The Fuzzy u-Chart for Sustainable Manufacturing in the Vietnam Textile Dyeing Industry

Abstract

Abstract:The inevitability of measurement errors and/or humans of subjectivity in data collection processes make accumulated data imprecise, and are thus called fuzzy data. To adapt to this fuzzy domain in a manufacturing process, a traditional u control chart for monitoring the average number of nonconformities per unit is required to extend. In this paper, we first generalize the u chart, named fuzzy u-chart, whose control limits are built on the basis of resolution identity, which is a well-known fuzzy set theory. Then, an approach to fuzzy-logic reasoning, incorporating the decision-maker's varying levels of optimism towards the online process, is proposed to categorize the manufacturing conditions. In addition, we further develop a condition-based classification mechanism, where the process conditions can be discriminated into intermittent states between in-control and out-of-control. As anomalous conditions are monitored to some extent, this condition-based classification mechanism can provide the critical information to deliberate the cost of process intervention with respect to the gain of quality improvement. Finally, the proposed fuzzy u-chart is implemented in the Vietnam textile dyeing industry to replace its conventional u-chart. The results demonstrate that the industry can effectively evade unnecessary adjustments to its current processes; thus, the industry can substantially reduce its operational cost and potential loss.

WITHDRAWN: Interval type-2 fuzzy moving average control charts considering explicit formula of average run length and their application

“…In a nutshell, they found that their proposed method was better than the previous methods. Truong et al (2017) analysed the fuzzy average number of nonconformities per unit in China for textile dyeing industry. Despite these successes, the data that is applicable in the real life somehow cannot be analysed using type-1 fuzzy sets due to single membership function and type-2 fuzzy numbers provides better tool to deal with incomplete and vague manufacturing data as their memberships have been extended to lower and upper bounds.…”

A Type-2 Fuzzy u-Control Chart Considering Probability-Based Average Run Length

“…This means that if the company does not apply the IT2Fu-chart, they might include the defect in their production. Research by Darestani et al [12], Fadaei and Pooya [19], Truong et al [20], Aslangiray and Akyuz [21], and Şentürk et al [22] studied T1Fu-chart and did not prove the research by using any method like ARL. Nevertheless, in this study, we extend the knowledge of fuzzy towards the IT2Fu-chart, and at the end of the study, it proves that the proposed method is much more sensitive and better at finding good-quality products.…”

“…These related researchers shed some light on the development of fuzzy charts where type-1 fuzzy variables or fuzzy rules were considered. In another study of fuzzy u-chart, Truong et al [20] monitored process mean in the textile dyeing industry in Vietnam. They suggested that the industry can apply fuzzy charts to reduce operational costs and potential losses.…”

Application of artificial intelligence techniques in textile wastewater decolorisation fields: A systematic and citation network analysis review

“…For example, there are various reasons for authors to include references, such as listing important books, but the author may not have read this book, or different journals have different requirements for the number of references, which may cause some papers to have unnecessary references. 25…”