Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2021, Cilt: 9 Sayı: 1, 134 - 158, 29.01.2021

Kenan Orçanlı

https://doi.org/10.21541/apjes.720051
Cited By: 1

Öz

In process control in the casting industry, the features of the product, such as diameter, thickness, density, are generally considered as quality characteristics and assignable causes affecting the process is tried to be determined by monitoring these quality characteristics in the quality control charts. However, instead of the features of the producing product as quality characteristics in the casting industry, the proportions of the elements that make up the product can also be accepted. Because the proportions of the elements that make up the product are desired to be within certain limits within the product and these generally vary. In addition, metal ratios, which can be selected as quality characteristics, can be monitored with quality control charts as in the properties of the product, but interpretation of out-of-control signals may not be sufficient. Therefore, in the solution of the problem, instead of quality control graphics, the process-oriented basis representations method in the literature can be used. As a result of the research in the literature, it has been determined that the process-oriented basis representations method has been used successfully in the modeling of geometric deviations in the manufacturing industry, but it is not applied in the process (chemistry, petro-chemistry, casting, etc.) industries, and in multivariate industrial production processes with interrelated quality characteristics. In this content, the aim of this study was is to show that metal alloy ratios can be used as quality characteristics and the process-oriented basis representations method can be applied in process control in the casting industry. The data used in the study were obtained from the production process of Brass Factory Directorate of Mechanical and Chemical Industry Company in Kırıkkale province between 01 January 2015 and 31 March 2015. The module in the Minitab package program was used to create the control charts. At the end of the study, it has been determined that in the process control in the casting industry, the element ratios that make up the product produced as quality characteristics can be selected and positive results can be obtained by monitoring the quality characteristics selected in this way with the process-oriented basis representations method. It is evaluated that the results obtained in the study will contribute both to the domestic and foreign literature theoretically and to the quality control applications in terms of practicality in the casting industry.

