Sürekli Kalite İyileştirme için Faktöriyel Tasarım Tabanlı Süreç Optimizasyonu

Abstract

Deneyin tasarımı, yeni bir süreç geliştirmek veya mevcut bir süreci geliştirmek için kilit bir rol oynar. Literatürde sürekli kalite iyileştirme için faktöriyel deneysel tasarımları kullanılmıştır. Bu makalede, tasarım faktörlerinin optimizasyonu için bir deney verisi analizi yapmak amacıyla faktöriyel deney tasarımına sahip yeni bir yöntem sunulmaktadır. Önerilen yöntem beş ana adıma sahiptir. İlk adım deney öncesi planlama ile ilgilidir. İkinci adım faktöriyel tasarıma sahip deneysel aşamadır. Üçüncü adım bir deneye ait verileri analiz eder. Daha sonra, tasarım faktörlerinin en uygun değerlerini elde etmek için faktöriyel tasarıma dayalı optimizasyon modeli ilk defa geliştirilmiştir. Son adım deneyden elde edilen sonuçları doğrulamak için sonuçlar ve tavsiyeler adımdır. Son olarak, güncel literatürdeki sayısal bir örnek için farklı hedef değerler kullanılarak karşılaştırma çalışmaları yapılmıştır. Ayrıca, önerilen faktöriyel tasarıma dayalı süreç optimizasyon modelinin belirtilen hedef değere göre daha fazla varyansı azaltabileceği sonucuna varılmıştır.

Keywords

• Kalite Mühendisliği
• Faktöriyel Deney Tasarımı
• Belirlenmiş Hedef Değer
• Süreç Optimizasyonu

Factorial Design-Based Process Optimization for Continuous Quality Improvement

Abstract

The design of the experiment plays a key role to develop a new process or improve an existing process. In the literature, factorial experimental designs are used for continuous quality improvement. This paper presents a novel methodology with a factorial experimental design in order to conduct an experiment data analysis for the optimization of design factors. The proposed methodology has five main steps. The first step is related to pre-experimental planning. The second step is the experimental phase with a factorial design. The third step analyzes data for an experiment. Next, a factorial design-based optimization model is firstly developed to get the optimal settings of design factors. The last step is the conclusions and recommendations step in order to validate the conclusions from the experiment. Finally, comparison studies are performed using the different target values for a numerical example from the current literature. In addition, it was concluded that the proposed factorial design-based process optimization model could reduce more variance based on the specified target value.

Keywords

• Quality Engineering
• Factorial Experimental Design
• Specified Target Value
• Process Optimization

References

1. Montgomery, D. C. (2012). Introduction to Statistical Quality Control. New York: John Wiley & Sons Inc., New York, USA.
2. Vining, G. G., & Myers, R. H. (1990). Combining Taguchi and response surface philosophies: a dual response approach. Journal of Quality Technology, 22(1), 38-45.
3. Del Castillo, E., & Montgomery, D. C. (1993). A nonlinear programming solution to the dual response problem. Journal of Quality Technology, 25(3), 199-204.
4. Lin, D. K., & Tu, W. (1995). Dual response surface optimization. Journal of Quality Technology, 27(1), 34-39.
5. Copeland, K. A., & Nelson, P. R. (1996). Dual response optimization via direct function minimization. Journal of Quality Technology, 28(3), 331-336.
6. Ames, A. E., Mattucci, N., Macdonald, S., Szonyi, G., & Hawkins, D. M. (1997). Quality loss functions for optimization across multiple response surfaces. Journal of Quality Technology, 29(3), 339-346.
7. Borror, C. M. (1998). Mean and variance modeling with qualitative responses: A case study. Quality Engineering, 11(1), 141-148.
8. Kim, K. J., & Lin, D. K. (1998). Dual response surface optimization: a fuzzy modeling approach. Journal of Quality Technology, 30(1), 1-10.

9. Kim, Y. J., & Cho, B. R. (2000). Economic integration of design optimization. Quality Engineering, 12(4), 561-567.
10. Kim, Y. J., & Cho, B. R. (2002). Development of priority-based robust design. Quality Engineering, 14(3), 355-363.
11. Tang, L. C., & Xu, K. (2002). A unified approach for dual response surface optimization. Journal of Quality Technology, 34(4), 437-447.
12. Ding, R., Lin, D. K., & Wei, D. (2004). Dual-response surface optimization: a weighted MSE approach. Quality Engineering, 16(3), 377-385.
13. Shin, S., Samanlioglu, F., Cho, B. R., & Wiecek, M. M. (2011). Computing trade-offs in robust design: perspectives of the mean squared error. Computers & Industrial Engineering, 60(2), 248-255.
14. Köksoy, O. (2006). Multiresponse robust design: Mean square error (MSE) criterion. Applied Mathematics and Computation, 175(2), 1716-1729.
15. Park, H., Park, S. H., Kong, H. B., & Lee, I. (2012). Weighted sum MSE minimization under per-BS power constraint for network MIMO systems. IEEE Communications Letters, 16(3), 360-363.
16. Shaibu, A. B., & Cho, B. R. (2009). Another view of dual response surface modeling and optimization in robust parameter design. The International Journal of Advanced Manufacturing Technology, 41(7-8), 631-641.
17. Costa, N. R. P. (2010). Simultaneous optimization of mean and standard deviation. Quality Engineering, 22(3), 140-149.
18. Park, C. (2013). Determination of the joint confidence region of the optimal operating conditions in robust design by the bootstrap technique. International Journal of Production Research, 51(15), 4695-4703.
19. Shin, S., Truong, N. K. V., Goethals, P. L., Cho, B. R., & Jeong, S. H. (2014). Robust design modeling and optimization of a multi-response time series for a pharmaceutical process. The International Journal of Advanced Manufacturing Technology, 74(5-8), 1017-1031.
20. Chan, H. L., & Ozdemir, A. (2017). The development of a customer-centred, response surface methodology-based robust parameter design optimisation model under a moderately skewed production process. International Journal of Experimental Design and Process Optimisation, 5(4), 255-284.
21. Ozdemir, A., & Cho, B. R. (2016). A nonlinear integer programming approach to solving the robust parameter design optimization problem. Quality and Reliability Engineering International, 32(8), 2859-2870.
22. Ozdemir, A., & Cho, B. R. (2017). Response surface-based robust parameter design optimization with both qualitative and quantitative variables. Engineering Optimization, 49(10), 1796-1812.
23. Lu, Y., Wang, S., Yan, C., & Huang, Z. (2017). Robust optimal design of renewable energy system in nearly/net zero energy buildings under uncertainties. Applied Energy, 187, 62-71.
24. Chatterjee, K., Drosou, K., Georgiou, S. D., & Koukouvinos, C. (2018). Response modelling approach to robust parameter design methodology using supersaturated designs. Journal of Quality Technology, 50(1), 66-75.
25. Ouyang, L., Ma, Y., Wang, J., Tu, Y., & Byun, J. H. (2018). An interval programming model for continuous improvement in micro-manufacturing. Engineering Optimization, 50(3), 400-414.
26. Özdemir, A. (2019). A mixed integer linear programming model for finding optimum operating conditions of experimental design variables using computer-aided optimal experimental designs. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, 11(2), 551-559.
27. Ozdemir, A., & Cho, B. R. (2019). Response surface optimization for a nonlinearly constrained irregular experimental design space. Engineering Optimization, 51(12), 2030-2048.
28. Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response surface methodology: process and product optimization using designed experiments. Hoboken: John Wiley & Sons Inc., New Jersey, USA.
29. Maple, V., 2013. Waterloo maple software. University of Waterloo Version, 17.