Efficient optimization of process parameters in shadow mask manufacturing using NNPLS and genetic algorithm

International Journal of Production Research

Volumn 43, Issue 15, 2005, Pages 3209-3230

  Jeong, Buhwan ^a   Lee, Jaewook ^a   Cho, Hyunbo ^a  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

Author keywords

Genetic algorithm; Neural network partial least squares (NNPLS); Process parameter optimization; Shadow mask manufacturing process; Variable importance index

Indexed keywords

CATHODE RAY TUBES; GENETIC ALGORITHMS; MASKS; NEURAL NETWORKS; OPTIMIZATION;

PROCESS PARAMETER OPTIMIZATION; SHADOW-MASK MANUFACTURING PROCESS; VARIABLE IMPORTANCE INDEX;

PROCESS ENGINEERING;

EID: 27844537803     PISSN: 00207543     EISSN: 1366588X     Source Type: Journal    
DOI: 10.1080/00958970500137167     Document Type: Article

References (27)

1. Botto, M.A. and Costa, J., A comparison of nonlinear predictive control techniques using neural network models. J. Syst. Archit., 1998, 44(8), 597-616.
2. Caldas, L.G. and Norford, L.K., A design optimization tool based on a genetic algorithm. Automat. Constr., 2002, 11(2), 173-184.
3. Camacho, E.F. and Bordons, C., Model Predictive Control in the Process Industry (1st ed.), 1995 (Springer: New York).
4. Cauchi, J.P. and Chagnon, P., Comparison of selection methods of explanatory variables in PLS regression with applications to manufacturing process data. Chemom. Intell. Lab. Syst., 2001, 58(2), 171-193.
5. Cook, D.F., Ragsdale, C.T. and Major, R.L., Combining a neural network with a genetic algorithm for process parameter optimization. Eng. Appl. Artif. Intell., 2000, 13(4), 391-396.
6. Daren, Z., QSPR studies of PCBs by the combination of genetic algorithms and PLS analysis. Comput. Chem., 2001, 25(2), 197-204
7. DeGarmo, E.P., Black, J.T. and Kohser, R.A., Materials and Processes in Manufacturing (7th ed.), 1988 (Macmillan: New York).
8. Derringer, G. and Suich, R., Simultaneous optimization of several response variables. J. Qual. Technol., 1980, 12(4), 214-219.
9. Edward, E., Conrad, B. and Selwayan, S., Optimization of a neural network model for calibration of voltammetric data. Chemom. Intell. Lab. Syst., 2002, 61(1-2), 35-49.
10. Evans, J.R. and Lindsay, W.M., The Management and Control of Quality, 1996 (West Publishing: St. Paul, MN).
11. Garson, D.G., Interpreting neural network connection weights. AI Expert, 1991, 6(7), 47-51.
12. Islier, A.A., A genetic algorithm approach for multiple criteria facility layout design. Int. J. Prod. Res., 1998, 36(6), 1549-1569.
13. Jang, J.S.R., Sun, C.T. and Mizutani, E., Neuro-fuzzy and Software Computing: A Computational Approach to Learning and Machine Intelligence, 1997 (Prentice-Hall: Englewood Cliffs, NJ).
14. Kim, C., Production of shadow mask improved technology. J. Mater. Process. Technol., 2002, 127(3), 409-418.
15. Leray, P. and Gallinari, P., Feature selection with neural network, Behaviormetrika, 1999, 26(1), 145-166.
16. Li, T.S., Su, C.T. and Chiang, T.L., Applying robust multi-response quality engineering for parameter selection using a novel neural-genetic algorithm. Comput. Ind., 2003, 50(1), 113-122.
17. MacGregor, J.F. and Kourti, T., Statistical process control of multivariate processes. Control Eng. Pract., 1995, 3(3), 403-414.
18. Malthouse, E.C., Tamhane, A.C. and Mah, R.S.H., Nonlinear partial least squares. Comput. Chem. Eng., 1997, 21(8), 875-890.
19. Maruthi, B.N. and Joseph, V.R, Improving the yield of printed circuit boards using design of experiments. Qual. Eng., 1999-2000, 12(2), 259-265.
20. Olden, J.D. and Jackson, D.A., Illuminating the 'black box': a randomization approach for understanding variable contributions in artificial neural networks. Ecol. Modell., 2002, 154(1-2), 135-150.
21. Potocnik, P. and Grabec, I., Nonlinear model predictive control of a cutting process. Neurocomputing, 2002, 43(1-4), 107-126.
22. Qin, S.J., Neural networks for intelligent sensors and control - practical issues and some solutions. In Neural Systems for Control, chapter 8, pp. 213-234, 1997 (Academic Press: New York).
23. Qin, S.J. and McAvoy, T.J., Nonlinear PLS modeling using neural networks. Comput. Chem. Eng., 1992, 16(4), 379-391.
24. Song, K., Jang, P., Cho, H. and Jun, C., Partial least square-based model predictive control for large-scale manufacturing processes. IIE Trans., 2002, 34(10), 881-890.
25. Tang, K. and Li, T., Combining PLS with GA-GP for QSAR. Chemom. Intell. Lab. Syst., 2002, 64(1), 55-64.
26. Tenny, M.J., Wright, S.J. and Rawlings, J.B., Nonlinear Model Predictive Control via Feasibility-perturbed Sequential Quadratic Programming, Optimization Technical Report 02-06, Computer Sciences Department, University of Wisconsin, 2002.
27. Vining, G.G., A compromise approach to multiresponse optimization. J. Qual. Technol., 1998, 30(4), 309-313.