APPLICATION OF FUZZY STATISTICAL PROCESS CONTROL FOR A MANUFACTURING OF GG25 GRAY CAST IRON MATERIAL

Year 2018, Volume: 7 Issue: 1, 427 - 437, 31.01.2018

Murat Çolak , Metin Uçurum , Mehmet Çınar Ümit Atıcı

https://doi.org/10.28948/ngumuh.387316

Cited By: 1

Abstract

   The group of ferrous materials with more than 2%
carbon in their chemical composition is commonly referred to as cast iron
materials. Carbon Equivalent (CE) is an empirical value in weight percent,
relating the combined effects of different alloying elements used in the making
of cast iron to an equivalent amount of carbon. Statistical process control
(SPC) can be applied in plants to obtain good quality and high standard
products which 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.
This study contains construction of a system design to observe whether the
conditions of an alloy production line are within the specification and control
limits. To determine the average percentage of CE
values, the fuzzy X-bar and R charts were
applied to GG25 gray cast iron samples for 20 days’ production. In order to
control the process parameters and improve quality of the cast products, the
comparison of the statistical and experimental
results show that the fuzzy SPC methods can be simply applied on a
foundry.

Keywords

GG25 gray cast iron, carbon equivalent, fuzzy statistical process control

GG25 GRİ DÖKME DEMİR MALZEMESİNİN ÜRETİMİNDE BULANIK İSTATİSTİKSEL PROSES KONTROLÜNÜN UYGULANMASI

Abstract

   Kimyasal
bileşimlerinde %2'den fazla karbon içeren demirli malzemeler grubuna yaygın
olarak dökme demir malzemeleri denir. Karbon Eşdeğeri (CE), dökme demir
üretiminde kullanılan farklı alaşım elementlerinin kombine etkilerini eşdeğer
miktarda karbon ile ilişkilendiren, ağırlık yüzdesi olarak ampirik bir
değerdir. İstatistiksel proses kontrol (SPC), çok popüler hale gelen kaliteli
ve yüksek standartlı ürünler elde etmek için fabrikalarda yaygın olarak uygulanmaktadır.
Bulanık proses kontrol analizi, X-R kontrol çizelgelerini kullanarak, bulanık
teori ile geliştirilmiş daha gerçekçi sonuçlar veren bir tekniktir. Bu çalışma,
bir gri dökme demir üretim hattı koşullarının, şartname ve kontrol sınırları dâhilinde
olup olmadığını bulanık istatistiksel proses kontrol tekniği ile gözlemlemek
için gerçekleştirilmiştir. Bulanık X-R grafikleri, 20 günlük üretim için GG25
gri dökme demir numunelerine uygulanmıştır. Sonuçlar, bulanık istatistiksel
proses kontrol (SPC) yöntemlerinin bir dökümhaneye basitçe uygulanabileceğini
göstermektedir