Anahtar Kelimeler

Casting Brass Process Oriented Basis Representation

Kaynakça

  • [1]Özel, S. (2005). Çok Değişkenli Kalite Kontrol Çizelgelerinin Döküm Sanayiinde Uygulanması, (Yayımlanmamış Yüksek Lisans Tezi), Kırıkkale Üniversitesi, Fen Bilimleri Enstitüsü, Kırıkkale.
  • [2]Özel, S. ve Birgören, B. (2007), “Çok değişkenli kalite kontrol çizelgelerinin döküm sanayiinde uygulanması”, Gazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, Cilt 22(4), 809-811.
  • [3]Energy and Environmental Profile of the U.S. Metalcasting Industry. (1999). U.S.Department of Energy Office of Industrial Technologies.
  • [4]Moseki, M. (2002). “Research into melting and casting of brass scrap for upliftment purposes”. Journal of the Southern African Institute of Mining and Metallurgy, 102(2), 109-114.
  • [5]Nur Hamizah, M. (2010). Investigation of Brass Microstructure and Mechanical Properties Using Metal Casting, (Ph.D. Thesis), Universiti Malaysia Pahang, Malaysia.
  • [6]Prıbulová A., Gengeľ P. ve Bartošová M. (2010). Odpady z výroby oceľových a liatinových odliatkov /prachy – ich charakteristika, vlastnosti a možnosti použitia, TUKE Košice.
  • [7]Saravanakumar, P. (2015). “A systematic approach on reducing scrap level using six sigma in Indian foundries”, International Journal of Emerging Researches in Engineering Science and Technology, 2, 12.
  • [8]Aran, A. (2007). Döküm Teknolojileri İmal Usulleri Ders Notları, İstanbul Teknik Üniversitesi, İstanbul.
  • [9]Mysik, R. K., Brusnitsyn, S. V. ve Sulitsin, A. V. (2020). Determination of Thermo-Physical and Physical Properties of Complex Alloyed Brass. Solid State Phenomena, 299, 442–446.
  • [10]Şakar ve diğerleri (2019), Leaded brass alloys for gamma-ray shielding applications, Radiation Physics and Chemistry, 159, 64–6969.
  • [11]Brady, G.S. (1991). Materials Handbook: An Encyclopedia for Purchasing Managers, Engineers, Executives, and Foremen, McGraw-Hill Book Company INC, New York.
  • [12]Akgün, O. (2000). Pirinç Alaşımlarının Hazırlanmasında Flaksların Etkisi, (Yüksek Lisans Tezi), İ.T.Ü. Fen Bilimleri Enstitüsü, İstanbul.
  • [13]Mindivan, H. (2001). Yüksek Mukavemetli Pirinçlerin Mikro Yapı ve Aşınma Özelliklerine Isıl İşlemin Etkisi, (Yayımlanmamış Yüksek Lisans Tezi), İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü, İstanbul.
  • [14]Sakallı, U.S. ve Birgoren, B. (2009). “A spreadsheet-based decision support tool for blending problems in brass casting industry”, Computers & Industrial Engineering, 56 (2): 724–735.
  • [15]Birgören, B. (2015), İstatistiksel Kalite Kontrolü, Nobel Akademik Yayıncılık, Ankara.
  • [16]Çolak, M. (2011). Döküm endüstrisinde ergitme endüksiyon ocakları ve spektral analiz hesaplamaları, http://www.demircelikstore.com/-1-3842-dokum-endustrisinde-ergitme-enduksiyon-ocaklari-ve-spektral-analiz-hesaplamalari.html, Erişim Tarihi:12.10.2016.
  • [17]Moment Expo, (2008), Makine ve Aksamları İhracatçıları Birliği Aylık Makine İhracatı ve Ticareti Dergisi, Sayı:06.
  • [18]Orçanlı, K, Bi̇rgören, B. ve Oktay, E. (2018). Döküm Sanayisinde Metal Alaşım Oranlarına Hotelling T² ve MEWMA Kontrol Grafikleri Uygulamaları. Sosyal Bilimler Araştırma Dergisi, 7 (1), 114-135
  • [19]Eygü, H. (2014), Çok Değişkenli İstatistiksel Kalite Kontrolünde Sıralı Küme Örnekleme Yönteminin Kullanılması: Çimento Sanayinde Bir Uygulama, (Yayımlanmamış Doktora Tezi), Atatürk Üniversitesi Sosyal Bilimler Enstitüsü, Erzurum.
  • [20]Alt, F.B. (1985). Multivariate Quality Control, The Encyclopedia of Statistical Sciences, Wiley, New York, 110-122.
  • [21]Montgomery, D.C. ve Wadsworth, H.M. (1972), Some Technques for Multivariate Quality Control Applications. In ASQC Technical Conference Transactions, Washington.
  • [22]Alt, F.B. ve Smıth, N.D. (1988), Multivariate Process Control. In Handbook of Statistics (eds.P. R. Krishnaiah and C. R. Rao), 333-351. Elsevier.
  • [23]Lowry,C.A., Woodall, W.H., Champ, C.W. ve Rigdon, S.E., (1992), “A multivariate exponentially weighted moving average control chart”, Technometrics, 34(1), 46-53.
  • [24]Reynolds, M. R. ve Cho, G. (2006), "Multivariate control charts for monitoring the mean vector and covariance matrix". Journal of Quality Technology 38(3), 230-253.
