Fuzzy <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mover accent="true"><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mo>-</mml:mo></mml:mover></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:math> Control Charts: A Data-Adaptability and Human-Acceptance Approach

Shu, Ming‐Hung; Dang, Dinh-Chien; Nguyễn, Thanh Lâm; Hsu, Bi‐Min; Phan, N H

doi:10.1155/2017/4376809

Cited by 16 publications

(12 citation statements)

References 34 publications

(56 reference statements)

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“…Section 2 briefly provides key characteristics of the traditional u chart to support part of Section 3, the procedure of constructing the fuzzy u-chart. It is well noted that these two sections depart from the approach presented in [26,28]. Here, the core issue is centered on the fuzzy-attributed control charts, fuzzy u-chart, while Nguyen et al [26] and Shu et al [28] stressed on the fuzzy-variable control charts, x and s control charts.…”

Section: Introductionmentioning

confidence: 98%

“…It is well noted that these two sections depart from the approach presented in [26,28]. Here, the core issue is centered on the fuzzy-attributed control charts, fuzzy u-chart, while Nguyen et al [26] and Shu et al [28] stressed on the fuzzy-variable control charts, x and s control charts. Section 4 first reviews the Nguyen and Hien's method [27] which is then extended for better performance of ranking results.…”

Section: Introductionmentioning

confidence: 98%

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

Truong

Shu²,

Nguyễn³

et al. 2017

Symmetry

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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.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

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

Truong

Shu²,

Nguyễn³

et al. 2017

Symmetry

Self Cite

View full text Add to dashboard Cite

show abstract

“…Mojtaba et al [14] proposed the fuzzy chart using triangle fuzzy random variable. Shu et al [15] proposed the fuzzy chart using data-adoptability approach. Afshari and Gildeh [16] designed a fuzzy control chart using the multiple dependent state (MDS) sampling.…”

Section: Introductionmentioning

confidence: 99%

“…Fadaei and Pooya [17] proposed the fuzzy U control chart. More detail on the control charts using the fuzzy approach can be seen in references [15,18].…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Variability in the Process Using Neutrosophic Statistical Interval Method

Aslam

Khan

2018

Symmetry

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Existing variance control charts are designed under the assumptions that no uncertain, fuzzy and imprecise observations or parameters are in the population or the sample. Neutrosophic statistics, which is the extension of classical statistics, has been widely used when there is uncertainty in the data. In this paper, we will originally design S 2 control chart under the neutrosophic interval methods. The complete structure of the neutrosophic S 2 control chart will be given. The necessary measures of neutrosophic S 2 will be given. The neutrosophic coefficient of S 2 control chart will be determined through the neutrosophic algorithm. Some tables are given for practical use. The efficiency of the proposed control chart is shown over the S 2 control chart designed under the classical statistics in neutrosophic average run length (NARL). A real example is also added to illustrate the proposed control chart. From the comparison in the simulation study and case study, it is concluded that the proposed control chart performs better than the existing control chart under uncertainty.

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“…Hybrid incremental model with optimal parametrization is used to predict surface roughness in milling processes [8]. Fuzzy x and s control charts are used to monitor the surface roughness of optical lenses in the online manufacturing process [9]. A two-stage neural network-based scheme to enhance the accurate identification rate is used in statistical process control and engineering process control, and satisfactory result is got [10].…”

Section: Introductionmentioning

confidence: 99%

Approach to Online Defect Monitoring in Fused Deposition Modeling Based on the Variation of the Temperature Field

Wang

2018

Complexity

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According to the surface state of the product, a process quality monitoring technology based on a Qualitative Trend Analysis method is proposed. The construction of the product surface defect knowledge base provides a basis for online monitoring of the nozzle status of the fused deposition modeling (FDM) machine. Ten workpieces are designed in all, and each one conducts two prints which are normal and abnormal, respectively. An infrared thermal imager is used to obtain the temperature image of each layer of the workpiece surface; the feature values are extracted, and the difference between the feature values in the two kinds of printing is calculated. The Qualitative Trend Analysis method is used to analyze the difference in order to obtain the corresponding sensitive feature parameters corresponding to the starting points in different phases of the product printing. The change rules of the sensitive feature values are summarized, and the threshold of each starting point is solved. Base on that, the knowledge rules are defined and the defect knowledge base is constructed. Finally, a new experiment is designed to obtain the theoretical starting point of each state of the product surface in the building process. By comparing the theoretical starting point with the actual starting point, the feasibility of the knowledge rules in monitoring the nozzle working state is verified.

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Fuzzy x- and s Control Charts: A Data-Adaptability and Human-Acceptance Approach

Cited by 16 publications

References 34 publications

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

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

Monitoring the Variability in the Process Using Neutrosophic Statistical Interval Method

Approach to Online Defect Monitoring in Fused Deposition Modeling Based on the Variation of the Temperature Field

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