Modelling and optimization of fluid dispensing for electronic packaging using neural fuzzy networks and genetic algorithms

Engineering Applications of Artificial Intelligence

Volumn 23, Issue 1, 2010, Pages 18-26

  Chan, K Y ^a,b   Kwong, C K ^a   Tsim, Y C ^a  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^b CURTIN UNIVERSITY   (Australia)

Author keywords

Fluid dispensing; Genetic algorithm; Manufacturing process design; Microchip encapsulation; Neural fuzzy networks; Process modeling; Process optimization

Indexed keywords

ANALYTICAL APPROACH; ELECTRONIC PACKAGING; FLUID DISPENSING PROCESS; FUZZY REGRESSIONS; GENETIC ALGORITHM APPROACH; GLOBAL COMPETITION; HIGH YIELD; LOW COSTS; MANUFACTURING PROCESS DESIGN; MEAN ERRORS; MODEL FLUIDS; NEURAL FUZZY NETWORKS; OPTIMUM OPERATING CONDITIONS; PROCESS CONDITION; PROCESS MODELING; QUALITY REQUIREMENTS; ROBUST OPERATION; STATISTICAL REGRESSION; TRIAL-AND-ERROR APPROACH; VALIDATION TEST;

ELECTRONICS PACKAGING; ERRORS; FLUIDS; GENETIC ALGORITHMS; INDUSTRIAL ENGINEERING; MICROPROCESSOR CHIPS; OPTIMIZATION; PROCESS CONTROL; PROCESS DESIGN; PRODUCTION ENGINEERING; STATISTICAL TESTS;

FUZZY NEURAL NETWORKS;

EID: 74649087383     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2009.09.009     Document Type: Article

References (37)

