Application of kernel principal component analysis to multi-characteristic parameter design problems

Annals of Operations Research

Volumn 263, Issue 1-2, 2018, Pages 69-91

  Soh, Woojin ^a   Kim, Heeyoung ^a   Yum, Bong Jin ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

Kernel principal component analysis; Multiple performance characteristics; Robust parameter design; SN ratio; Taguchi method

Indexed keywords

EID: 84929224696     PISSN: 02545330     EISSN: 15729338     Source Type: Journal    
DOI: 10.1007/s10479-015-1889-2     Document Type: Article

References (38)

1. Antony, J., Anand, R. B., Kumar, M., & Tiwari, M. K. (2006). Multiple response optimization using Taguchi methodology and neuro-fuzzy based model. Journal of Manufacturing Technology Management, 17(7), 908–925.
2. Ben-Hur, A., & Weston, J. (2010). A user’s guide to support vector machines. In O. Carugo & F. Eisenhaber (Eds.), Data mining techniques for the life sciences (pp. 223–239). New York: Human Press.
3. Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer.
4. Datta, S., Nandi, G., Bandyopadhyay, A., & Pal, P. K. (2009). Application of PCA-based hybrid Taguchi method for correlated multicriteria optimization of submerged arc weld: A case study. International Journal of Advanced Manufacturing Technology, 45(3–4), 276–286.
5. Forouraghi, B. (2000). A genetic algorithm for multiobjective robust design. Applied Intelligence, 12(3), 151–161.
6. Goyal, T., Walia, R. S., & Sidhu, T. S. (2013). Multi-response optimization of low-pressure cold-sprayed coatings through Taguchi method and utility concept. The International Journal of Advanced Manufacturing Technology, 64(5–8), 903–914.
7. Hsu, C. H., Chang, C. C., & Lin, C. J. (2003). A practical guide to support vector classification (pp. 1–16).
8. Hsu, C. M. (2001). Solving multi-response problems through neural networks and principal component analysis. Journal of the Chinese Institute of Industrial Engineers, 18(5), 47–54.
9. Huang, C. C., & Tang, T. T. (2006). Parameter optimization in melt spinning by neural networks and genetic algorithms. International Journal of Advanced Manufacturing Technology, 27(11–12), 1113–1118.
10. Jean, M. D., & Wang, J. T. (2006). Using a principal components analysis for developing a robust design of electron beam welding. International Journal of Advanced Manufacturing Technology, 28(9–10), 882–889.
11. Jeyapaul, R., Shahabudeen, P., & Krishnaiah, K. (2006). Simultaneous optimization of multi-response problems in the Taguchi method using genetic algorithm. International Journal of Advanced Manufacturing Technology, 30(9–10), 870–878.
12. Jolliffe, I. T. (2002). Principal component analysis. New York: Springer.
13. Jung, J. H., & Kwon, W. T. (2010). Optimization of EDM process for multiple performance characteristics using Taguchi method and grey relational analysis. Journal of Mechanical Science and Technology, 24(5), 1083–1090.
14. Lampert, C. H. (2009). Kernel methods in computer vision. Hanover, MA: Now Publishers Inc.
15. Liao, H. C. (2006). Multi-response optimization using weighted principal component. International Journal of Advanced Manufacturing Technology, 27(7–8), 720–725.
16. Lin, C. L. (2004). Use of the Taguchi method and grey relational analysis to optimize turning operations with multiple performance characteristics. Materials and Manufacturing Processes, 19(2), 209–220.
17. Lin, J. L., & Lin, C. L. (2002). The use of the orthogonal array with grey relational analysis to optimize the electrical discharge machining process with multiple performance characteristics. International Journal of Machine Tools & Manufacture, 42(2), 237–244.
18. Lin, J. L., & Lin, C. L. (2005). The use of grey-fuzzy logic for the optimization of the manufacturing process. Journal of Materials Processing Technology, 160(1), 9–14.
