Suitable Die Shaping for a Rectangular Shape Bottle by Application of FEM and AI Technique

Authors: N. Ploysook, R. Rugsaj, C. Suvanjumrat

Abstract:

The characteristic requirement for producing rectangular shape bottles was a uniform thickness of the plastic bottle wall. Die shaping was a good technique which controlled the wall thickness of bottles. An advance technology which was the finite element method (FEM) for blowing parison to be a rectangular shape bottle was conducted to reduce waste plastic from a trial and error method of a die shaping and parison control method. The artificial intelligent (AI) comprised of artificial neural network and genetic algorithm was selected to optimize the die gap shape from the FEM results. The application of AI technique could optimize the suitable die gap shape for the parison blow molding which did not depend on the parison control method to produce rectangular bottles with the uniform wall. Particularly, this application can be used with cheap blow molding machines without a parison controller therefore it will reduce cost of production in the bottle blow molding process.

Keywords: AI, bottle, die shaping, FEM.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1099666

References:

[1] N.C. Lee, Plastic Blow Molding Handbook. New York: Van Nostrand Reinhold, 1990.
[2] S.L. Belcher, Practical Extrusion Blow Molding. USA: CRC Press, 1999.
[3] H.G. deLorenzi, H.F. Nied, Finite Element Simulation of Thermoforming and Blow Molding. New York: Oxford University Press, 1991, ch. 5.
[4] S.I. Tanifuji, “Overall numerical simulation,” Polym Eng. Sci., vol. 40, no. 8, pp. 1878–1893, Aug. 2000.
[5] G. Marckmann, E. Verron, B. Peseux, “Finite element analysis of blow molding and thermoforming using a dynamic explicit procedure,” Polym Eng. Sci., vol. 41, no. 3, pp. 426–439, Mar. 2001.
[6] C. Gauvin, F. Thibault, D. Laroche, “Optimization of blow molded part performance through process simulation,” Polym Eng. Sci., vol. 44, no. 7, pp. 1407–1414, Jul. 2003.
[7] J.C. Yu, X.X. Chen, T.R. Hung, F. Thibault, “Optimization of extrusion blow molding process using soft computing and Taguchi’s method,” J. Intell. Manuf., vol. 15, pp. 625–634, 2004.
[8] G.Q. Huang, H.X. Huang, “Optimizing parison thickness for extrusion blow molding by hybrid method,” J. Mater. Process Tech, vol. 182, pp. 512–518, 2006.
[9] J.C. Yu, J.Y. Juang, “Design optimization of extrusion-blow –molded parts using prediction-reliability-guided search of evolving network modeling,” J. Mater. Process Tech, vol. 182, pp. 512–518, 2006.
[10] D. Laroche, K.K. Kabanemi, “Integrated numerical modeling of the blow molding process,” Polym. Eng. Sci., vol. 39, no. 7, pp. 1223–1233, 1999.
[11] N.C. Lee, Blow Molding Design Guide. Munich: Carl Hanser Verlag, 1998.
[12] M.T. Hagan, H.B. Demuh, M. Beals, Neural Network Design. Boston: PWS Publishing Company, 1996.