Research Article
A FUZZY APPLICATION OF X-R CONTROL CHARTS OF AN ALUMINUM PRODUCTION PLANT USING FUZZY TRIANGULAR NUMBERS
Abstract
In this study, fuzzy mean and range control charts were used to follow the feeding material and concentrate production line of Eti Aluminum Co. Fuzzy control charts were collected from the factory over a period of time and compared to Shewhart control charts used by the factory. The results showed that fuzzy control charts detected errors in the production process more accurately than Shewhart control charts. This method has increased quality and efficiency. Process capability indices (PCIs) provide numerical measures as to whether a process has confirmed the defined capability prerequisite. These were used to measure the process's ability to decide how well it meets specification limits (SLs). PCIs have been implemented by companies to evaluate quality and efficiency performance. Fuzzy process capability analysis using X-R control charts gave more accurate results.
References
- Intaramo, R. and Pongpullponsak, A., “Development of fuzzy extreme value theory control charts using α-cuts for skewed”, Applied Mathematical Sciences, 6, 5811-5834, 2012.
- Bradshaw, C.W., “A fuzzy set theoretic interpretation of economic control limits”, European Journal of Operational Research, 13, 403-408, 1983.
- Tannock, J.D.T., “A fuzzy control charting method for individuals”, International Journal of Production Research, 41, 13-22, 2003.
- Gülbay, M. and Kahraman, C., “An alternative approach to fuzzy control charts: direct fuzzy approach”, Information Sciences, 177, 1463-1480, 2007.
- Shu, M.H. and Wu, H.C., “Fuzzy X and R control charts: fuzzy dominance approach”, Computers & Industrial Engineering, 61, 676-685, 2011.
- Alizadeh, H.M. and Ghomi, S.M.T.F., “Fuzzy development of mean and range control charts using statistical properties of different representative values”, Journal of Intelligent and Fuzzy Systems, 22, 253-265, 2011.
- Khademi, M. and Amirzadeh, V., “Fuzzy rules for fuzzy X and R control charts”, Iranian Journal of Fuzzy Systems, 11, 55-66, 2014.
- Kanagawa, A., Tamaki, F. and Ohta, H., “Control charts for process average and variability based on linguistic data”, International Journal of Production Research, 31, 913-922, 1993.
- Gülbay, M., Kahraman, C. and Ruan, D., “α-Cuts fuzzy control charts for linguistic data”, International Journal of Intelligent Systems, 19, 1173-1196, 2004.
- Gülbay, M. and Kahraman, C., “Development of fuzzy process control charts and fuzzy unnatural pattern analyses”, Computational Statistics & Data Analysis, 51, 434-451, 2006.
- Şentürk, S., Erginel, N., Kaya, I. and Kahraman, C., “Design of fuzzy U ̃ control chart”, Journal of Multiple Valued-Logic and Soft Computing, 5, 459-473, 2011.
- Şentürk, S., “Construction of fuzzy C control charts based on fuzzy rule method”, Anadolu University Journal of Science and Technology A-Applied Sciences and Engineering, 18, 563-572, 2017.
- Kaya, I. and Kahraman, C., “Process capability analyses based on fuzzy measurements and fuzzy control charts”, Expert Systems with Applications, 38, 3172-3184, 2011.
- Uçurum, M., “Fuzzy statistical process control of a calcite grinding plant using total color difference parameter (ΔE)”, IOSR Journal of Engineering, 7, 7-22, 2017.
- Montgomery, D.C., Introduction to Statistical Quality Control, 5th ed., John Wiley & Sons, USA, 2005.
- Montgomery, D.C., Statistical Quality Control: A Modern Introduction, 6th ed., John Wiley & Sons, USA, 2009.
- Prajapati, D.R., “Implementation of SPC techniques in automotive industry: A case study”, International Journal of Emerging Technology and Advanced Engineering, 2, 227-241, 2012.
- Emami, G., Laleh, K.F. and Radfar, F., “A fuzzy application of X and R control charts using fuzzy triangular numbers”, 2nd International Conference on Modern Research’s in Management, Economics Accounting, Malaysia, 2016.
- Erginel, N., “Fuzzy individual and moving range control charts with α-cuts”, Journal of Intelligent & Fuzzy Systems, 19, 373-383, 2008.
- Zabihinpour, S.M., Ariffin, M.K.A., Tang, S.H. and Azfanizam, A.S., “Fuzzy based approach for monitoring the mean and range of the products quality”, Journal of Applied Environmental & Biological Sciences, 4, 1-7, 2014.
- Kane, V.E., “Process capability indices”, Journal of Quality Technology, 18, 41-52, 1986.
- Kotz, S. and Johnson, N., “Process capability indices-A review 1992-2000”, Journal of Quality Technology, 34, 2-19, 2002.
- Kaya, I. and Kahraman, C., “Development of fuzzy process accuracy index for decision making problems”, Information Sciences, 180, 861-872, 2010.
- Carlsson, C. and Fullér, R., “On possibilistic mean value and variance of fuzzy numbers”, Fuzzy Sets and Systems, 122, 315-326, 2001.
