The application of statistical process control

Ipek, Halil; Ankara, Huseyin; Özdağ, Hüseyin

doi:10.1016/s0892-6875(99)00067-9

Cited by 22 publications

(14 citation statements)

References 1 publication

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“…The cumulative weight passing particle sizes d 20 and d 80 [44,45] are used to evaluate the width of the PSD in mineral processing, and the particle size ratio of d 80 /d 20 is often calculated for the PSD width. The particle sizes d 10 [49,50]. A decrease in these particle size ratios means a narrower PSD.…”

Section: Fuzzy Statistical Process Control Of a Calcite Grinding Plantmentioning

confidence: 99%

“…Control charts are among the most effective means for controlling process control systems via statistical methods in an economical and secure way. Control charts are used for determining quantitative and qualitative variations that occur in a process over a certain time frame [10]. To use a control chart such as the X-chart to monitor the process mean or the R-chart to monitor variability, samples are taken over time and values of a statistic are plotted [11].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Statistical Process Control of a Calcite Grinding Plant Using Total Color Difference Parameter (ΔE)

Uçurum¹

2017

IOSR JEN

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: -Statistical process control (SPC) is one of the important approaches used in quality management.SPC can be applied in plants to obtain good quality and high standard products that have become very popular in many industries. Fuzzy process capability analysis by using X-R control charts gives more realistic results, developed with fuzzy theory. Fuzzy control charts are more sensitive than SPC. Therefore, fuzzy control charts lead to producing better-quality products. In this study, total color difference parameter (ΔE) was studied using fuzzy observation on a calcite grinding plant products. For this purpose, color parameters of the grinding plant products were evaluated using triangular fuzzy number (TFN) and fuzzy process capability indices (PCIs). The results show that the mill plant seems to be under control. Therefore, on a randomly selected sample used in the fuzzy statistical process control work was chosen and other color parameters such as whiteness index (WI), saturation index (SI), hue angle (H), browning index (BI) and yellowness index (YI) and particle size properties, XRF, XRD, FTIR, TGA-DTA and SEM were then determined on the calcite sample.

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Section: Fuzzy Statistical Process Control Of a Calcite Grinding Plantmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy Statistical Process Control of a Calcite Grinding Plant Using Total Color Difference Parameter (ΔE)

Uçurum¹

2017

IOSR JEN

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“…If Cp k is higher than 1, then 99.73% of the products in the process will be within specification limits. Generally, Cp and Cp k values of more than 1.33 are required by most modern industrial manufacturers (mass production) (Pitt, 1996;Ipek et al, 1999). Consequently, this study will consider a minimum value of 1.33.…”

Section: ¼ X ð7þmentioning

confidence: 99%

Optimizing a recycling process of SMC composite waste

et al. 2008

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Investigations about the recycling of sheet moulding compounds (SMC) waste as incorporation material for thermoplastic polymer matrix are reported in this paper. A new efficient process is developed in order to strongly increase the reinforcement glass fraction of SMC leading to good mechanical performance of the new thermoplastic compounds. The overall process is composed of two main steps: mechanical and chemical. The second stage is characterised in terms of optimization and capability by means of experimental design and statistical process control techniques for finding the optimal chemical conditions and validating the process.

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“…Statistical process control (SPC) [1] is an earlier widely used univariate FD method. And principal component analysis (PCA) [2][3][4] is a multivariate method.…”

Section: Introductionmentioning

confidence: 99%

OES structural feature based fault detection method for plasma etching

Zhao

Wang

2012

2012 International Conference on Systems and Informatics (ICSAI2012)

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Optical Emission Spectra (OES) is a widely used signal in plasm etching. In this paper, an OES structural feature based fault detection method is proposed. Firstly, a template of normal OES curves is extracted via non-negative matrix factorization. Then singular points of the curves are detected by local matching based on the template. The magnitude and occurrence time of these singular points form a structural feature vector, which is a quantitative and simplified description of the curve's shape. Lastly, one-class SVM is introduced for generate a fault detection model based on structural feature vectors from normal OES curves. Experiments on an industrial benchmark dataset show that the proposed method is effective.

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The application of statistical process control

Cited by 22 publications

References 1 publication

Fuzzy Statistical Process Control of a Calcite Grinding Plant Using Total Color Difference Parameter (ΔE)

Fuzzy Statistical Process Control of a Calcite Grinding Plant Using Total Color Difference Parameter (ΔE)

Optimizing a recycling process of SMC composite waste

OES structural feature based fault detection method for plasma etching

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