X-R Kontrol Kartları ve Çok Boyutlu Ölçekleme Analizi Kullanılarak Çayeli Bakır İşletmelerinin İstatistiksel Proses Kontrolü

Çinko ve bakır cevherlerinin besleme malı, konsantre ve atıkları çok boyutlu ölçekleme analizi ile incelenmiştir. X-R analizine göre bakır (besleme malı, konsantre ve artık) için hesaplanan 〖LCL_X,UCL〗_X and UCL_R değerleri sırasıyla 1.94, 16.92, 0.16; 2.96, 22.90, 0.41 ve 0.89, 5.19, 0.21’dir. Benzer şekilde, çinko için bu değerler sırasıyla 0.31, 43.46, 0.23; 3.00, 50.33, 0.66 ve 2.34, 5.97, 0.37’dir. Hesaplanan Cp bakır ve çinko değerleri sırasıyla 2,08, 1,42, 1,39 ve 1,82, 1,54 ve 1,25'tir. Bakır ve çinkonun besleme malı, konsantre ve atık parametreleri 1,0'den büyüktür. Benzer şekilde, bu çalışma bakır ve çinko için hesaplanan Cpk değerlerinin (2,15, 1,20 ve 1,72 ; 3,82, 1,05 ve 1,53) 1,0'den büyük olduğunu göstermektedir. Stress değerleri, analizin ilk aşamasında hesaplanmış ve bakır ve çinko için sırasıyla 0,00258 ve 0,00674'te belirlenmiştir. Bununla birlikte RSQ, sırasıyla bakır ve çinko için 0,9998 ve 0,9986 olarak hesaplanmıştır. Bu değerler faktörler arasında yüksek bir korelasyon olduğunu göstermiştir. Son olarak, bu çalışma Çayeli Bakır İşletmelerinde karar vericilere yardımcı olmak için ortalama ve aralık kontrol çizelgeleri, süreç doğruluk indeksleri ve çok boyutlu ölçekleme analizi gibi istatistiksel işlem kontrol tekniklerinin kullanışlılığını göstermiştir.

Anahtar Kelimeler:

bakır, kontrol kartları, çok boyutlu ölçekleme analizi, süreç doğruluk indeksleri, çinko

Statistical Process Control for Çayeli Copper Companies using X-R Control Charts and Multidimensional Scaling Analysis

The feeding materials, concentrates and tailings of zinc and copper ores were examined by multidimensional scaling analysis. The calculated 〖LCL_X,UCL〗_X and UCL_R values for copper (feeding material, concentrate, and tailing) according to X-R analysis are 1.94, 16.92, 0.16; 2.96, 22.90, 0.41 and 0.89, 5.19, 0.21 respectively. Likewise, these values for zinc are 0.31, 43.46, 0.23; 3.00, 50.33, 0.66 and 2.34, 5.97, 0.37 respectively. The calculated Cp copper and zinc values are 2.08, 1.42, 1.39 and 1.82, 1.54, 1.25 respectively. The feeding material, concentrate, and tailing parameters of the copper and zinc products are greater than 1.0. Likewise, this study shows that the calculated Cpk values for copper and zinc (2.15, 1.20, 1.72 and 3.82, 1.05, 1.53 respectively) are larger than 1. Stress value was calculated at the first step of the analysis and established at 0.00258 and 0.00674 for copper and zinc, respectively, which indicates a fair fit for both. Nevertheless, the coefficient of determination (RSQ) was calculated as 0.9998 and 0.9986 for copper and zinc, respectively. These values indicated a high correlation between factors. Finally, this study showed that the usefulness of statistical process control techniques, such as mean and range control charts, process capability indexes and multidimensional scaling analysis, in helping decision makers in Çayeli Copper Companies.

Keywords:

copper, control charts, multidimensional scaling analysis, process capability indexes, zinc,

