Finite element and neural network models for process optimization in selective laser sintering

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

Volumn 218, Issue 6, 2004, Pages 607-614

  Boillat, E ^a   Kolossov, S ^a   Glardon, R ^a   Loher, M ^b   Saladin, D ^b   Levy, G ^b  

^a EPFL   (Switzerland)

^b FHO Fachhochschule Ostschweiz   (Switzerland)

Author keywords

Finite element method; Neural networks; Process optimization; Selective laser sintering

Indexed keywords

FINITE ELEMENT METHOD; LASER APPLICATIONS; PROCESS CONTROL; SINTERING;

BOUNDING QUALITY; PROCESS OPTIMIZATION; PROCESS SIMULATION; SELECTIVE LASER SINTERING;

NEURAL NETWORKS;

EID: 19044378846     PISSN: 09544054     EISSN: 09544054     Source Type: Journal    
DOI: 10.1243/0954405041167121     Document Type: Conference Paper

References (21)

1. Duvall, G. A practical methodology for the statistical design of complex logic products for performance. IEEE Trans. VLSI Systems, 1995, 3, 112-123.
2. Kovarova-Kovar, K., Gehlen, S., Kunze, A., Keller, T., von Däniken, R., Kolb, M., et al. Application of model-predictive control based on artificial neural networks to optimize the fed-batch process for riboflavin production. J. Biotechnol., 2000, 1-13.
3. Rangwala, S. S. and Dornfield, D. Learning and optimization of machining operations using computing abilities of neural networks. IEEE Trans. Systems, Man, and Cybernetics, 1989, 19(2), 299-314.
4. Monostori, L. and Egresits, C. Modeling and monitoring of milling through neuro-fuzzy techniques. In IFAC on Intelligent Manufacturing Systems, Vienna, Austria, 1994, pp. 463-469.
5. Tisza, M. Integration of numerical modeling and knowledge based system in metal forming. ATP, 1999, 1, 117-128.
6. Dong, D. and McAvoy, T. J. Nonlinear principal component analysis-based on principal curves and neural networks. Computers and Chem. Engng, 1996, 20, 65-78.
7. Boillat, E. Finite element method on non-conforming grids by penalizing the matching constraint. M2AN, 2003, 37(2), 357-372.
8. Touloukian, Y. S. and Ho, C. Y. Thermophysical Properties of Matter, TPRC Data Series, 1970.
9. Frenkel, Viscous flow of crystalline bodies under the action of surface tension. J. Phys. USSR, 1945, 9, 385-391.
10. Magenes, E., Nochetto, R. H. and Verdi, C. Energy error estimates for a linear scheme to approximate non-linear parabolic. Math. Modeling and Numer. Analysis, 1987, 21, 655-678.
11. Kolossov, S., Boillat, E., Glardon, R., Fischer, P. and Loher, M. 3D-FE simulation for the temperature evolution during the selective laser sintering process. Appl. Phys. A, 2003 (submitted).
12. Ripley, B. D. Pattern Recognition and Neural Networks, 1997 (Cambridge University Press, Cambridge).
13. Bishop, Ch. Neural Networks for Pattern Recognition, 1995 (Clarendon Press, London).
14. Hornik, K. Multilayer feedforward networks are universal approximators. Neural Networks, 1989, 2, 359-366.
15. Jang, J. S. R., Sun, C. T. and Mizutani, E. Neuro-Fuzzy and Soft Computing, 1998 (Prentice-Hall, Englewood Cliffs, New Jersey).
16. Takagi, T. and Sugeno, M. Fuzzy identification of systems and its application to modelling and control. IEEE Trans. Systems, Man, and Cybernetics, 1985, 15, 116-132.
17. Webb, A. Statistical Pattern Recognition, 1999 (John Wiley, Chichester).
18. Sauer, T. and Xu, Y. On multivariate Lagrange interpolation. Math. Comput., 1995, 1147-1170.
19. Bojanov, B. D., Hakopian, H. A. and Sahakian, A. A. Spline Functions and Multivariate Interpolations, 1993 (Kluwer Academic, Dordrecht, The Netherlands).
20. Jung, H. B. and Kim, K. A. New parameterisation method for NURBS surface interpolation. Int. J. Advd Mfg Technol., 2000, 16, 784-790.
21. Sugeno, M. and Kang, G. T. Structure identification of fuzzy models. Fuzzy Sets and Systems, 1988, 28, 15-33.