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A PROPOSED RESPONSE SURFACE-BASED ROBUST DESIGN MODEL FOR QUALITY ENGINEERING PROBLEMS

Yıl 2020, , 195 - 205, 30.01.2020
https://doi.org/10.28948/ngumuh.547931

Öz



  The aim of robust design models is to reduce the variability reduction
as small as possible. The process bias defined as a difference between the
desired target value and the process mean is an important concern for quality
engineering problems. In addition, the selection of different variability
measures may also change optimal operating conditions for a response variable.
Therefore, this paper is three-fold. One, another view of dual response model
is proposed with the three different variability measures in order to determine
optimum robust design solutions for input variables while minimizing the
process bias. Two, the linearization of constraints is performed using the
sequential quadratic programming method as an effective optimization method.
Three, a printing process from the literature is conducted to obtain the best
optimal settings for input variables. Finally, the results of the proposed
model show approximately % 16 more variance reduction than traditional models
.

Kaynakça

  • [1] TAGUCHI, G., Introduction to Quality Engineering, UNIPUB/Kraus International, New York, US, 1986.
  • [2] VINING, G.G., MYERS, R.H., “Combining Taguchi and Response Surface Philosophies: A Dual Response Approach”, Journal of Quality Technology, 22, 38-45, 1990.
  • [3] DEL CASTILLO, E., MONTGOMERY, D.C., “A Nonlinear Programming Solution to the Dual Response Problem”, Journal of Quality Technology, 25, 199-204, 1993.
  • [4] LIN, D.K.J., TU, W., “Dual Response Surface Optimization”, Journal of Quality Technology, 27, 34-39, 1995.
  • [5] CHO, B.R., PHILIPS, M.D., KAPUR, K.C., “Quality Improvement by RSM Modelling for Robust Design”, Proceedings of the Fifth Industrial Engineering Research Conference, 650-655. Minnesota, US, 1996.
  • [6] COPELAND, K.A.F., NELSON, P.R., “Dual Response Optimization via Direct Function Minimization”, Journal of Quality Technology, 28, 331-336, 1996.
  • [7] KIM, K.J., LIN, D.K., “Dual Response Surface Optimization: A Fuzzy Modelling Approach”, Journal of Quality Technology, 30, 1-10, 1998.
  • [8] CHO, B.R., KIM, Y.J., KIMBLER, D.L., PHILLIPS, M.D., “An Integrated Joint Optimization Procedure for Robust and Tolerance Design”, International Journal of Production Research, 38, 2309-2325, 2000.
  • [9] TANG, L.C., XU, K., “A Unified Approach for Dual Response Surface Optimization”, Journal of Quality Technology, 34, 437-447, 2002.
  • [10] KIM, Y.J., CHO, B.R., “Development of Priority-based Robust Design”, Quality Engineering, 14, 355-363, 2002.
  • [11] KÖKSOY, O., DOGANAKSOY, N., “Joint Optimization of Mean and Standard Deviation Using Response Surface Methods”, Journal of Quality Technology, 35, 239-252, 2003.
  • [12] DING, R., LIN, D.K.J., WEI, D., “Dual-Response Surface Optimization: A Weighted MSE Approach”, Quality Engineering, 16, 377-385, 2004.
  • [13] ROMANO, D., VARETTO, M., VICARIO, G., “Multiresponse Robust Design: A General Framework based on Combined Array”, Journal of Quality Technology, 36, 27-37, 2004.
  • [14] SHIN, S., CHO, B.R., “Bias-Specified Robust Design Optimization and Its Analytical Solutions”, Computers & Industrial Engineering, 48, 129-140, 2005.
  • [15] Köksoy, O., “Multiresponse Robust Design: Mean Square Error (MSE) Criterion”, Applied Mathematics and Computation, 175, 1716-1729, 2006.
  • [16] PARK, H., PARK, S.H., KONG, H.B., LEE, I., “Weighted Sum MSE Minimization Under per-BS Power Constraint for Network MIMO Systems”, Communications Letters, IEEE, 16, 360-363, 2012.
  • [17] ROBINSON, T.J., WULFF, S.S., MONTGOMERY, D.C., KHURI, A.I., “Robust Parameter Design Using Generalized Linear Mixed Models”, Journal of Quality Technology, 38, 65-75, 2006.
  • [18] SHAIBU, A.B., CHO, B.R., “Another View of Dual Response Surface Modeling and Optimization in Robust Parameter Design”, The International Journal of Advanced Manufacturing Technology, 41, 631-641, 2009.
  • [19] Costa, N.R.P., “Simultaneous Optimization of Mean and Standard Deviation”, Quality Engineering, 22, 140-149, 2010.
  • [20] OZDEMIR, A., CHO, B.R., “A Nonlinear Integer Programming Approach to Solving the Robust Parameter Design Optimization Problem”, Quality and Reliability Engineering International, 32, 2859-2870, 2016.
  • [21] Box, G.E.P., Draper, N.R., Empirical Model Building and Response Surfaces, Wiley, New York, US, 1987.
  • [22] Goethals, P., Aragon, L., Cho, B.R., “Experimental Investigations of Estimated Response Surface Functions with Different Variability Measures”, International Journal of Experimental Design and Process Optimisation, 1, 123-163, 2009.
  • [23] Ruszczyński, A.P., Nonlinear Optimization, Princeton University Press, New Jersey, US, 2006.
  • [24] Fishback, P.E., Linear and Nonlinear Programming with Maple: An Interactive, Applications-Based Approach, Chapman and Hall/CRC, Florida, US, 2009.
  • [25] SAS Institute, Using JMP 11, SAS Institute, Cary, NC, US, 2013.
  • [26] Maple, V., Waterloo Maple Software. University of Waterloo, Version, 17, Waterloo, ON, Canada, 2013.

