CV Control Chart Using GWMA Technique

When the production run is short and process parameters change frequently, it is difficult to monitor the process using traditional control charts. In such a case, the coefficient of variation (CV) is very useful for monitoring the process variability. The CV control chart, however, is not sensitive at small shifts in the magnitude of CV. This study suggest the CV-GWMA(generally weighted moving average) control chart, combining the GWMA technique, which shows better performance than the EWMA(exponentially weighted moving average) or DEWMA(double exponentially weighted moving average) technique in detecting small shifts of the process. Through a performance evaluation, the proposed control chart showed more excellent performance than the existing CV-EWMA control chart or the CV-DEWMA control chart in detecting small shifts in CV.

Download Full-text

DEWMA Control Chart for the Coefficient of Variation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.201-203.1682 ◽

2011 ◽

Vol 201-203 ◽

pp. 1682-1688 ◽

Cited By ~ 7

Author(s):

Eui Pyo Hong ◽

Hae Woon Kang ◽

Chang Wook Kang

Keyword(s):

Control Chart ◽

Coefficient Of Variation ◽

Control Charts ◽

Moving Average ◽

Exponentially Weighted Moving Average ◽

Average Technique ◽

Small Shift ◽

Production Run ◽

Weighted Moving Average ◽

Exponentially Weighted

When the production run is short and process parameters change frequently, it is difficult to monitor the process using traditional control charts. In such a case, the coefficient of variation (CV) is very useful for monitoring the process variability. The CV control chart, however, is not sensitive at small shift in the magnitude of CV. The CV-EWMA (exponentially weighted moving average) control chart which was developed recently is effective in detecting a small shifts of CV. In this paper, we propose the CV-DEWMA control chart, combining the DEWMA (double exponentially weighted moving average) technique. We show that CV-DEWMA control chart perform better than CV-EWMA control chart in detecting small shifts when sample size n is larger than 5.

Download Full-text

Demerit-GWMA Control Chart for Demerit Statistics

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.156-157.413 ◽

2010 ◽

Vol 156-157 ◽

pp. 413-421

Author(s):

Hae Woon Kang ◽

Chang Wook Kang ◽

Jae Won Baik ◽

Sung Ho Nam

Keyword(s):

Control Chart ◽

Mobile Phones ◽

Moving Average ◽

Exponentially Weighted Moving Average ◽

Process Mean ◽

Average Technique ◽

Complex Products ◽

Ewma Control Chart ◽

Weighted Moving Average ◽

Exponentially Weighted

A classical Demerit control chart is used to monitor the process through which various types of defects in complex products, such as automobiles, computers, mobile phones, etc. are found in general. As a technique for rapidly detecting small shifts of the process mean in the control chart, the EWMA(exponentially weighted moving average) technique is very effective. This study suggested the Demerit-GWMA control chart, combining the GWMA(generally weighted moving average) technique, which shows better performance than EWMA technique in detecting small shifts of process mean, into the classical Demerit control chart, and evaluated its performance. Through the evaluation of its performance, it was found that the Demerit-GWMA control chart is more sensitive than both the classical Demerit control chart and the Demerit-EWMA control chart in detecting small shifts of process mean.

Download Full-text

Penerapan Peta Kendali Neutrosophic Exponentially Weighted Moving Average (NEWMA) X dalam Monitoring Rata-Rata Proses Ketebalan Kaca

Jambura Journal of Mathematics ◽

10.34312/jjom.v4i1.11993 ◽

2022 ◽

Vol 4 (1) ◽

Author(s):

Wibawati Wibawati ◽

Widya Amalia Rahma ◽

Muhammad Ahsan ◽

Wilda Melia Udiatami

Keyword(s):

Control Chart ◽

Control Charts ◽

Moving Average ◽

Quality Characteristic ◽

Average Process ◽

Exponentially Weighted Moving Average ◽

Ewma Control Chart ◽

Control Limits ◽

Weighted Moving Average ◽

Exponentially Weighted

In the industrial sector, the measurement results of a quality characteristic often involve an uncertainty interval (interval indeterminacy). This causes the classical control chart to be less suitable for monitoring quality. Currently, a control chart with a neutrosophic approach has been developed. The neutrosophic control chart was developed based on the concept of neutrosophic numbers with control charts. One of the control charts that have been developed to monitor the mean process is the Neutrosophic Exponentially Weighted Moving Average (NEWMA) X control chart. This control chart is a combination of neutrosophic with classical EWMA control chart. The neutrosophic control chart consists of two control charts, namely lower and upper, each of which consists of upper and lower control limits. Therefore, NEWMA X is more sensitive to detect out-of-control observations. In this research, the NEWMA X control chart will be used to monitor the average process of the thickness of the panasap dark grey 5mm glass produced by a glass industry. Through the analysis in this research, it was found that by using weighting λN [0, 10; 0, 10] and constant value kN [2, 565; 2, 675], the average process of the thickness of panasap dark grey 5mm glass has not beet controlled statistically because 21 observations were identified that were outside the control limits (out of control). When compared with the classical EWMA control chart with the same weighting λ, 17 observations were detected out of control. This proves that the NEWMA X control chart is more sensitive in detecting observations that are out of control because the determination of the in-control state is based on two values, lower and upper, both at the lower and upper control limits.

Download Full-text