PDF

___

Benneyan, J.C., Lloyd, R.C., Plsek, P.E., 2003. Statistical Process Control As A Tool for Research and Healthcare Improvement, Quality Safety Health Care, Vol. 12, pp. 458-464. DOI: 10.1136/qhc.12.6.458
Oakland, J.S., 2004. Oakland on Quality Management, 3rd Edition, Butterworth Heinemann.
Mason, B., Antony, J., 2007. Statistical Process Control: An Essential Ingredient for Improving Service and Manufacturing Quality, Managing Service Quality, Vol. 10(4), pp. 233-238. DOI: 10.1108/09604520010341618
Salaheldin, I., 2009. Critical Success Factors for TQM Implementation and Their Impact on Performance of SMEs, International Journal of Productivity and Performance Management, Vol. 58(3), pp. 215-237. DOI: 10.1108/17410400910938832
Madanhire, I., Mbohwa, C., 2016. Application of Statistical Process Control (SPC) in Manufacturing Industry in A Developing Country, Procedia CIRP, Vol. 40, pp. 580-583. DOI: 10.1016/j.procir.2016.01.137
Mirko, S., Jelena, J., Zdravko, K., Aleksandra, V., 2009. Basic Quality Tools in Continuous Improvement Process, Journal of Mechanical Engineering, Vol. 55(5), pp. 1-4.
Azizi, A., 2015. Evaluation Improvement of Production Productivity Performance using Statistical Process Control Overall Equipment Efficiency and Autonomous Maintenance, Procedia Manufacturing, Vol. 2, pp. 186-190. DOI: 10.1016/j.promfg.2015.07.032
Tan, K.C., 2002. A Comparative Study of 16 National Awards, Total Quality Magazine, Vol. 14(3), pp. 165-167. DOI: 10.1108/09544780210425874
Machado, J.T., 2012. Multidimensional Scaling Analysis of Fractional Systems, Computers and Mathematics with Applications, Vol. 64(10), pp. 2966-2972. DOI: 10.1016/j.camwa.2012.02.069
Lopes, A.M., Andrade, J.P., Machado, J.T., 2016. Multidimensional Scaling Analysis of Virus Diseases, Computer Methods and Programs in Biomedicine, Vol. 131, pp. 97-110. DOI: 10.1016/j.cmpb.2016.03.029
Wang, J.H., Raz, T., 1990. On the Construction of Control Charts using Linguistic Variables, International Journal of Production Research, Vol. 28(3), pp. 477-487. DOI: 10.1080/00207549008942731
Montgomery, D.C., Woodall, W.H., 1997. A Discussion on Statistically-Based Process Monitoring and Control, Journal of Quality Technology, Vol. 29(2), pp. 121-162. DOI: 10.1080/00224065.1997.11979738
Chan, L.K., Cui, H.J., 2003. Skewness Correction X and R Charts for Skewed Distributions, Naval Research Logistics, Vol. 50(6), pp. 555-573. DOI: 10.1002/nav.10077
Khademi, M., Amirzadeh, V., 2014. Fuzzy Rules for Fuzzy X and R Control Charts, Iranian Journal of Fuzzy Systems, Vol. 11(5), pp. 55-66. DOI: 10.22111/IJFS.2014.1722
Motorcu, A.R., Güllü, A., 2006. Statistical Process Control in Machining, A Case Study for Machine Tool Capability and Process Capability, Materials and Design, Vol. 27, pp. 364-372. DOI: 10.1016/j.matdes.2004.11.003
Simanová, L., Gejdoš, P., 2015. The Use of Statistical Quality Control Tools to Quality Improving in the Furniture Business, Procedia Economics and Finance, Vol. 34, pp. 276-283. DOI: 10.1016/S2212-5671(15)01630-5
Uçurum, M., Malgır, E., Deligezen, H., Karaer, N., Avşar, M., 2016. Applicability of Statistical Process Control for Surface Modification Plant and Properties of Coated Calcite, Physicochemical Problems of Mineral Processing, Vol. 52(2), pp. 803-820. DOI: 10.5277/ppmp160223
Meyer, J.M., Heath, A.C., Eaves, L.J., Chakravarti, A., 1992. Using Multidimensional Scaling on Data from Pairs of Relatives to Explore the Dimensionality of Categorical Multifactorial Traits, Genetic Epidemiology, Vol. 9(2), pp. 87-107. DOI: 10.1002/gepi.1370090203
Jaworska, N., Anastasova, A., 2009. A Review of MDS and Its Utility in Various Psychological Domains, Tutorials in Quantitative Methods for Psychology, Vol. 5, pp. 1-10. DOI: 10.20982/tqmp.05.1.p001
Oraman, Y., Unakitan, G., Yilmaz, E., Başaran, B., 2011. Analysis of the Factors Affecting Consumer’s Some Traditional Food Products Preferences by Multidimensional Scaling Method, Journal of Tekirdag Agricultural Faculty, Vol. 8(1), pp. 33-40.
Yerel, S., Ankara, H., 2011. Process Control for A Coal Washing Plant using A Range Control Chart and Multidimensional Scaling Analysis, Energy Sources, Part A: Recovery, Utilization and Environmental Effects, Vol. 33, pp. 1028-1034. DOI: 10.1080/15567030903096998
Milton, J.S., Arnold, J.C., 2002. Introduction to Probability and Statistic: Principles and Applications for Engineering and the Computing Sciences, 4th Edition, McGraw Hill.
Montgomery, D.C., Runger, G.C., Hubele, N.F., 2011. Engineering Statistics, 5th Edition, John Willey&Sons Inc.
Parkash V., Kumar D., Rajoria R., 2013. Statistical Process Control, International Journal of Research in Engineering Technology, Vol. 2, pp. 70-72.
Alvarez, E., Fernandez, P.J., Encomienda, F.J., Munoz, J.F., 2015. Methodological Insights for Industrial Quality Control Management, Journal of King Saud University-Science, Vol. 27, pp. 271-277. DOI: 10.1016/j.jksus.2015.02.002
İşçen, C.F., Altin, A., Şenoğlu, B., Yavuz, H.S., 2009. Evaluation of Surface Water Quality Characteristics by using Multivariate Statistical Techniques, Environmental Monitoring Assessment, Vol. 151(1), pp. 259-264. DOI: 10.1007/s10661-008-0267-9