  • [25]Hotelling, H. (1947). Multivariate Quality Control, İllustrated by the Air Testing of Sample Bombsights, in Techniques of Statistical Analysis, Mc-Graw Hill, New York.
  • [26]Montgomery, D.C. (2013). Introduction to Statistical Quality Control (6th Edition), John Wiley, New York.
  • [27]Runger, G.C. (1996), “Projections and the U² Multivariate Control Chart.” Journal of Quality Technology 28(3), 313-319.
  • [28]Barton, R.R., ve Gonzalez-Barreto, D.R. (1996). “Process oriented basis representations for multivariate process diagnostics”, Quality Engineering, 9, 107-118.
  • [29]Mantrıpragada, R. ve Whıtney, D.E. (1999), Modeling and Controlling Variation Propagation in Mechanical Assemblies Using State Transition Models. IEEE Transactions on Robotics and Automation 15(1), 124-140.
  • [30]Jin, J. ve Shi, J. (1999). "State space modeling of sheet metal assembly for dimensional control", Journal of Manufacturing Science and Engineering-Transactions of the ASME, 121(4), 756-762.
  • [31]Dıng, Y., Ceglarek, D. ve Shı, J. (2000). “Modeling and Diagnosis of Multistage Manufacturing Processes: Part I: State Space Model.” In Japan/USA Symposium on Flexible Automation, Ann Arbor, Michigan, JUSFA-13146.
  • [32]Zhou, S., Huang, Q. ve Shı, J. (2003), “State Space Modeling of Dimensional Variation Propagation in Multistage Machining Process Using Differential Motion Vectors”. IEEE Transactions on Robotics and Automation 19(2), 296-309.
  • [33]Djurdjanovıc, D. ve Nı, J. (2001).”Linear State Space Modeling of Dimensional Machining Errors”. Transactions of NAMRI/SME XXIX, 541-548.
  • [34]Huang, Q.. Zhou, N. ve Shı, J. (2000), Stream of Variation Modeling and Diagnosis of Multistation Machining Processes. American Society of Mechanical Engineers, Manufacturing Engineering Division, MED 11, 81-88.
  • [35]Espada Colon, H.I. ve Gonzalez-Barreto, D.R. (1997). “Component registration diagnosis for printed circuit boards using process-oriented basis elements”, Computers and Industrial Engineering, 33, 389-392.
  • [36] Gong, L., Jwo, W. ve Tang, K. (1997). “Using on-line sensors in statistical process control”, Management Science, 43, 1017-1028.
  • [37]Singh, R. ve Gilbreath, G. (2002). “A real-time information system for multivariate statistical process control”, International Journal of Production Economics, 75, 161-172.
  • [38]Koçer, B. ve Bi̇rgören, B . (2010). Approaches For Problem Dıagnosıs Vıa Statıstıcal Process Control Charts . Gazi University Journal of Science , 17 (4) , 59-69.
  • [39]Montgomery, D. C. (2009). Introduction to Statistical Quality Control (5th Edition) John Wiley, New York.
  • [40]Runger, G.C., Barton, R.R., del Castillo, E. ve Woodall, W.H. (2007). "Optimal monitoring of multivariate data for fault patterns", Journal of Quality Technology, 39(2), 159-172.
  • [41]Apley, D.W. ve Shi, J. (1998). “Diagnosis of multiple fixture faults in panel assembly”, ASME Journal of Manufacturing Science and Engineering, 120, 793-801.
  • [42]Lee, H.Y. ve Apley, D.W. (2004). “Diagnosing manufacturing variation using second-order and fourth-order statistics”. International Journal of Flexible Manufacturing Systems, 16, 45–64.
  • [43]Yang, K., He, Y. ve Xie, W. (1994). “Statistical diagnosis and analysis techniques: a multivariate statistical study for an automotive door assembly process”. Quality Engineering, 7, 1–29.
  • [44]Ceglarek, D., Shi, J. ve Wu, S.M. (1994). "A knowledge-based diagnostic approach for the launch of the auto-body assembly process", Journal of Engineering for Industry, 116(4), 491-499.
  • [45]Mason, R.L. Chou, Y.M. ve Young. J.C. (2001). “Applying Hotelling’s T² statistic to batch processes”, Journal of Quality Technology, 33, 466–479.
  • [46]Stoumbos, Z.G., Reynolds, M.R., Ryan, T.P. ve Woodall, W.H. (2000). “The state of statistical process control as we proceed into the 21st century, Journal of the American Statistical Association, 95, 992-998.
  • [47]Fuchs, C., ve Kenett, R.S. (1998). Multivariate Quality Control: Theory and Applications, Marcel Dekker, New York.
