Back Propagation neural network modeling for warpage prediction and optimization of plastic products during injection molding

Materials and Design

Volumn 32, Issue 4, 2011, Pages 1844-1850

  Yin, Fei ^a   Mao, Huajie ^a   Hua, Lin ^a   Guo, Wei ^a   Shu, Maosheng ^a  

^a WUHAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

A. Polymers; C. Molding; F. Defects

Indexed keywords

A. POLYMERS; BACK PROPAGATION NEURAL NETWORKS; BP NEURAL NETWORKS; C. MOLDING; COOLING TIME; ENERGY CONSUMPTION AND PRODUCTION; ERROR RANGE; EVALUATION INDEX; F. DEFECTS; FE-SIMULATION; FINITE ELEMENT SIMULATIONS; INPUT AND OUTPUTS; KEY PROCESS; MATHEMATICAL EQUATIONS; MELT TEMPERATURE; MOLD TEMPERATURES; ORTHOGONAL EXPERIMENTAL DESIGN METHOD; PACKING PRESSURE; PLASTIC INJECTION MOLDING; PREDICTION SYSTEMS; PROCESS PARAMETER VALUES; PROCESS PARAMETERS; PRODUCTION CYCLE; SOFTWARE PLATFORMS; WARPAGE PREDICTIONS; WARPAGES;

BACKPROPAGATION; COMPUTER SIMULATION; DEFECTS; ENERGY UTILIZATION; FORECASTING; INJECTION MOLDING; MOLDS; NEURAL NETWORKS; OPTIMIZATION; POLYMERS;

PLASTIC PARTS;

EID: 79251619070     PISSN: 02641275     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.matdes.2010.12.022     Document Type: Article

References (23)

1. Deng Y.M., Zhang Y., Lam Y.C. A hybrid of mode-pursuing sampling method and genetic algorithm for minimization of injection molding warpage. Mater Des 2010, 31:2118-2123.
2. Farshi B., Gheshmi S., Miandoabchi E. Optimization of injection molding process parameters using sequential simplex algorithm. Mater Des 2010, 32:414-423.
3. Shi H.Z., Gao Y.H., Wang X.C. Optimization of injection molding process parameters using integrated artificial neural network model and expected improvement function method. Int J Adv Manuf Technol 2010, 48:955-962.
4. Kurtaran H., Ozcelik B., Erzurumlu T. Warpage optimization of a bus ceiling lamp base using neural network model and genetic algorithm. J Mater Process Technol 2005, 169:314-319.
5. Ozcelik B., Sonat I. Warpage and structural analysis of thin shell plastic in the plastic injection molding. Mater Des 2009, 30:367-375.
6. Huang M.C., Tai C.C. The effective factors in the warpage problem of an injection molded part with a thin shell feature. J Mater Process Technol 2001, 110:1-9.
7. Kong W.Y., Kim J.K. Warpage in plastic packages: effects of process conditions, geometry and materials. IEEE Trans Compon Packag Manufact 2003, 26:2345-2352.
8. Erzurumlu T., Ozcelik B. Minimization of warpage and sink index in injection-molded thermoplastic parts using Taguchi optimization method. Mater Des 2006, 27:853-861.
9. Wang G.L., Zhao G.Q., Li H.P., Guan Y.J. Influence factor analysis of warpage and optimization of process parameters in plastic injection molding of thin-wall plastic parts based on Taguchi optimization method. China Mech Eng 2009, 20:488-492.
10. Gao Y.H., Wang X.C. An effective warpage optimization method in injection molding based on the Kriging model. Int J Adv Manuf Technol 2008, 37:953-960.
11. Fogel D., Robinson C. Computational intelligence: the experts speak 2003, Wiley-IEEE Press.
12. Russell S., Norving P. Artificial intelligence: a modern approach 2004, Posts & Telecom Press, Beijing. 2nd ed.
13. He JM, Huang HX. Predicting processing parameters for water-assisted injection molding based on inverse GA-LMBP neural network. SPE Technical conference ANTEC; 2007. p. 676-9.
14. Min B.H., Shin B.C. A study on volumetric shrinkage of injection molded parts based on neural networks. J Injection Molding Technol 2001, 5:201-207.
15. Younesi M., Bahrololoom M.E., Ahmadzadeh M. Prediction of wear behaviors of nickel free stainless steel-hydroxyapatite bio-composites using artificial neural network. Comput Mater Sci 2010, 47:645-654.
16. Fu Z.M., Mo J.H., Chen L., Chen W. Using genetic algorithm-back propagation neural network prediction and finite-element model simulation to optimize the process of multiple-step incremental air-bending forming of sheet metal. Mater Des 2010, 31:267-277.
17. Dehghani K., Nekahi A. Artificial neural network to predict the effect of thermomechanical treatments on bake hardenability of low carbon steels. Mater Des 2010, 31:2224-2229.
18. Yu W.X., Li M.Q., Luo J., Su S.S., Li C.Q. Prediction of the mechanical properties of the post-forged Ti-6Al-4V alloy using fuzzy neural network. Mater Des 2010, 31:3282-3288.
19. Cook D.F., Ragsdale C.T., Major R.L. Combining a neural network with a genetic algorithm for process parameter optimization. Eng Appl Artif Intell 2000, 13:391-396.
20. Sadeghi B.H.M. A BP-neural network predictor model for plastic injection molding process. J Mater Process Technol 2000, 103:411-416.
21. Altan M. Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods. Mater Des 2010, 31:599-604.
22. Lotti C., Ueki M., Bretas R.E.S. Prediction of the shrinkage of injection molded iPP plaques using artificial neural networks. J Injection Molding Technol 2002, l6:157-175.
23. Osswald T.A., Turng L. Injection Molding Handbook 2002, Carl Hanser Verlag, Munich.