Design of variable control charts based on type-2 fuzzy sets with a real case study

Kaya, İ̇hsan; Turgut, Ataullah

doi:10.1007/s00500-020-05172-4

Cited by 17 publications

(16 citation statements)

References 47 publications

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“…en, standardized CUSUM and Zi are calculated by using equation (12) for each of the sample, and the results are presented in Tables 8 and 9. In this study, we will use a tabular CUSUM with κ � 0.5 (because the shift size is 1σ and σ � 1), μ 0 � 16, and H � 5 (as the recommended value of the decision interval is H � 5σ � 5).…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Control charts are used to compare with the crisp number to choose the best method. Other than that, Kaya and Turgut [12] analysed the type-2 fuzzy variables control chart and applied it on a real case application from electronic industry. ey concluded that type-2 fuzzy control charts can evaluate the process in more sensitive and precise way.…”

Section: Introductionmentioning

confidence: 99%

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Interval Type-2 Fuzzy Standardized Cumulative Sum Control Charts in Production of Fertilizers

Razali

Abdullah

Salleh

et al. 2021

Mathematical Problems in Engineering

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Statistical process control is a method used for controlling processes in which causes of variations and correction actions can be observed. Control chart is one of the powerful tools of statistical process control that are used to control nonconforming products. Previous literature suggests that fuzzy charts are more sensitive than conventional control charts, and hence, they provide better quality and conformance of products. Nevertheless, some of the data used are more suitable to be presented in interval type-2 fuzzy numbers compared to type-1 fuzzy numbers as interval type-2 fuzzy numbers have more ability to capture uncertain and vague information. In this paper, we develop an interval type-2 fuzzy standardized cumulative sum (IT2F-SCUSUM) control chart and apply it to data of fertilizer production. This new approach combines the advantages of interval type-2 fuzzy numbers and standardized sample means which can control the variability. Twenty samples with a sample size of six were examined for testing the conformance. The proposed IT2F-SCUSUM control chart unveils that 15 samples are “out of control.” The results are also compared to the conventional CUSUM chart and type-1 fuzzy CUSUM chart. The conventional chart shows that 13 samples are “out of control.” In contrast, the type-1 fuzzy CUSUM chart shows that the process is “out of control” for 14 samples. In the analysis of average run length, the proposed IT2F-SCUSUM chart outperforms the other two CUSUM charts. Thus, we can conclude that the IT2F-SCUSUM chart is more sensitive and takes lesser number of observations to identify the shift in the process. The analyses suggest that the IT2F-SCUSUM chart is a promising tool in examining conformance of the quality of the fertilizer production.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interval Type-2 Fuzzy Standardized Cumulative Sum Control Charts in Production of Fertilizers

Razali

Abdullah

Salleh

et al. 2021

Mathematical Problems in Engineering

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“…This work is licensed under a Creative …………………… Since the introduction of process charts in the 1924s, (SPC) has become an important tool for monitoring and improving product quality in industrial sectors. SQC approaches have been widely used in a variety of industries and organizations to raise quality standards, increase process efficiency, and minimize product defects and variations [2], [9]- [12]. Furthermore, various approaches have been developed to facilitate better quality control during production processes, with notable contributions from, Ishikawa, Deming, Juran, Montgomery, and others.…”

Section: Volume … 2021mentioning

confidence: 99%

“…Also the potential yield can be calculated as Eq as (2). Hence , the event of x1 and x2 is an occurrence that can be presented as 12 xx and the potential yield process thus (3)…”