  • [48]Murphy, B.J. (1987). “Selecting out of control variables with the T² multivariate quality control procedures”, The Statistician, 36: 571-583.
  • [49]Doganaksoy, N., Faltin, F.W. ve Tucker, W.T. (1991), “Identification of out of control quality characteristics in a multivariate manufacturing enviroment”, Communications in Statistics–Theory and Methods, 20(9), 27-75.
  • [50]Mason, R. L., Tracy, N. D. ve Young, J. C. (1995). “Decomposition of T² for multivariate control chart interpretation”, Journal of Quality Technology, 27(2), 99-1108.
  • [51]Nedumaran, G. ve Pignatiello, J.J. (1998). “Diagnosing signals from T² and χ² multivariate control charts”, Journal of Quality Engineering, 10, 657-667.
  • [52]Birgören, B. (2000). “Çok boyutlu kalite kontrolde T² sinyallerinin scheffe tipi aralıklarla yorumlanması”, İstatistik Sempozyumu, Bildiriler Kitabı, 347-358.
  • [53]Maravelakis, P. E., Bersimis, S., Panaretos, J. ve Psarakis, S. (2002). “Identifying the out of control variable in multivariate control”, Communications in Statistics-Theory and Methods, 31: 2391-2408.
  • [54]Mason, R.L., Chou, Y.M., Sullivan, J.H., Stoumbos, Z.G. ve Young, J.C. (2003). "Systematic patterns in T² charts", Journal of Quality Technology, 35, 47-58.
  • [55]Orçanlı K., Oktay E. ve Birgören B. (2017). “The Effects of Covariance Over the Residuals of Process Oriented Basis Representation in Mulivariate Quality Control”, Social Sciences Research Journal, 6 (2), 20-40.
  • [58]Apley, D.W. ve Lee, H.Y. (2003). "Simultaneous identification of premodeled and unmodeled variation pattern", Journal of Quality Technology, 42(1), 36.
  • [59]Apley, D. W. ve Shi, J. (2001). “A factor-analysis method for diagnosing variability in multivariate manufacturing processes”. Technometrics, 43, 84–95.
  • [60]Jin, N. ve Zhou, S. (2006). "Data-driven variation source ıdentification of manufacturing processes based on eigenspace comparison", Naval Search Logistics, 55:383–396.
  • [61]Ding, Y., Gupta, A. ve Apley, D. (2004). “Singularity ıssues in fixture fault diagnosis for multi-station assembly systems”, ASME Journal of Manufacturing Science and Engineering, 126, 200–210.
  • [62]Huang, Q. ve Shi, J. (2004). “Variation transmission analysis and diagnosis of multi-operational machining processes”. IIE Transactions on Quality and Reliability, 36, 807–815.
  • [63]Birgören, B. (1998). Multivariate Statistical Process Control for Quality Diagnostics and Applications to Process Oriented Basis Representations, (Ph.D. Thesis), PennState University, Pennsylvania.
  • [64]Birgören, B. (2004), "A method for problem dıagnosıs ın multıvarıate qualıty control: constraıned solutıon spaces for process orıented basıs representatıons", Teknoloji, 7(1), 19-28.
  • [65]Colon, E. (1998) “Component registration diagnosis for printed circuit boards using process-oriented basis elements, Computers and Industrial Engineering, 33, 389-392.
  • [66]Padilla, V.O. (2005), Process Oriented Basis Estimation in Presence of Non-orthogonal Basis Elements, (Master Thesis), Unıversity of Puerto Rıco, Puerto Rıco.
  • [67]Schmitt A.J., Marcus, A. ve Barton R. (2002). "Benefit analysis of process-oriented basis representation as a method of multivariate statistical process control". IIE 2002, Conference Proceedings.
  • [68]Barton, R.R. ve Gonzalez-Barreto, D.R. (1999). “Process-oriented basis representations: linking manufacturing process design and diagnosis”, Proc. Euro. Conf. Con. Eng., 9, 109–114.
  • [69]Montgomery, D.C., Peck, E.A. ve Vining, G.G. (2012). Introduction to Linear Regression Analysis, John Wiley & Sons, New York.
  • [70]Orçanlı, K. (2017), Çok değişkenli Kontrol Grafikleri ve Yapay Sinir Ağları ile Döküm Sanayinde Bir İstatistiksel Süreç Kontrolü, Yayımlanmamış Doktora Tezi, Atatürk Üniversitesi Sosyal Bilimler Enstitüsü, Erzurum.
  • [71]Hoerl, A.E. ve Kennard, R.W. (1970). “Ridge regression: Biased estimation for nonorthogonal problems", Technometrics, 12, 55-67.
  • [72]Gunst, R.F. ve Mason, R.L. (1997). "Biased estimation in regression: An evaluation using mean squared error", Journal of the American Statistical Association, 72(359), 616-628.