ALÜMİNYUM ÜRETİM TESİSİNİN ÜÇGEN BULANIK SAYILAR KULLANILARAK X-R KONTROL ŞEMALARININ BULANIK MANTIK UYGULAMASI
Abstract
Bu çalışmada, üretim sürecini izlemek için bulanık ortalama ve aralık kontrol grafikleri kullanılmıştır. Bulanık kontrol çizelgeleri, belirli bir süre aralığında fabrikadan veri toplanmış ve fabrika tarafından kullanılan Shewhart kontrol çizelgeleri ile karşılaştırılmıştır. Sonuçlar, bulanık kontrol grafiklerinin Shewhart kontrol grafiklerine göre üretim sürecindeki hataları daha doğru bir şekilde tespit ettiği görülmüştür. Bu yöntem, kaliteyi ve verimliliği artırmıştır. İşlem yeteneği endeksleri (PCI'ler), bir işlemin tanımlanmış yetenek önkoşulunu onaylayıp onaylamadığına dair sayısal önlemler sağlar. Bunlar, sürecin şartname sınırlarını (SL'ler) ne kadar iyi karşıladığına karar verme yeteneğini ölçmek için kullanılmıştır. PCI'lar şirketler tarafından kalite ve verimlilik performansını değerlendirmek için uygulanmıştır. X-R kontrol grafikleri kullanılarak yapılan bulanık proses yeterlilik analizi daha doğru sonuçlar vermiştir.
References
- Intaramo, R. and Pongpullponsak, A., “Development of fuzzy extreme value theory control charts using α-cuts for skewed”, Applied Mathematical Sciences, 6, 5811-5834, 2012.
- Bradshaw, C.W., “A fuzzy set theoretic interpretation of economic control limits”, European Journal of Operational Research, 13, 403-408, 1983.
- Tannock, J.D.T., “A fuzzy control charting method for individuals”, International Journal of Production Research, 41, 13-22, 2003.
- Gülbay, M. and Kahraman, C., “An alternative approach to fuzzy control charts: direct fuzzy approach”, Information Sciences, 177, 1463-1480, 2007.
- Shu, M.H. and Wu, H.C., “Fuzzy X and R control charts: fuzzy dominance approach”, Computers & Industrial Engineering, 61, 676-685, 2011.
- Alizadeh, H.M. and Ghomi, S.M.T.F., “Fuzzy development of mean and range control charts using statistical properties of different representative values”, Journal of Intelligent and Fuzzy Systems, 22, 253-265, 2011.
- Khademi, M. and Amirzadeh, V., “Fuzzy rules for fuzzy X and R control charts”, Iranian Journal of Fuzzy Systems, 11, 55-66, 2014.
- Kanagawa, A., Tamaki, F. and Ohta, H., “Control charts for process average and variability based on linguistic data”, International Journal of Production Research, 31, 913-922, 1993.
- Gülbay, M., Kahraman, C. and Ruan, D., “α-Cuts fuzzy control charts for linguistic data”, International Journal of Intelligent Systems, 19, 1173-1196, 2004.
- Gülbay, M. and Kahraman, C., “Development of fuzzy process control charts and fuzzy unnatural pattern analyses”, Computational Statistics & Data Analysis, 51, 434-451, 2006.
- Şentürk, S., Erginel, N., Kaya, I. and Kahraman, C., “Design of fuzzy U ̃ control chart”, Journal of Multiple Valued-Logic and Soft Computing, 5, 459-473, 2011.
- Şentürk, S., “Construction of fuzzy C control charts based on fuzzy rule method”, Anadolu University Journal of Science and Technology A-Applied Sciences and Engineering, 18, 563-572, 2017.
- Kaya, I. and Kahraman, C., “Process capability analyses based on fuzzy measurements and fuzzy control charts”, Expert Systems with Applications, 38, 3172-3184, 2011.
- Uçurum, M., “Fuzzy statistical process control of a calcite grinding plant using total color difference parameter (ΔE)”, IOSR Journal of Engineering, 7, 7-22, 2017.
- Montgomery, D.C., Introduction to Statistical Quality Control, 5th ed., John Wiley & Sons, USA, 2005.
- Montgomery, D.C., Statistical Quality Control: A Modern Introduction, 6th ed., John Wiley & Sons, USA, 2009.
- Prajapati, D.R., “Implementation of SPC techniques in automotive industry: A case study”, International Journal of Emerging Technology and Advanced Engineering, 2, 227-241, 2012.
- Emami, G., Laleh, K.F. and Radfar, F., “A fuzzy application of X and R control charts using fuzzy triangular numbers”, 2nd International Conference on Modern Research’s in Management, Economics Accounting, Malaysia, 2016.
- Erginel, N., “Fuzzy individual and moving range control charts with α-cuts”, Journal of Intelligent & Fuzzy Systems, 19, 373-383, 2008.
- Zabihinpour, S.M., Ariffin, M.K.A., Tang, S.H. and Azfanizam, A.S., “Fuzzy based approach for monitoring the mean and range of the products quality”, Journal of Applied Environmental & Biological Sciences, 4, 1-7, 2014.
- Kane, V.E., “Process capability indices”, Journal of Quality Technology, 18, 41-52, 1986.
- Kotz, S. and Johnson, N., “Process capability indices-A review 1992-2000”, Journal of Quality Technology, 34, 2-19, 2002.
- Kaya, I. and Kahraman, C., “Development of fuzzy process accuracy index for decision making problems”, Information Sciences, 180, 861-872, 2010.
- Carlsson, C. and Fullér, R., “On possibilistic mean value and variance of fuzzy numbers”, Fuzzy Sets and Systems, 122, 315-326, 2001.
There are 24 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Journals |
| Authors | |
| Publication Date | December 31, 2020 |
| Published in Issue | Year 2020 Volume: 6 Issue: 2 |
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