Parameter optimization of a multi-response process for lead frame manufacturing by employing artificial neural networks

International Journal of Advanced Manufacturing Technology

Volumn 28, Issue 5-6, 2006, Pages 584-591

  Hsieh, Kun Lin ^a  

^a NATIONAL TAITUNG UNIVERSITY   (Taiwan)

Author keywords

Back propagation neural network; Lead frame; Multi response problem; Parameter optimization

Indexed keywords

NEURAL NETWORKS; OPTIMIZATION; PARAMETER ESTIMATION; PROBLEM SOLVING;

BACK-PROPAGATION NEURAL NETWORK; LEAD FRAME; MULTI-RESPONSE PROBLEM; PARAMETER OPTIMIZATION;

PROCESS CONTROL;

EID: 33645511446     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-004-2383-1     Document Type: Article

References (25)

1. Ames AE, Mattucci N, Macdonald S, Szonyi G, Hawkins DM (1997) Quality loss functions for optimization across multiple responses surface. J Qual Technol 29(3):339-346
2. Castillo ED, Montgomery DC, McCarville DR (1996) Modified desirability functions for multiple response optimization. J Qual Technol 28(3):337-345
3. Chapman RE (1995-1996) Photochemistry multiple response cooptimization. Qual Eng 8(1):31-45
4. Cybenko G (1989) Approximation by superposition of a sigmoidal function. Math Control Signal System 2:303-314
5. Derringer G, Suich R (1980) Simultaneous optimization of several response variables. J Qual Technol 12(4):214-219
6. Fowlkes WY, Creveling CM (1995) Engineering methods for robust product design: using Taguchi methods in technology and product development. Addison-Wesley, Boston
7. Johnson RA, Wichern DW (1992) Applied multivariate statistical analysis. Prentice-Hall, New York
8. Khuri AI, Conlon M (1981) Simultaneous optimization of multiple responses represented by polynomial regression functions. Technometrics 23:363-375
9. Lai Y-J. Chang TH (1994) A fuzzy approach for multiresponse optimization: an off-line quality engineering problem. Fuzzy Set Syst 63:117-129
10. Layne KL (1995) Methods to determine optimum factor levels for multiple response in designed experimentation. Qual Eng 7(4):649-656
11. Leon NA (1996-1997) A pragmatic approach to multiple-response problem using loss function. Qual Eng 9(2):213-220
12. Logothetis N, Haigh A (1988) Characterizing and optimizing multi-response processes by the Taguchi method. Qual Reliab Eng Int 4:159-169
13. Montgomery DC (1991) Design and analysis of experiments. Wiley, New York
14. Myers RH, Montgomery DC (1995) Response surface methodology. Wiley, New York
15. NeuralWare, Inc. (1990) NeuralWorks Professional II/Plus and Neural-Works Exporer. NeuralWare, Carnegie
16. Peace GS (1993) Taguchi methods: a hands-on approach. Addison-Wesley, Boston
17. Phadke MS (1989) Quality engineering using robust design. Prentice-Hall, New York
18. Pignatiello JJ Jr. (1993) Strategies for robust multi-response quality engineering. IIE Trans 25:5-15
19. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE. McClelland JL (eds.) Parallel distribution processing: explorations in microstructure of cognition. MIT Press, Cambridge, pp 318-362
20. Su C-T. Heish K-L (1998) Applying neural networks to achieve robust design for dynamic quality characteristics. Int J Qual Reliab Manage 15(4-5):509-519
21. Su C-T. Tong L-I (1997) Multi-response robust design by principal component analysis. Total Qual Manage 8(6):409-116
22. Taguchi G (1966) Statistical analysis (in Japanese). Maruzen, Tokyo
23. Tong L-I, Hsieh K-L (2001) Optimization of multiple quality responses involving qualitative and quantitative characteristics in IC manufacturing using artificial neural networks. Comput Ind 46(1):1-12
24. Tong L-I. Su C-T (1997) Optimizing multi-response problems in the Taguchi method by fuzzy multiple attribute decision making. Qual Reliab Eng Int 13:25-34
25. Tong L-I, Su C-T, Wang C-H (1997) The optimization of multi-response problems in Taguchi method. Int J Qual Reliab Manage 14(4):367-380