Döküm Sanayinde Süreç Tabanlı Temel Gösterimleri İle İstatistiksel Süreç Kontrolü

Yıl 2021, Cilt: 9 Sayı: 1, 134 - 158, 29.01.2021

Kenan Orçanlı

https://doi.org/10.21541/apjes.720051
Cited By: 1

Öz

Döküm sanayinde süreç kontrolünde, kalite karakteristiği olarak genellikle üretilen ürünün çap, kalınlık, yoğunluk gibi özellikleri ele alınmaktadır. Söz konusu kalite karakteristikleri, genellikle kalite kontrol grafikleri ile izlenerek süreci etkileyen özel nedenlerin varlığı ortaya konulmaya çalışılmaktadır. Ancak döküm sanayinde kalite karakteristikleri olarak üretilen ürünün özellikleri yerine ürünü oluşturan elementlerin oranları da kabul edilebilmektedir. Çünkü ürünün içeriğini oluşturan elementlerin oranlarının, belirli sınırlar arasında olması istenmekte ve genellikle değişkenlik göstermektedir. Kalite karakteristikleri olarak seçilebilen metal oranları, ürünün özelliklerinde olduğu gibi kalite kontrol grafikleri ile izlenebilmekte ancak kontrol dışı sinyallerin yorumlanması yeterince yapılamamaktadır. Dolayısıyla sorunun çözümünde kalite kontrol grafiklerinin yerine literatürde yer alan süreç tabanlı temel gösterimleri metodu kullanılabilir. Yapılan literatür araştırması neticesinde, süreç tabanlı temel gösterimleri metodunun, imalat sanayinde geometrik sapmaların modellenmesinde başarılı bir şekilde kullanıldığı ancak proses (kimya, petro-kimya, döküm vb.) endüstrilerinde ve birbiriyle ilişki içinde olan kalite karakteristiklerin bulunduğu çok değişkenli endüstriyel üretim süreçlerinde uygulamasının olmadığı tespit edilmiştir. Bu kapsamda yapılan bu çalışmanın amacı, döküm sanayinde süreç kontrolünde, metal alaşım oranlarının kalite karakteristiği olarak kullanılabileceğini ve yine süreç tabanlı temel gösterimleri metodunun uygulanabileceğini göstermektir. Çalışmada kullanılan veriler, 01 Ocak 2015-31 Mart 2015 tarihleri arasında Kırıkkale ilinde yerleşik Makine ve Kimya Endüstrisi Kurumu’na bağlı Pirinç Fabrikası Müdürlüğünün üretim biriminden elde edilmiştir. Kontrol grafiklerinin oluşturulmasında MINITAB paket programında yer alan modül kullanılmıştır. Çalışmanın sonunda; döküm sanayinde uygulanan süreç kontrolünde, kalite karakteristiği olarak üretilen ürünü oluşturan element oranlarının da seçilebileceği ve bu şekilde seçilen kalite karakteristiklerin süreç tabanlı temel gösterimleri yöntemi ile izlenerek olumlu sonuçlar elde edilebileceği tespit edilmiştir. Çalışmada elde edilen bulgu ve sonuçların gerek ulusal gerekse uluslararası literatüre hem teorik ve hem de pratik katkı sağlayacağı düşünülmektedir.