KALİTE MÜHENDİSLİĞİ PROBLEMLERİ İÇİN ÖNERİLEN BİR YANIT YÜZEYİ TABANLI SAĞLAM TASARIM MODELİ

Yıl 2020, , 195 - 205, 30.01.2020
https://doi.org/10.28948/ngumuh.547931

Öz



  Sağlam tasarım
modellerinin amacı değişkenliği mümkün olduğu kadar azaltmaktır. İstenen hedef
değer ile işlem ortalaması arasındaki fark olarak tanımlanan işlem yanlılığı,
kalite mühendisliği problemleri için önemli bir husustur. Ek olarak, farklı
değişkenlik ölçümlerinin seçimi bir yanıt değişkeni için en uygun çalışma
koşullarını da değiştirebilir. Bu nedenle, bu makalenin üç amaçlıdır.
Birincisi, yanıt modelinin bir başka görünümü işlem yanlılığını en aza
indirirken girdi değişkenleri için en iyi sağlam tasarım çözümlerini belirlemek
amacıyla üç farklı değişkenlik ölçüsüyle önerilmiştir. İkincisi, kısıtlamaların
doğrusallaştırılması, etkin bir optimizasyon yöntemi olarak sıralı ikinci
dereceden programlama yöntemi kullanılarak gerçekleştirilir. Üçüncüsü, girdi değişkenleri
için en uygun ayarları elde etmek için literatürden bir baskı işlemi süreci
araştırılmıştır. Son olarak, önerilen modelin sonuçları geleneksel modellere
göre yaklaşık % 16 daha fazla varyansın azaldığını gösterir.




Kaynakça

  • [1] TAGUCHI, G., Introduction to Quality Engineering, UNIPUB/Kraus International, New York, US, 1986.
  • [2] VINING, G.G., MYERS, R.H., “Combining Taguchi and Response Surface Philosophies: A Dual Response Approach”, Journal of Quality Technology, 22, 38-45, 1990.
  • [3] DEL CASTILLO, E., MONTGOMERY, D.C., “A Nonlinear Programming Solution to the Dual Response Problem”, Journal of Quality Technology, 25, 199-204, 1993.
  • [4] LIN, D.K.J., TU, W., “Dual Response Surface Optimization”, Journal of Quality Technology, 27, 34-39, 1995.
  • [5] CHO, B.R., PHILIPS, M.D., KAPUR, K.C., “Quality Improvement by RSM Modelling for Robust Design”, Proceedings of the Fifth Industrial Engineering Research Conference, 650-655. Minnesota, US, 1996.
  • [6] COPELAND, K.A.F., NELSON, P.R., “Dual Response Optimization via Direct Function Minimization”, Journal of Quality Technology, 28, 331-336, 1996.
  • [7] KIM, K.J., LIN, D.K., “Dual Response Surface Optimization: A Fuzzy Modelling Approach”, Journal of Quality Technology, 30, 1-10, 1998.
  • [8] CHO, B.R., KIM, Y.J., KIMBLER, D.L., PHILLIPS, M.D., “An Integrated Joint Optimization Procedure for Robust and Tolerance Design”, International Journal of Production Research, 38, 2309-2325, 2000.
  • [9] TANG, L.C., XU, K., “A Unified Approach for Dual Response Surface Optimization”, Journal of Quality Technology, 34, 437-447, 2002.
  • [10] KIM, Y.J., CHO, B.R., “Development of Priority-based Robust Design”, Quality Engineering, 14, 355-363, 2002.
  • [11] KÖKSOY, O., DOGANAKSOY, N., “Joint Optimization of Mean and Standard Deviation Using Response Surface Methods”, Journal of Quality Technology, 35, 239-252, 2003.
  • [12] DING, R., LIN, D.K.J., WEI, D., “Dual-Response Surface Optimization: A Weighted MSE Approach”, Quality Engineering, 16, 377-385, 2004.
  • [13] ROMANO, D., VARETTO, M., VICARIO, G., “Multiresponse Robust Design: A General Framework based on Combined Array”, Journal of Quality Technology, 36, 27-37, 2004.
  • [14] SHIN, S., CHO, B.R., “Bias-Specified Robust Design Optimization and Its Analytical Solutions”, Computers & Industrial Engineering, 48, 129-140, 2005.
  • [15] Köksoy, O., “Multiresponse Robust Design: Mean Square Error (MSE) Criterion”, Applied Mathematics and Computation, 175, 1716-1729, 2006.
  • [16] PARK, H., PARK, S.H., KONG, H.B., LEE, I., “Weighted Sum MSE Minimization Under per-BS Power Constraint for Network MIMO Systems”, Communications Letters, IEEE, 16, 360-363, 2012.
  • [17] ROBINSON, T.J., WULFF, S.S., MONTGOMERY, D.C., KHURI, A.I., “Robust Parameter Design Using Generalized Linear Mixed Models”, Journal of Quality Technology, 38, 65-75, 2006.
  • [18] SHAIBU, A.B., CHO, B.R., “Another View of Dual Response Surface Modeling and Optimization in Robust Parameter Design”, The International Journal of Advanced Manufacturing Technology, 41, 631-641, 2009.
  • [19] Costa, N.R.P., “Simultaneous Optimization of Mean and Standard Deviation”, Quality Engineering, 22, 140-149, 2010.
  • [20] OZDEMIR, A., CHO, B.R., “A Nonlinear Integer Programming Approach to Solving the Robust Parameter Design Optimization Problem”, Quality and Reliability Engineering International, 32, 2859-2870, 2016.
  • [21] Box, G.E.P., Draper, N.R., Empirical Model Building and Response Surfaces, Wiley, New York, US, 1987.
  • [22] Goethals, P., Aragon, L., Cho, B.R., “Experimental Investigations of Estimated Response Surface Functions with Different Variability Measures”, International Journal of Experimental Design and Process Optimisation, 1, 123-163, 2009.
  • [23] Ruszczyński, A.P., Nonlinear Optimization, Princeton University Press, New Jersey, US, 2006.
  • [24] Fishback, P.E., Linear and Nonlinear Programming with Maple: An Interactive, Applications-Based Approach, Chapman and Hall/CRC, Florida, US, 2009.
  • [25] SAS Institute, Using JMP 11, SAS Institute, Cary, NC, US, 2013.
  • [26] Maple, V., Waterloo Maple Software. University of Waterloo, Version, 17, Waterloo, ON, Canada, 2013.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Endüstri Mühendisliği
Bölüm Endüstri Mühendisliği
Yazarlar

