A neural network-based approach for optimising rubber extrusion lines

International Journal of Computer Integrated Manufacturing

Volumn 20, Issue 8, 2007, Pages 828-837

  Marcos, A Gonzlez ^a   Espinoza, A V Perna ^b   Elas, F Alba ^b   Forcada, A Garca ^c  

^a UNIVERSITY OF LEÓN   (Spain)

^b UNIVERSITY OF LA RIOJA   (Spain)

^c SA la Portalada   (Spain)

Author keywords

Cure characteristics; Data mining; Mixing conditions; Neural network; Rubber extrusion lines

Indexed keywords

DATA MINING; EXTRUSION; OPTIMIZATION; RUBBER;

CURE CHARACTERISTICS; MIXING CONDITIONS; RUBBER EXTRUSION LINES;

NEURAL NETWORKS;

EID: 35448952613     PISSN: 0951192X     EISSN: 13623052     Source Type: Journal    
DOI: 10.1080/09511920601108808     Document Type: Article

References (35)

1. Azhari, C.H., Sahari, J. and Qing, L., Mathematical modeling of thermoplastic natural rubber melt flow in a double feed extrusion system. Polymer, 1998, 39, 4915-4921.
2. Busfield, J.J.C., Deeprasertkul, C., Thomas, A.G., The effect of liquids on the dynamic properties of carbon black filled natural rubber as a function of pre-strain. Polymer, 2000, 41, 9219-9225.
3. Castejón, M., Ordieres, J.B., Martínez de Pisón, F.J. and Vergara González, E.P., Outlier detection and data clearing in multivariate non-normal simples. The PAELLA algorithm. Data Min. Knowl. Disc., 2004, 9, 171-187.
4. Drozdov, A.D. and Dorfmann, A., The nonlinear viscoelastic response of carbon black-filled natural rubbers. Int. J. Solids Structures, 2002, 39, 5699-5717.
5. El-Nashar, D.E. and Turky, G., Effect of mixing conditions and chemical cross-linking agents on the physicomechanical and electrical properties of NR/NBR blends. Polymer-plastics Technol. Engng, 2003, 42, 269-284.
6. Freakley, P.K. and Fletcher, J.B., The single-rotor continuous mixing system. Rubber World, 2002, 226, 28-31.
7. Gourgand, M., Lacomme, P. and Traoré, M.K., Design of a monitoring environment for manufacturing systems management and optimization. Int. J. Comput.Integr. Mfg, 2003, 16, 61-80.
8. Haberstroh, E. and Ryzko, P., Analysis methods for the prediction of rubber compound quality for mixing in internal mixers. Kautschuk und Gummi Kunststoffe, 1999, 52, 15-30.
9. Haykin, S., Neural Networks, a Comprehensive Foundation, 1998 (Prentice Hall: New Jersey).
10. Herbiet, R., Effect of process parameters on the compounding in internal mixers of polymer systems highly filled with halogen free flameretarding agents. Gummi, Fasern, Kunststoffe, 2000, 53, 194-198.
11. Hou, L. and Nassehi, V., Evaluation of stress-effective flow in rubber mixing. Nonlinear Anal.-Theor., 2001, 47, 1809-1820.
12. http://www-ra.informatik.uni-t.uebingen.de/SNNS/. Institute for parallel and distributed high performance systems. SNNS - Stuttgart Neural Network Simulator (accessed 23 January 2006).
13. Ishiaku, U.S., Chong, C.S. and Ismail, H., Cure characteristics and vulcanizate properties of a natural rubber compound extended with convoluted rubber powder. Polym. Testing, 2000, 19, 507-521.
14. Koolhiran, A. and White, J.L., Comparasion of intermeshing rotor and traditional rotors of internal mixers in dispersing silica and other fillers. J. Appl. Polymer Sci., 2000, 78, 1551-1554.
15. Limper, A. and Moessinger, J., Production mixer with variable intermeshing rotor clearance. Gummi, Fasern, Kunststoffe, 2000, 53, 158-161.