Anahtar Kelimeler

Döküm Pirinç Süreç Tabanlı Temel Gösterimleri

Kaynakça

  • [1]Özel, S. (2005). Çok Değişkenli Kalite Kontrol Çizelgelerinin Döküm Sanayiinde Uygulanması, (Yayımlanmamış Yüksek Lisans Tezi), Kırıkkale Üniversitesi, Fen Bilimleri Enstitüsü, Kırıkkale.
  • [2]Özel, S. ve Birgören, B. (2007), “Çok değişkenli kalite kontrol çizelgelerinin döküm sanayiinde uygulanması”, Gazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, Cilt 22(4), 809-811.
  • [3]Energy and Environmental Profile of the U.S. Metalcasting Industry. (1999). U.S.Department of Energy Office of Industrial Technologies.
  • [4]Moseki, M. (2002). “Research into melting and casting of brass scrap for upliftment purposes”. Journal of the Southern African Institute of Mining and Metallurgy, 102(2), 109-114.
  • [5]Nur Hamizah, M. (2010). Investigation of Brass Microstructure and Mechanical Properties Using Metal Casting, (Ph.D. Thesis), Universiti Malaysia Pahang, Malaysia.
  • [6]Prıbulová A., Gengeľ P. ve Bartošová M. (2010). Odpady z výroby oceľových a liatinových odliatkov /prachy – ich charakteristika, vlastnosti a možnosti použitia, TUKE Košice.
  • [7]Saravanakumar, P. (2015). “A systematic approach on reducing scrap level using six sigma in Indian foundries”, International Journal of Emerging Researches in Engineering Science and Technology, 2, 12.
  • [8]Aran, A. (2007). Döküm Teknolojileri İmal Usulleri Ders Notları, İstanbul Teknik Üniversitesi, İstanbul.
  • [9]Mysik, R. K., Brusnitsyn, S. V. ve Sulitsin, A. V. (2020). Determination of Thermo-Physical and Physical Properties of Complex Alloyed Brass. Solid State Phenomena, 299, 442–446.
  • [10]Şakar ve diğerleri (2019), Leaded brass alloys for gamma-ray shielding applications, Radiation Physics and Chemistry, 159, 64–6969.
  • [11]Brady, G.S. (1991). Materials Handbook: An Encyclopedia for Purchasing Managers, Engineers, Executives, and Foremen, McGraw-Hill Book Company INC, New York.
  • [12]Akgün, O. (2000). Pirinç Alaşımlarının Hazırlanmasında Flaksların Etkisi, (Yüksek Lisans Tezi), İ.T.Ü. Fen Bilimleri Enstitüsü, İstanbul.
  • [13]Mindivan, H. (2001). Yüksek Mukavemetli Pirinçlerin Mikro Yapı ve Aşınma Özelliklerine Isıl İşlemin Etkisi, (Yayımlanmamış Yüksek Lisans Tezi), İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü, İstanbul.
  • [14]Sakallı, U.S. ve Birgoren, B. (2009). “A spreadsheet-based decision support tool for blending problems in brass casting industry”, Computers & Industrial Engineering, 56 (2): 724–735.
  • [15]Birgören, B. (2015), İstatistiksel Kalite Kontrolü, Nobel Akademik Yayıncılık, Ankara.
  • [16]Çolak, M. (2011). Döküm endüstrisinde ergitme endüksiyon ocakları ve spektral analiz hesaplamaları, http://www.demircelikstore.com/-1-3842-dokum-endustrisinde-ergitme-enduksiyon-ocaklari-ve-spektral-analiz-hesaplamalari.html, Erişim Tarihi:12.10.2016.
  • [17]Moment Expo, (2008), Makine ve Aksamları İhracatçıları Birliği Aylık Makine İhracatı ve Ticareti Dergisi, Sayı:06.
  • [18]Orçanlı, K, Bi̇rgören, B. ve Oktay, E. (2018). Döküm Sanayisinde Metal Alaşım Oranlarına Hotelling T² ve MEWMA Kontrol Grafikleri Uygulamaları. Sosyal Bilimler Araştırma Dergisi, 7 (1), 114-135
  • [19]Eygü, H. (2014), Çok Değişkenli İstatistiksel Kalite Kontrolünde Sıralı Küme Örnekleme Yönteminin Kullanılması: Çimento Sanayinde Bir Uygulama, (Yayımlanmamış Doktora Tezi), Atatürk Üniversitesi Sosyal Bilimler Enstitüsü, Erzurum.
  • [20]Alt, F.B. (1985). Multivariate Quality Control, The Encyclopedia of Statistical Sciences, Wiley, New York, 110-122.
  • [21]Montgomery, D.C. ve Wadsworth, H.M. (1972), Some Technques for Multivariate Quality Control Applications. In ASQC Technical Conference Transactions, Washington.
  • [22]Alt, F.B. ve Smıth, N.D. (1988), Multivariate Process Control. In Handbook of Statistics (eds.P. R. Krishnaiah and C. R. Rao), 333-351. Elsevier.
  • [23]Lowry,C.A., Woodall, W.H., Champ, C.W. ve Rigdon, S.E., (1992), “A multivariate exponentially weighted moving average control chart”, Technometrics, 34(1), 46-53.
  • [24]Reynolds, M. R. ve Cho, G. (2006), "Multivariate control charts for monitoring the mean vector and covariance matrix". Journal of Quality Technology 38(3), 230-253.
  • [25]Hotelling, H. (1947). Multivariate Quality Control, İllustrated by the Air Testing of Sample Bombsights, in Techniques of Statistical Analysis, Mc-Graw Hill, New York.
