Table of Contents
International Journal of Quality, Statistics, and Reliability
Volume 2012, Article ID 494818, 11 pages
http://dx.doi.org/10.1155/2012/494818
Research Article

A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method

1Department of Industrial Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran
2Department of Industrial Engineering, Eyvanekey University, Semnan, Iran
3Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran

Received 12 February 2012; Revised 16 May 2012; Accepted 3 June 2012

Academic Editor: Tadashi Dohi

Copyright © 2012 Ali Salmasnia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. G. Taguchi, Introduction to Quality Engineering, Asian Productivity Organization (Distributed by American Supplier Institute Inc.), Dearborn, Mich, USA, 1986.
  2. G. S. Peace, Taguchi Methods: A Hands-On Approach, Addison- Wesley, Boston, Mass, USA, 1993.
  3. M. S. Phadke, Quality Engineering Using Robust Design, Prentice- Hall, New York, NY, USA, 1989.
  4. D. C. Ko, D. H. Kim, and B. M. Kim, “Application of artificial neural network and Taguchi method to preform design in metal forming considering workability,” International Journal of Machine Tools and Manufacture, vol. 39, no. 5, pp. 771–785, 1999. View at Google Scholar · View at Scopus
  5. Y. Su, Z. Bao, F. Wang, and T. Watanabe, “Efficient GA approach combined with Taguchi method for mixed constrained circuit design,” in International Conference on Computational Science and Its Applications (ICCSA '11), pp. 290–293, 2011.
  6. Y. L. Lo and C. C. Tsao, “Integrated Taguchi method and neural network analysis of physical profiling in the wirebonding process,” IEEE Transactions on Components and Packaging Technologies, vol. 25, no. 2, pp. 270–277, 2002. View at Publisher · View at Google Scholar · View at Scopus
  7. K. J. Kim, J. H. Byun, D. Min, and I. J. Jeong, Multiresponse Surface Optimization: Concept, Methods, and Future Directions, Tutorial, Korea Society for Quality Management, 2001.
  8. C. T. Su and K. L. Hsieh, “Applying neural networks to achieve robust design for dynamic quality characteristics,” International Journal of Quality and Reliability Management, vol. 15, pp. 509–519, 1998. View at Google Scholar
  9. H. C. Liao, “A data envelopment analysis method for optimizing multi-response problem with censored data in the Taguchi method,” Computers and Industrial Engineering, vol. 46, no. 4, pp. 817–835, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. A. Charnes, W. W. Cooper, and E. Rhodes, “Measuring the efficiency of decision making units,” European Journal of Operational Research, vol. 2, no. 6, pp. 429–444, 1978. View at Google Scholar · View at Scopus
  11. E. Gutiérrez and S. Lozano, “Data envelopment analysis of multiple response experiments,” Applied Mathematical Modelling, vol. 34, no. 5, pp. 1139–1148, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Antony, R. B. Anand, M. Kumar, and M. K. Tiwari, “Multiple response optimization using Taguchi methodology and neuro-fuzzy based model,” Journal of Manufacturing Technology Management, vol. 17, no. 7, pp. 908–925, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. K. L. Hsieh and L. I. Tong, “Optimization of multiple quality responses involving qualitative and quantitative characteristics in IC manufacturing using neural networks,” Computers in Industry, vol. 46, no. 1, pp. 1–12, 2001. View at Publisher · View at Google Scholar · View at Scopus
  14. K. L. Hsieh, “Parameter optimization of a multi-response process for lead frame manufacturing by employing artificial neural networks,” International Journal of Advanced Manufacturing Technology, vol. 28, no. 5-6, pp. 584–591, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Noorossana, S. Davanloo Tajbakhsh, and A. Saghaei, “An artificial neural network approach to multiple-response optimization,” International Journal of Advanced Manufacturing Technology, vol. 40, no. 11-12, pp. 1227–1238, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. H. H. Chang, “A data mining approach to dynamic multiple responses in Taguchi experimental design,” Expert Systems with Applications, vol. 35, no. 3, pp. 1095–1103, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. H. H. Chang and Y. K. Chen, “Neuro-genetic approach to optimize parameter design of dynamic multiresponse experiments,” Applied Soft Computing Journal, vol. 11, no. 1, pp. 436–442, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. T. L. Chiang and C. T. Su, “Optimization of TQFP molding process using neuro-fuzzy-GA approach,” European Journal of Operational Research, vol. 147, no. 1, pp. 156–164, 2003. View at Publisher · View at Google Scholar · View at Scopus