16. Menon, A.R.R., Aigbodion, A.I., Pillai, C.K.S. and Mathew, N.M., Processability characteristics and physico-mechanical properties of natural rubber modified with cashewnut shell liquid and cashewnut shell liquid-formaldehyde resin. Eur. Polym. J., 2002, 38, pp. 163-168.
17. Merikoski, S., Laurikkala, M. and Koivisto, H., An adaptive neuro-fuzzy inference system as a soft sensor for viscosity in rubber mixing process. In N. Mastorakis (ed.), Advances in Neural Networks and Applications, edited by N. Mastorakis, pp. 287-291, 2001 (World Scientific and Engineering Society Press: Greece).
18. Morton-Jones, D.H., Procesamiento de Plásticos, 1993 (Limusa: Mexico).
19. Nassehi, V. and Ghoreishy, M.H., Finite element analysis of mixing in partially filled twin blade internal mixers. Int. Polymer Process., 1998, 13, 231-238.
20. Ordieres Meré, J.B., González Marcos, A., González, J.A. and Lobato Rubio, V., Estimation of mechanical properties of steel, strip in hot dip galvanizing lines. Ironmaking Steelmaking, 2004, 31, 43-50.
21. Ordieres, J., López, L.M., Bello, A. and Forcada, A., Intelligent methods helping the design of a manufacturing system, for die extrusion rubbers. Int. J. Comput. Integr. Mfg, 2003, 16, 173-180.
22. Peña, D., Análisis de Datos Multivariantes, 2002 (McGraw Hill: Madrid).
23. Pernía Espinoza, A.V., Castejón Limas, M., González Marcos, A. and Lobato Rubio, V., Steel annealing furnace robust neural nertwok model. Ironmaking Steelmaking, 2005, 32, 418-426.
24. Regalia, R. and Sibila, S., CAD and CFD simulation in internal mixers. Gummi, Fasern, Kunststoffe, 199, 52, 264-269.
25. Royo, J., Manual de Tecnologia del Caucho, 1989 (Consorcio Nacional de Industriales del. Caucho: Madrid).
26. Saha, S., Rheological and morphological characteristics of polyvinylchloride/polychloroprene blends - effect of temperatura and mixing speed. Eur. Polym. J., 2001, 37, 399-410.
27. Sammon, J.W., A nonlinear mapping for data structure analysis. IEEE Trans Comput., 1969, 18, 401-409.
28. Schwartz, G.A., Prediction of rheometric properties of compounds by using artificial neural networks. Rubber Chem. Technol., 2001, 74, 116-123.
29. Sirisinha, C., Bauler-Limcharoen, S. and Thunyarittikorn, J., Relationships among blending conditions, size of dispersed phase, and oil resistance in natural rubber and nitrile rubber blends. J. Appl. Polym. Sci., 2001, 82, 1232-1237.
30. Sombatsompop, N. and Dangtangee, R., Effects of the actual diameters and diameter ratios of barrels and dies on the elastic swell and entrance pressure drop of natural rubber in capillary die flow. J. Appl. Polym. Sci., 2002, 86, 1762-1772.
31. The R Development Core Team. R: A Language and Environment for Statistical Computing. Available online at: http://cran.r-project.org/doc/ manuals/fullrefman.pdf (accessed 15 January 2006).
32. Thomas, M.D.H., Dolbey, P.C., Binding, D.R. and Cassidy, M., Extrusion of rubber: prediction of die swell. Kautschuk und Gummi Kunststoffe, 1997, 50, 900-902.
33. Vapnik, V.N., The Nature of Statistical Learning Theory, Second edition, 2000 (Springer-Verlag: New York).
34. Wu, C.-Y. and Hsu, Y.-C., Optimal shape design of an extrusion die using polynomial networks and genetic algorithms. Int. J. Advd Mfg Technol., 2002, 19, 79-87.
35. Zhang, A., Wang, L. and Zhou, Y., A study on rheological properties of carbon black extended powdered SBR using a torque rheometer. Polym. Testing, 2003, 22, 133-141.