Akın Özdemir 0000-0002-1716-6694

Yayımlanma Tarihi 30 Ocak 2020
Gönderilme Tarihi 1 Nisan 2019
Kabul Tarihi 4 Aralık 2019
Yayımlandığı Sayı Yıl 2020

Kaynak Göster

APA Özdemir, A. (2020). A PROPOSED RESPONSE SURFACE-BASED ROBUST DESIGN MODEL FOR QUALITY ENGINEERING PROBLEMS. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 9(1), 195-205. https://doi.org/10.28948/ngumuh.547931
AMA Özdemir A. A PROPOSED RESPONSE SURFACE-BASED ROBUST DESIGN MODEL FOR QUALITY ENGINEERING PROBLEMS. NÖHÜ Müh. Bilim. Derg. Ocak 2020;9(1):195-205. doi:10.28948/ngumuh.547931
Chicago Özdemir, Akın. “A PROPOSED RESPONSE SURFACE-BASED ROBUST DESIGN MODEL FOR QUALITY ENGINEERING PROBLEMS”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 9, sy. 1 (Ocak 2020): 195-205. https://doi.org/10.28948/ngumuh.547931.
EndNote Özdemir A (01 Ocak 2020) A PROPOSED RESPONSE SURFACE-BASED ROBUST DESIGN MODEL FOR QUALITY ENGINEERING PROBLEMS. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 9 1 195–205.
IEEE A. Özdemir, “A PROPOSED RESPONSE SURFACE-BASED ROBUST DESIGN MODEL FOR QUALITY ENGINEERING PROBLEMS”, NÖHÜ Müh. Bilim. Derg., c. 9, sy. 1, ss. 195–205, 2020, doi: 10.28948/ngumuh.547931.
ISNAD Özdemir, Akın. “A PROPOSED RESPONSE SURFACE-BASED ROBUST DESIGN MODEL FOR QUALITY ENGINEERING PROBLEMS”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 9/1 (Ocak 2020), 195-205. https://doi.org/10.28948/ngumuh.547931.
JAMA Özdemir A. A PROPOSED RESPONSE SURFACE-BASED ROBUST DESIGN MODEL FOR QUALITY ENGINEERING PROBLEMS. NÖHÜ Müh. Bilim. Derg. 2020;9:195–205.
MLA Özdemir, Akın. “A PROPOSED RESPONSE SURFACE-BASED ROBUST DESIGN MODEL FOR QUALITY ENGINEERING PROBLEMS”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, c. 9, sy. 1, 2020, ss. 195-0, doi:10.28948/ngumuh.547931.
Vancouver Özdemir A. A PROPOSED RESPONSE SURFACE-BASED ROBUST DESIGN MODEL FOR QUALITY ENGINEERING PROBLEMS. NÖHÜ Müh. Bilim. Derg. 2020;9(1):195-20.

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