previously conducted on multiattribute quality control is made in order to help the interested researchers and practitioners get informed about the references on the relevant research in this field, regarding the design, performance and applications of multiattribute control charts. Copyright © 2009 John Wiley & Sons, Ltd.

Multi-attribute unit and group runs control chart to identify the process deterioration

Gadre and Rattihalli [Gadre, M.P. and Rattihalli, R.N., 2005a, A unit and group runs based chart to identify increases in fraction nonconforming. Journal of Quality Technology, 37, 199–209.] proposed a control chart called the unit and group runs (UGR) control chart to identify increases in fraction non-conforming. In this article, the concept of UGR chart is extended to the multi-attribute case to detect the process deterioration. It is illustrated that in multi-attribute cases also, the UGR chart gives a remarkable reduction in out-of-control average time to signal when compared with the multi-attribute np chart, the multi-attribute synthetic chart and the multi-attribute group runs chart recently developed by Gadre and Rattihalli [Gadre, M.P. and Rattihalli, R.N., 2005b, Some group inspection based multi-attribute control charts. Economic Quality Control, 20, 191–204.]. The steady state performance of the multi-attribute UGR chart is also excellent. The procedure of identifying the attributes causing signal is also described and illustrated.

Bootstrap method approach in designing multi-attribute control charts

In a production process, when the quality of a product depends on more than one correlated characteristic, multivariate quality control techniques are used. Although multivariate statistical process control is receiving increased attention in the literature, little work has been done to deal with multi-attribute processes. In monitoring the quality of a product or process in multi-attribute environments in which the attributes are correlated, several issues arise. For example, a high number of false alarms (type I error) occur and the probability of not detecting defects (type II error) increases when the process is monitored by a set of independent uni-attribute control charts. In this paper, to overcome these problems, first we develop a new methodology to derive control limits on the attributes based on the bootstrap method in which we build simultaneous confidence intervals on the attributes. Then, based upon the in-control and out-of-control average run length criteria we investigate the performance of the proposed method and compare it with the ones from the Bonferroni and Sidak’s procedure using simulation. The results of the simulation study show that the proposed method performs better than the other two methods. At the end, we compare the bootstrap method with the T2 control chart for attributes.

LIKELIHOOD RATIO BASED MULTI-ATTRIBUTE CONTROL CHART

In a production process, when quality of the product depends on more than one characteristic, 'Multivariate Quality Control' (MQC) techniques are efficiently used. Many MQC techniques have been developed to control the multivariate variable processes, but no much work has been reported to control the multivariate attribute processes. In this article, to detect a change in the vector of fraction non-conforming, we develop 'Likelihood Ratio based Multi-Attribute Control Chart' (LR-MACC) using the exact joint distribution and the LR-test under multinomial setup. It is verified that, in some situations, LR-MACC is superior to the MNP chart proposed by Lu et al.7 When MACC gives a signal, the attributes responsible are not readily identifiable. Therefore, a procedure to detect the responsible attributes is also developed.

Some Group Inspection Based Multi-Attribute Control Charts to Identify Process Deterioration

In a production process, when quality of the product depends on more than one characteristic, ‘Multivariate Quality Control’ (MQC) techniques are used. Many MQC techniques have been developed to control multivariate variable processes, but not much work has been reported on multivariate attribute processes. In this article, we propose some group inspection based multi-attribute control charts to identify process deterioration. The charts proposed are the ‘Multi-Attribute np’ (MA – np) chart, the ‘Multi-Attribute Synthetic’ (MA – Syn) chart and the ‘Multi-Attribute Group Runs’ (MA – GR) chart. The charts are developed by using MP-test based on the exact distribution. It is numerically illustrated that, MA – GR chart performs better than the other two charts. Also in steady state MA – GR chart performs better than MA – np and MA – Syn control charts. A procedure of identifying the attributes causing signal is also proposed.

An optimal design of multiattribute control charts for processes subject to a multiplicity of assignable causes

We have developed a model for the optimal design of multiattribute control charts (MACCs) for processes subject to multiple assignable causes. The development of the model is based on the J approximation (J.K. Jolayemi, Comput. Stat. Data Anal., 18, 1994, pp. 403–417; J.K. Jolayemi and J.N. Berrettoni, Appl. Math. Comput. 32 (1) (1989) pp. 1–16) and on the modification and extension of Gibra's model for the design of attribute control charts (I.N. Gibra, J. Quality Technol. 13 (2) (1981) pp. 93–99). A computational procedure for optimizing the model is presented. Some numerical examples are given to illustrate the model and the computational procedure. The examples show that the MACC and its design parameters are very sensitive to changes in the values of the input parameters. The model does not require any approximation by the “matching” process (see I.N. Gibra, J. Quality Technol. 13 (2) (1981) pp. 93–99 for an example of the matching process), as an exact solution to it is very easy to obtain. The justifications for applying the J approximation (JA) are illustrated by numerical examples. The numerical examples show that JA is very accurate in determining sample sizes. The examples also show that unlike when used to compute convolution probability values, JA is indifferent to the differences in the values of the proportion defectives when applied for determining sample sizes – whatever the magnitudes of the differences.