  • [26]Montgomery, D.C. (2013). Introduction to Statistical Quality Control (6th Edition), John Wiley, New York.
  • [27]Runger, G.C. (1996), “Projections and the U² Multivariate Control Chart.” Journal of Quality Technology 28(3), 313-319.
  • [28]Barton, R.R., ve Gonzalez-Barreto, D.R. (1996). “Process oriented basis representations for multivariate process diagnostics”, Quality Engineering, 9, 107-118.
  • [29]Mantrıpragada, R. ve Whıtney, D.E. (1999), Modeling and Controlling Variation Propagation in Mechanical Assemblies Using State Transition Models. IEEE Transactions on Robotics and Automation 15(1), 124-140.
  • [30]Jin, J. ve Shi, J. (1999). "State space modeling of sheet metal assembly for dimensional control", Journal of Manufacturing Science and Engineering-Transactions of the ASME, 121(4), 756-762.
  • [31]Dıng, Y., Ceglarek, D. ve Shı, J. (2000). “Modeling and Diagnosis of Multistage Manufacturing Processes: Part I: State Space Model.” In Japan/USA Symposium on Flexible Automation, Ann Arbor, Michigan, JUSFA-13146.
  • [32]Zhou, S., Huang, Q. ve Shı, J. (2003), “State Space Modeling of Dimensional Variation Propagation in Multistage Machining Process Using Differential Motion Vectors”. IEEE Transactions on Robotics and Automation 19(2), 296-309.
  • [33]Djurdjanovıc, D. ve Nı, J. (2001).”Linear State Space Modeling of Dimensional Machining Errors”. Transactions of NAMRI/SME XXIX, 541-548.
  • [34]Huang, Q.. Zhou, N. ve Shı, J. (2000), Stream of Variation Modeling and Diagnosis of Multistation Machining Processes. American Society of Mechanical Engineers, Manufacturing Engineering Division, MED 11, 81-88.
  • [35]Espada Colon, H.I. ve Gonzalez-Barreto, D.R. (1997). “Component registration diagnosis for printed circuit boards using process-oriented basis elements”, Computers and Industrial Engineering, 33, 389-392.
  • [36] Gong, L., Jwo, W. ve Tang, K. (1997). “Using on-line sensors in statistical process control”, Management Science, 43, 1017-1028.
  • [37]Singh, R. ve Gilbreath, G. (2002). “A real-time information system for multivariate statistical process control”, International Journal of Production Economics, 75, 161-172.
  • [38]Koçer, B. ve Bi̇rgören, B . (2010). Approaches For Problem Dıagnosıs Vıa Statıstıcal Process Control Charts . Gazi University Journal of Science , 17 (4) , 59-69.
  • [39]Montgomery, D. C. (2009). Introduction to Statistical Quality Control (5th Edition) John Wiley, New York.
  • [40]Runger, G.C., Barton, R.R., del Castillo, E. ve Woodall, W.H. (2007). "Optimal monitoring of multivariate data for fault patterns", Journal of Quality Technology, 39(2), 159-172.
  • [41]Apley, D.W. ve Shi, J. (1998). “Diagnosis of multiple fixture faults in panel assembly”, ASME Journal of Manufacturing Science and Engineering, 120, 793-801.
  • [42]Lee, H.Y. ve Apley, D.W. (2004). “Diagnosing manufacturing variation using second-order and fourth-order statistics”. International Journal of Flexible Manufacturing Systems, 16, 45–64.
  • [43]Yang, K., He, Y. ve Xie, W. (1994). “Statistical diagnosis and analysis techniques: a multivariate statistical study for an automotive door assembly process”. Quality Engineering, 7, 1–29.
  • [44]Ceglarek, D., Shi, J. ve Wu, S.M. (1994). "A knowledge-based diagnostic approach for the launch of the auto-body assembly process", Journal of Engineering for Industry, 116(4), 491-499.
  • [45]Mason, R.L. Chou, Y.M. ve Young. J.C. (2001). “Applying Hotelling’s T² statistic to batch processes”, Journal of Quality Technology, 33, 466–479.
  • [46]Stoumbos, Z.G., Reynolds, M.R., Ryan, T.P. ve Woodall, W.H. (2000). “The state of statistical process control as we proceed into the 21st century, Journal of the American Statistical Association, 95, 992-998.
  • [47]Fuchs, C., ve Kenett, R.S. (1998). Multivariate Quality Control: Theory and Applications, Marcel Dekker, New York.
  • [48]Murphy, B.J. (1987). “Selecting out of control variables with the T² multivariate quality control procedures”, The Statistician, 36: 571-583.
  • [49]Doganaksoy, N., Faltin, F.W. ve Tucker, W.T. (1991), “Identification of out of control quality characteristics in a multivariate manufacturing enviroment”, Communications in Statistics–Theory and Methods, 20(9), 27-75.