  19. D. Lu and J. Antony, “Optimization of multiple responses using a fuzzy-rule based inference system,” International Journal of Production Research, vol. 40, no. 7, pp. 1613–1625, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. J. L. Lin, K. S. Wang, B. H. Yan, and Y. S. Tarng, “Optimization of the electrical discharge machining process based on the Taguchi method with fuzzy logics,” Journal of Materials Processing Technology, vol. 102, no. 1, pp. 48–55, 2000. View at Publisher · View at Google Scholar · View at Scopus
  21. C. B. Cheng, C. J. Cheng, and E. S. Lee, “Neuro-fuzzy and genetic algorithm in multiple response optimization,” Computers and Mathematics with Applications, vol. 44, no. 12, pp. 1503–1514, 2002. View at Publisher · View at Google Scholar · View at Scopus
  22. H. J. Zimmermann, “Fuzzy programming and linear programming with several objective functions,” Fuzzy Sets and Systems, vol. 1, no. 1, pp. 45–55, 1978. View at Google Scholar · View at Scopus
  23. T. V. Sibalija and V. D. Majstorovic, “An integrated approach to optimize parameter design of multi-response processes based on Taguchi method and artificial intelligence,” Journal Intelligent Manufacture. In press. View at Publisher · View at Google Scholar
  24. T. V. Sibalija, S. Z. Petronic, V. D. Majstorovic, R. Prokic-Cvetkovic, and A. Milosavljevic, “Multi-response design of Nd:YAG laser drilling of Ni-based superalloy sheets using Taguchi's quality loss function, multivariate statistical methods and artificial intelligence,” International Journal of Advanced Manufacturing Technology, vol. 54, no. 5–8, pp. 537–552, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. A. Salmasnia, R. B. Kazemzadeh, and M. M. Tabrizi, “A novel approach for optimization of correlated multiple responses based on desirability function and fuzzy logics,” Neurocomputing, vol. 91, pp. 56–66, 2012. View at Publisher · View at Google Scholar
  26. Y. S. Tarng, W. H. Yang, and S. C. Juang, “Use of fuzzy logic in the Taguchi method for the optimization of the submerged arc welding process,” International Journal of Advanced Manufacturing Technology, vol. 16, no. 9, pp. 688–694, 2000. View at Publisher · View at Google Scholar · View at Scopus
  27. P. Chatsirirungruang, “Application of genetic algorithm and Taguchi method in dynamic robust parameter design for unknown problems,” International Journal of Advanced Manufacturing Technology, vol. 47, no. 9–12, pp. 993–1002, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. L. I. Tong, C. H. Wang, and H. C. Chen, “Optimization of multiple responses using principal component analysis and technique for order preference by similarity to ideal solution,” International Journal of Advanced Manufacturing Technology, vol. 27, no. 3-4, pp. 407–414, 2005. View at Publisher · View at Google Scholar · View at Scopus
  29. L. I. Tong, C. C. Chen, and C. H. Wang, “Optimization of multi-response processes using the VIKOR method,” International Journal of Advanced Manufacturing Technology, vol. 31, no. 11-12, pp. 1049–1057, 2007. View at Publisher · View at Google Scholar · View at Scopus
  30. Neural Ware, Neural Works Professional II/Plus and Neural Works Explorer, Neural Ware, Carnegie, Pa, USA; Penn Centre West, Beverly Hills, Calif, USA, 1990.
  31. J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, Mich, USA, 1975.
  32. M. T. Hagan, H. B. Demuth, and M. H. Beale, Neural Network Design, PWS Publishing, Boston, Mass, USA, 1996.