Keywords

GG25 gri dökme demir, karbon eşitliği, bulanık istatistik kontrol

References

• [1] AVNER, S.H., Introduction to Physical Metallurgy, Second Edition, McGrawhill İnternational Editions, Chapter 11, 450-453, 1974.
• [2] BRAMFITT, B.L., BEN SCOTER, A.O., Metallographer's Guide: Practices and Procedures for Irons and Steels, USA, ASM International, Chapter 1, 16-21, 2002.
• [3] BOCKU, S., “A Study of the Microstructure and Mechanical Properties of Continuously Cast Iron Products”, Metalurgıja, 45, 287-290, 2006.
• [4] CHAO, C.G, LUIT, S., HON, M.H., “A study of Tensile Properties of Ferritic Compacted Graphite Cast Irons at Intermediate Temperatures”, Journal of Materials Science, 24(7), 2610-2614, 1998.
• [5] KENAWY, M.A., ABDEL–FATAH, A.M., OKASHA, N., EL-GAZARY, M., “Ultrasonic Measurements and Metallurgical Properties of Ductile Cast Iron”, Egyptian Journal of Solids, 24(2), 133-140, 2001.
• [6] CAMPBELL, J., Castings, Second Edition. University of Birmingham, UK, 2003.
• [7] ÇAVUŞOĞLU, N., Döküm Teknolojisi 1, İstanbul Teknik Üniversitesi Matbaası, Gümüşsuyu, 1981.
• [8] FREDRIKSSON, H., STJERNDAHL, A., TINOCO, J., “On the Solidification of Nodular Cast Iron and its Relation to the Expansion and Contractiona”, Materials Science and Engineering, A 413–414, 363-372, 2005.
• [9] STEFANESCU, D.M., ASM Handbook Metals Handbook, Vol.15, Casting, ASM International, Metals Park, 296 – 307, OHIO, 1988.
• [10] THEODORA, K, JENNIFER, L, JOHN F.M., “Experiences with Industrial Applications of Projection Methods for Multivariate Statistical Process Control”, Computers Chem. Eng., 20, 745-750, 1996.
• [11] IPEK, H., ANKARA, H., OZDAG, H., “The Application of Statistical Process Control”, Minerals Engineering, 12, 827-835, 1999.
• [12] WOODALL, W.H., “Controversies and Contradictions in Statistical Process Control”, J. Qual. Technol., 32, 341-350, 2000.
• [13] DUDEK-BURLIKOWSKA, M., “Using Control Charts X-R in Monitoring a Chosen Production Process”, J. Achieve. Mater. Manufactur. Eng., 49, 487-498, 2011.
• [14] KHADEMI, M., AMIRZADEH, V., “Fuzzy Rules for Fuzzy X and R Control Charts”, Iranian Journal of Fuzzy Systems, 11(5), 55-66, 2014.
• [15] FILZMOSER, R., VERTL, R., “Testing Hypotheses with Fuzzy Data: The Fuzzy P value”, Metrika, 59, 21–29, 2004.
• [16] VIERTL, R., HARETER, D., “Fuzzy Estimation and Imprecise Probability”. Journal of Applied Mathematics and Mechanics, 84, 731–739, 2004.
• [17] GÜLBAY, M., KAHRAMAN, C., “An Alternative Approach to Fuzzy Control Charts: Direct Fuzzy Approach”, Information Sciences, 177, 1463–1480, 2007.
• [18] KANAGAWA, A., TAMAKI, F., OHTA, H., “Control Charts for Process Average and Variability Based on Linguistic Data”, International Journal of Production Research, 31, 913–922, 1993.
• [19] SUGANO, N., “Fuzzy Set Theoretical Approach to Achromatic Relevant Color on the Natural Color System”, International Journal of Innovative Computing. Information and Control, 2(1), 193–203, 2006.
• [20] SHU, M.H., HSIEN, C.W., “Fuzzy X and R Control Charts: Fuzzy Dominance Approach”, Computers & Industrial Engineering, 61, 676–685, 2011.
• [21] SINGH, R., Cast Iron Metallurgy, Materials Performance, 2009
• [22] PRAJAPATI, D.R. “Implementation of SPC Techniques in Automotive Industry: A Case Study”, International Journal of Emerging Technology and Advanced Engineering, 2(3), 227-241, 2012.
• [23] http://www.world-class-quality.com (accession date 09.03.2015).
• [24] MONTGOMERY, D.C., Introduction to Statistical Quality Control, New York: John 626 Wiley & Sons. 2005.
• [25] KHADEMI, M., AMIRZADEH, V., “Fuzzy Rules for Fuzzy X and R Control Charts”, Iranian Journal of Fuzzy Systems, 11(5), 55-66, 2014
• [26] KANE V.E., “Process Capability Indices”, Journal of Quality Technology, 18, 41–52, 1986.
• [27] KOTZ, S., JOHNSON, N., “Process Capability Indices – A review”, Journal of Quality Technology, 34, 2–19, 2002.
• [28] KAYA, I., KAHRAMAN, C., “Process Capability Analyses Based on Fuzzy Measurements and Fuzzy Control Charts”, Expert Systems with Applications, 38, 3172–3184, 2011.
• [29] HAGHIGHI, H., SHAHKARAMI, A.A., SHAHKARAM, F., SHAKERI, M., NAJIZADEH, R., Statistical Quality Control Tools; Practical approach, 1st Edition, Industrial Management Organization Publishing. 1994.
• [30] http://www.kurumsalkalite.com/surec-yeterlilik-indeksi-2-cpk-ppk, (accession date 15.09.2014).
• [31] ZEYVELİ, M., SELALMAZ, E., İstatistiksel Proses Kontrol Tekniklerinin Zincir İmalatı Yapan Bir İşletmede Uygulanması, Doğu Anadolu Bölgesi Araştırmaları, 2008.