19. Lin, J. L., & Tarng, Y. S. (1998). Optimization of the multi-response process by the Taguchi method with grey relational analysis. Journal of Grey System, 4(4), 355–370.
20. Lin, J. L., Wang, K. S., Yan, B. H., & Tarng, Y. S. (2000). Optimization of the electrical discharge machining process based on the Taguchi method with fuzzy logics. Journal of Materials Processing Technology, 102(1), 48–55.
21. Müller, K. R., Mika, S., Rätsch, G., Tsuda, K., & Schölkopf, B. (2001). An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 12(2), 181–202.
22. Phadke, M. S. (1989). Quality engineering using robust design. New Jersey: Prentice Hall.
23. Schölkopf, B., Smola, A., & Müller, K. R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319.
24. Schölkopf, B., & Smola, A. J. (2002). Learning with kernels. Cambridge, MA: MIT Press.
25. Sharma, V., Chattopadhyaya, S., & Hloch, S. (2011). Multi response optimization of process parameters based on Taguchi-Fuzzy model for coal cutting by water jet technology. International Journal of Advanced Manufacturing Technology, 56(9–12), 1019–1025.
26. Sibalija, T. V., & Majstorovic, V. D. (2009). Multi-response optimisation of thermosonic copper wire-bonding process with correlated responses. International Journal of Advanced Manufacturing Technology, 42(3–4), 363–371.
27. Sibalija, T. V., & Majstorovic, V. D. (2012). An integrated approach to optimise parameter design of multi-response processes based on Taguchi method and artificial intelligence. Journal of Intelligent Manufacturing, 23(5), 1511–1528.
28. Su, C. T., & Tong, L. I. (1997). Multi-response robust design by principal component analysis. Total Quality Management, 8(6), 409–416.
29. Sukthomya, W., & Tannock, J. D. T. (2005). Taguchi experiemental design for manufacturing process optimisation using historical data and a neural network process model. International Journal of Quality & Reliability Management, 22(5), 485–502.
30. Taguchi, G., & Yokoyama, Y. (1993). Taguchi methods: Design of experiments, quality engineering series vol 4. Michigan: ASI Press.
31. Tarng, Y. S., Juang, S. C., & Chang, C. H. (2002). The use of grey-based Taguchi methods to determine submerged arc welding process parameters in hardfacing. Journal of Materials Processing Technology, 128(1), 1–6.
32. Tong, L. I., & Su, C. T. (1997). Optimizing multi-response problems in the Taguchi method by fuzzy multiple attribute decision making. Quality and Reliability Engineering International, 13(1), 25–34.
33. Tsao, C. C., & Hocheng, H. (2008). Evaluation of thrust force and surface roughness in drilling composite material using Taguchi analysis and neural network. Journal of Materials Processing Technology, 203(1), 342–348.
34. Tzeng, C. J., Lin, Y. H., Yang, Y. K., & Jeng, M. C. (2009). Optimization of turning operations with multiple performance characteristics using the Taguchi method and grey relational analysis. Journal of Materials Processing Technology, 209(6), 2753–2759.
35. Wang, Z., Zhu, L. I., & Wu, J. H. (1996). Grey relational analysis of correlation of errors in measurement. Journal of Grey System, 8(1), 73–78.
36. Yang, Y. S., Shih, C. Y., & Fung, R. F. (2014). Multi-objective optimization of the light guide rod by using the combined Taguchi method and grey relational approach. Journal of Intelligent Manufacturing, 25(1), 99–107.
37. Yildiz, A. R., Öztürk, N., Kaya, N., & Öztürk, F. (2007). Hybrid multi-objective shape design optimization using Taguchi’s method and genetic algorithm. Structural and Multidisciplinary Optimization, 34(4), 317–332.
38. Yum, B. J., Kim, S. J., Seo, S. K., Byun, J. H., & Lee, S. H. (2013). The Taugchi robust design method: Current status and future directions. Journal of the Korean Institute of Industrial Engineers, 39(5), 325–341.