Section: A Estimation For Potential Process Yield Ssy Based On 6σmentioning

confidence: 99%

Comparative Analysis Four Different Ways of Calculating Yield Index SSS_pk Based on Information of Control Chart, and Six Sigma, to Measuring the Process Performance in Industries: Case Study in Aden’s Oil Refinery, Yemen

et al. 2021

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Process capability index PCIs have long been used to assess process efficiency in the manufacturing. While PCIs assess the process stability and efficiency, real measurements of product quality depended on estimate the standard deviation for a short-term variance was calculated as the base approach in several previous studies for measuring PCIs. Studies in recent years seek to estimate shift of the process mean from target. In today's smart manufacturing production climate, manufacturers keep track of production performance. Since the deviance of process mean comparative to the target is different. This allows a more robust norm to be implemented by estimate the relative shift to describe the variance of process in Six Sigma (SS). For that purpose, this paper suggests an evaluation method by comparative analysis to four different ways of calculating yield indexes Spk based on six sigma and control information, of S X chart. Moreover, this paper extends the univariate process index yield SSSpk to a multi-generalized yield index is called SSMSpk. An established an updated approach to assess univariate and multi-characteristic, based on understanding variation which is critical to quality (CTQ) and focusing on the position and actions of natural process tolerance between defined tolerances limits. To demonstrate the trueness verification proficiency of this approach, this paper includes an industrial case study to assess the process efficiency in Aden's oil refinery, Yemen. The findings of this study indicated that the indexes which are calculating based on six sigma outperformed existing indices and that this study has important implications for industrial practitioners, researchers and quality control experts interested in the evaluation of process performance.

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“…When fuzzy data are transformed into fuzzy representative values, the problem of data information loss inevitably occurs. I. Kaya et al [11][12][13] discussed the problem of information loss in the process of transforming fuzzy data into representative numerical values. The above four fuzzy representative values may lead to the loss of some important sample data information, which is due to the different information contained in different a level cut sets not being fully and accurately utilized.…”

Section: Characterization and Calculation Of Fuzzy Multilevel Qualitymentioning

confidence: 99%

Research on fuzzy control charts for fuzzy multilevel quality characteristics

Zhou

Huang

2021

Int. J. Metrol. Qual. Eng.

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Fuzzy control charts are proposed to solve the problem that traditional control charts cannot be applied to fuzzy quality characteristics. First, fuzzy quality characteristics are converted to representative statistics, which are fuzzy mode transformation, fuzzy level midrange transformation and fuzzy level median transformation. Control charts are designed based on the Poisson distribution. Second, the effects of the different statistics are analysed. Direct Fuzzy Control Charts are designed to avoid some information omission when translating fuzzy quality characteristics into representative statistics. The area ratio that falls within the control limits is used to conclude whether the process is relative out of control or in control. The performance of the control charts is analysed by MATLAB simulation. Finally, an example of an energy metre assembly is given to prove the proposed method.

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Design of variable control charts based on type-2 fuzzy sets with a real case study

Cited by 17 publications

References 47 publications

Interval Type-2 Fuzzy Standardized Cumulative Sum Control Charts in Production of Fertilizers

Interval Type-2 Fuzzy Standardized Cumulative Sum Control Charts in Production of Fertilizers

Comparative Analysis Four Different Ways of Calculating Yield Index SSS_pk Based on Information of Control Chart, and Six Sigma, to Measuring the Process Performance in Industries: Case Study in Aden’s Oil Refinery, Yemen

Research on fuzzy control charts for fuzzy multilevel quality characteristics

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Design of variable control charts based on type-2 fuzzy sets with a real case study

Cited by 17 publications

References 47 publications

Interval Type-2 Fuzzy Standardized Cumulative Sum Control Charts in Production of Fertilizers

Interval Type-2 Fuzzy Standardized Cumulative Sum Control Charts in Production of Fertilizers

Comparative Analysis Four Different Ways of Calculating Yield Index SSSpk Based on Information of Control Chart, and Six Sigma, to Measuring the Process Performance in Industries: Case Study in Aden’s Oil Refinery, Yemen

Research on fuzzy control charts for fuzzy multilevel quality characteristics

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Product

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Comparative Analysis Four Different Ways of Calculating Yield Index SSS_pk Based on Information of Control Chart, and Six Sigma, to Measuring the Process Performance in Industries: Case Study in Aden’s Oil Refinery, Yemen