  • [50]Mason, R. L., Tracy, N. D. ve Young, J. C. (1995). “Decomposition of T² for multivariate control chart interpretation”, Journal of Quality Technology, 27(2), 99-1108.
  • [51]Nedumaran, G. ve Pignatiello, J.J. (1998). “Diagnosing signals from T² and χ² multivariate control charts”, Journal of Quality Engineering, 10, 657-667.
  • [52]Birgören, B. (2000). “Çok boyutlu kalite kontrolde T² sinyallerinin scheffe tipi aralıklarla yorumlanması”, İstatistik Sempozyumu, Bildiriler Kitabı, 347-358.
  • [53]Maravelakis, P. E., Bersimis, S., Panaretos, J. ve Psarakis, S. (2002). “Identifying the out of control variable in multivariate control”, Communications in Statistics-Theory and Methods, 31: 2391-2408.
  • [54]Mason, R.L., Chou, Y.M., Sullivan, J.H., Stoumbos, Z.G. ve Young, J.C. (2003). "Systematic patterns in T² charts", Journal of Quality Technology, 35, 47-58.
  • [55]Orçanlı K., Oktay E. ve Birgören B. (2017). “The Effects of Covariance Over the Residuals of Process Oriented Basis Representation in Mulivariate Quality Control”, Social Sciences Research Journal, 6 (2), 20-40.
  • [58]Apley, D.W. ve Lee, H.Y. (2003). "Simultaneous identification of premodeled and unmodeled variation pattern", Journal of Quality Technology, 42(1), 36.
  • [59]Apley, D. W. ve Shi, J. (2001). “A factor-analysis method for diagnosing variability in multivariate manufacturing processes”. Technometrics, 43, 84–95.
  • [60]Jin, N. ve Zhou, S. (2006). "Data-driven variation source ıdentification of manufacturing processes based on eigenspace comparison", Naval Search Logistics, 55:383–396.
  • [61]Ding, Y., Gupta, A. ve Apley, D. (2004). “Singularity ıssues in fixture fault diagnosis for multi-station assembly systems”, ASME Journal of Manufacturing Science and Engineering, 126, 200–210.
  • [62]Huang, Q. ve Shi, J. (2004). “Variation transmission analysis and diagnosis of multi-operational machining processes”. IIE Transactions on Quality and Reliability, 36, 807–815.
  • [63]Birgören, B. (1998). Multivariate Statistical Process Control for Quality Diagnostics and Applications to Process Oriented Basis Representations, (Ph.D. Thesis), PennState University, Pennsylvania.
  • [64]Birgören, B. (2004), "A method for problem dıagnosıs ın multıvarıate qualıty control: constraıned solutıon spaces for process orıented basıs representatıons", Teknoloji, 7(1), 19-28.
  • [65]Colon, E. (1998) “Component registration diagnosis for printed circuit boards using process-oriented basis elements, Computers and Industrial Engineering, 33, 389-392.
  • [66]Padilla, V.O. (2005), Process Oriented Basis Estimation in Presence of Non-orthogonal Basis Elements, (Master Thesis), Unıversity of Puerto Rıco, Puerto Rıco.
  • [67]Schmitt A.J., Marcus, A. ve Barton R. (2002). "Benefit analysis of process-oriented basis representation as a method of multivariate statistical process control". IIE 2002, Conference Proceedings.
  • [68]Barton, R.R. ve Gonzalez-Barreto, D.R. (1999). “Process-oriented basis representations: linking manufacturing process design and diagnosis”, Proc. Euro. Conf. Con. Eng., 9, 109–114.
  • [69]Montgomery, D.C., Peck, E.A. ve Vining, G.G. (2012). Introduction to Linear Regression Analysis, John Wiley & Sons, New York.
  • [70]Orçanlı, K. (2017), Çok değişkenli Kontrol Grafikleri ve Yapay Sinir Ağları ile Döküm Sanayinde Bir İstatistiksel Süreç Kontrolü, Yayımlanmamış Doktora Tezi, Atatürk Üniversitesi Sosyal Bilimler Enstitüsü, Erzurum.
  • [71]Hoerl, A.E. ve Kennard, R.W. (1970). “Ridge regression: Biased estimation for nonorthogonal problems", Technometrics, 12, 55-67.
  • [72]Gunst, R.F. ve Mason, R.L. (1997). "Biased estimation in regression: An evaluation using mean squared error", Journal of the American Statistical Association, 72(359), 616-628.
Toplam 70 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Kenan Orçanlı 0000-0001-5716-4004

Yayımlanma Tarihi 29 Ocak 2021
Gönderilme Tarihi 14 Nisan 2020
Yayımlandığı Sayı Yıl 2021 Cilt: 9 Sayı: 1

Kaynak Göster

IEEE K. Orçanlı, “Döküm Sanayinde Süreç Tabanlı Temel Gösterimleri İle İstatistiksel Süreç Kontrolü”, APJES, c. 9, sy. 1, ss. 134–158, 2021, doi: 10.21541/apjes.720051.

Cited By

İSTATİSTİKSEL SÜREÇ KONTROLÜ İLE İLETKEN ENDÜSTRİSİNDE KALİTE İYİLEŞTİRME UYGULAMASI
Uludağ University Journal of The Faculty of Engineering
https://doi.org/10.17482/uumfd.1299193
 Kapak Resmi İndir