Startseite Optimization of Injection Stretch Blow Molding: Part I – Defining Part Thickness Profile
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Optimization of Injection Stretch Blow Molding: Part I – Defining Part Thickness Profile

  • R. Denysiuk , N. Gonçalves , R. Pinto , H. Silva , F. Duarte , J. Nunes und A. Gaspar-Cunha
Veröffentlicht/Copyright: 19. Juni 2019
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Abstract

This paper suggests a methodology based on a neuroevolutionary approach to optimize the use of material in blow molding applications. This approach aims at determining the optimal thickness distribution for a certain blow molded product as a function of its geometry. Multiobjective search is performed by neuroevolution to reflect the conflicting nature of the design problem and to capture some possible trade-offs. During the search, each design alternative is evaluated through a finite element analysis. The coordinates of the mesh elements are the inputs to an artificial neural network whose output determines the thickness for the corresponding location. The proposed approach is applied to the design of an industrial bottle. The results reveal the validity and usefulness of the proposed technique, which was able to distribute the material along the most critical regions to obtain adequate mechanical properties. The approach is general and can be applied to products with different geometries.


*Correspondence address, Mail address: Renê de Souza Pinto, Institute for Polymer and Composites, University of Minho, 4804-533 Guimarães, Braga, Portugal, E-mail:

References

ANSYS, “POST1 and POST26 – Interpretation of Equivalent Strains”, ANSYS Mechanical APDL Theory Reference, 15 (2013)Suche in Google Scholar

Beer, F. P., Johnston, R., Dewolf, J. and Mazurek, D.: Mechanics of Materials, McGraw-Hill, Boston (2006)Suche in Google Scholar

Beume, N., Naujoks, B. and Emmerich, M., “SMS-EMOA: Multiobjective Selection Based on Dominated Hypervolume”, Eur. J. Oper. Res., 181, 16531669 (2007) 10.1016/j.ejor.2006.08.008Suche in Google Scholar

Biglione, J., Béreaux, Y., Charmeau, J.-Y., Balcaen, J. and Chhay, S., “Numerical Simulation and Optimization of the Injection Blow Molding of Polypropylene Bottles-A Single Stage Process”, Int. J. Mater. Form., 9, 471487 (2016) 10.1007/s12289-015-1234-ySuche in Google Scholar

Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms, 16th Volume, John Wiley & Sons, New York (2001)Suche in Google Scholar

Denysiuk, R., Duarte, F. M., Nunes, J. P. and Gaspar-Cunha, A., “Evolving Neural Networks to Optimize Material Usage in Blow Molded Containers”, EUROGEN – International Conference on Evolutionary and Deterministic Methods for Design Optimization and Control with Applications to Industrial and Societal Problems, Madrid (2017)10.1007/978-3-319-89890-2_32Suche in Google Scholar

Diraddo, R. W., Garcia-Rejon, A., “On-Line Prediction of Final Part Dimensions in Blow Molding: A Neural Network Computing Approach”, Polym. Eng. Sci., 33, 653664 (1993) 10.1002/pen.760331102Suche in Google Scholar

Gaspar-Cunha, A., Viana, J. C., “Using Multi-Objective Evolutionary Algorithms to Optimize Mechanical Properties of Injection Molded Parts”, Int. Polym. Proc., 20, 274285 (2005) 10.3139/217.1889Suche in Google Scholar

Huang, G.-Q., Huang, H.-X., “Optimizing Parison Thickness for Extrusion Blow Molding by Hybrid Method”, J. Mater. Prod. Technol., 182, 512518 (2007) 10.1016/j.jmatprotec.2006.09.015Suche in Google Scholar

Huang, H.-X., Liao, C.-M., “Prediction of Parison Swell in Extrusion Blow Molding Using Neural Network Method (1101). SPE ANTEC Tech. Papers, 804807 (2002)Suche in Google Scholar

Huang, H.-X., Lu, S., “Neural Modeling of Parison Extrusion in Extrusion Blow Molding”, J. Reinf. Plast. Compos., 24, 10251034 (2005) 10.1177/0731684405048201Suche in Google Scholar

Laroche, D., Kabanemi, K. K., Pecora, L. and Diraddo, R. W., “Integrated Numerical Modeling of the Blow Molding Process”, Polym. Eng. Sci., 39, 12231233 (1999) 10.1002/pen.11509Suche in Google Scholar

Mises, R. v., “Mechanik der festen Körper im plastisch-deformablen Zustand”, Nachrichten von der Gesellschaft der Wissenschaften zu Göttingen, Mathematisch-Physikalische Klasse, 582–592 (1913)Suche in Google Scholar

Storn, R., Price, K., “Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces”, J. Global Optim., 11, 341359 (1997) 10.1023/A:1008202821328Suche in Google Scholar

Thibault, F., Malo, A., Lanctot, B. and Diraddo, R., “Preform Shape and Operating Condition Optimization for the Stretch Blow Molding Process”, Polym. Eng. Sci., 47, 289301 (2007)10.1002/pen.20707Suche in Google Scholar

Yang, Z., Naeem, W., Menary, G., Deng, J. and Li, K., “Advanced Modelling and Optimization of Infared Oven in Injection Stretch Blow-Moulding for Energy Saving”, IFAC Proceedings Volumes, 47, 766771 (2014) 10.3182/20140824-6-ZA-1003.01191Suche in Google Scholar

Zitzler, E., Thiele, L., “Multiobjective Optimization Using Evolutionary Algorithms – A Comparative Case Study”, International Conference on Parallel Problem Solving from Nature, Amsterdam (1998) 10.1007/BFb0056872Suche in Google Scholar

Received: 2018-08-02
Accepted: 2018-11-10
Published Online: 2019-06-19
Published in Print: 2019-07-03

© 2019, Carl Hanser Verlag, Munich

Heruntergeladen am 18.9.2025 von https://www.degruyterbrill.com/document/doi/10.3139/217.3746/html
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