1. Babets K., and Geskin E.S. Application of fuzzy logic for modeling of waterjet depainting. Machining Science and Technology 4 1 (2000) 81-100
2. Back T., and Schwefel H.P. An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation 1 1 (1993) 1-23
3. Brinksmeier E., Tonshoff H.K., Czenkusch C., and Heinzel C. Modelling and optimisation of grinding processes. Journal of Intelligent Manufacturing 9 (1998) 303-314
4. Chen D.X., 2002. Modeling and off-line control of fluid dispensing for electronics packaging. Ph.D. Thesis, The University of Saskatchewan.
5. Chen Y., Tang J., Fung R.Y.K., and Ren Z. Fuzzy regression based mathematical programming for QFD. International Journal of Production Research 42 5 (2004) 3583-3604
6. Chiang H.H., Hieber C.A., and Wang K.K. A unified simulation of the filling and postfilling stages in injection molding. Part 1: formulation. Polymer Engineering and Science 31 (1991) 116-124
7. Chipperfield A. J., Fleming P. J., Pohlheim H., Fonseca C. M., 1994. Genetic Algorithm Toolbox User's Guide. ACSE Research Report No. 512, University of Sheffield.
8. DeJong K.A., 1975. An analysis of the behaviour of a class of genetic adaptive systems. Ph.D. Thesis, Department of Computer and Communication Science, University of Michigan.
9. De Jong K. A., Spears W. M., 1990. An analysis of the interacting roles of population size and crossover in genetic algorithms, The Frist International Conference on Parallel Problems Solving from Nature, pp. 38-47.
10. Eiben A.E., Hinterding R., and Michalewicz Z. Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation 3 2 (1999) 124-141
11. Fiordaliso A. A constrained Takagi-Sugeno fuzzy system that allows for better interpretation and analysis. Fuzzy Sets and Systems 118 (2001) 307-318
12. Goldberg D.E. Genetic Algorithms in Search, Optimization and Machine Learning (1989), Addison Wesley Longman, Inc, United States of America
13. Grefenstette J.J. Optimization of control parameters for genetic algorithms, IEEE Transaction on Systems. Man and Cybernetics 16 1 (1986) 122-128
14. Han R., Shi L., and Gupta M. Three-dimensional simulation of microchip encapsulation process. Polymer Engineering and Science 40 3 (2000) 776-785
15. Hussein Y.A., and El-Ghazaly S.M. Modeling and optimization of microwave devices and circuits using genetic algorithms. IEEE Transactions on Microwave Theory and Techniques 52 1 (2004) 329-336
16. Ip K.W., Kwong C.K., Bai H., and Tsim Y.C. Process modelling of epoxy dispensing for microchip encapsulation using fuzzy linear regression with fuzzy intervals. International Journal of Advanced Manufacturing Technology 22 (2003) 417-423
17. Kang S.Y., Xie H., and Lee Y.C. Physical and fuzzy logic modeling of a flip-chip thermocompression bonding process. Journal of Electronic Packaging 115 (1993) 63-70
18. Knowles J.D., and Corne D.W. Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation 8 (2000) 149-172
19. Kwong C.K., Chan K.Y., and Wong H. An empirical approach to modeling fluid dispensing for electronic packaging. International Journal of Advanced Manufacturing Technology 34 (2007) 111-121
20. Lai Y.J., and Chang S.I. A fuzzy approach for multiresponse optimization: an off-line quality engineering Problem. Fuzzy Sets and Systems 63 (1994) 117-129
21. Li H.L., Chou T., and Chou C.P. Optimization of resistance spot welding process using Taguchi method and a neural network. Experimental Techniques 31 5 (2007) 30-36
22. Li H.X., Tso S.K., and Deng H. A concept approach to integrate design and control for the epoxy dispensing process. International Journal of Advanced Manufacturing Technology 17 (2001) 677-682
23. Ling C.H., Leung F.H.F., Lam H.K., and Tam P.K.S. Short-term electric load forecasting based on a neural fuzzy network. IEEE Transactions on Industrial Electronics 50 6 (2003) 1305-1316
24. Muhlenbein H., and Voosen D.S. Predictive models for the breeder genetic algorithm: I. Continuous parameter optimization. Evolutionary Computation 1 1 (1993) 25-49
25. Petri K.L., Billo R.E., and Bidanda B. A neural network process model for abrasive flow machining operations. Journal of Manufacturing Systems 17 1 (1998) 52-65
26. Poon G.K.K., Williams D.J., and Chin K.S. Optimizing the lithographic patterning effect in an acid copper electroplating process. International Journal of Advanced Manufacturing Technology 16 (2000) 881-888
27. Schaffer J., Caruana R., Eshelman L., Das R., 1989. A study of control parameters affecting online performance of genetic algorithms for function optimization. In: Proceedings of the Third International Conference on Genetic Algorithms, pp. 51-61.
28. Schaible B., and Lee Y.C. Fuzzy Logic Based Regression Models for electronics manufacturing applications. Advances in Electronic Packaging 1 (1997) 147-155
29. Seber G.A.F. Linear regression analysis (2003), Wiley
30. Tan K.H., and Yuen M.M.F. A fuzzy multiobjective approach for minimization of injection molding defects. Polymer Engineering and Science 40 4 (2000) 956-971
31. Tong K.W., Kwong C.K., and Yu K.M. Intelligent process design system for the transfer moulding of electronic packages. International Journal of Production Research 42 10 (2004) 1911-1931
32. Tzafestas S.G., and Zikidis K.C. NeuroFAST: on-line neuro-fuzzy ART-based structure and parameter learning TSK model. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics 31 5 (2001) 797-802
33. Wilson P.R., Ross J.N., and Brown A.D. Optimizing the Jiles-Atherton model of hysteresis by a genetic algorithm. IEEE Transactions on Magnetics 37 2 (2001) 989-993
34. Xie H., and Lee Y.C. Process optimization using a fuzzy logic response surface method. IEEE Transactions on Components, Packaging and Manufacturing Technology - Part A 17 2 (1994) 202-210
35. Xie H., MAHAJAN R.L., and LEE Y.C. Fuzzy logic models for thermally based microelectronic manufacturing processes. IEEE Transactions on Semiconductor Manufacturing 8 3 (1995) 219-226
36. Ying H. General MISO Takagi-Sugeno fuzzy systems with simplified linear rule consequent as universal approximators for control and modelling applications. Information Sciences 108 (1998) 91-107
37. Zitzler E., and Thiele L. Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE transactions on Evolutionary Computation 3 4 (1999) 257-271