Startseite Single and Multi Objective Optimization for Injection Molding Using Numerical Simulation with Surrogate Models and Genetic Algorithms
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Single and Multi Objective Optimization for Injection Molding Using Numerical Simulation with Surrogate Models and Genetic Algorithms

  • J. Zhou , L.-S. Turng und A. Kramschuster
Veröffentlicht/Copyright: 10. Mai 2022
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Abstract

The objective of this study is to develop an integrated computer-aided engineering (CAE) optimization system that can quickly and intelligently determine the optimal process conditions for injection molding. This study employs support vector regression (SVR) to establish the surrogate model based on executions of three-dimensional (3D) simulation for a selected dataset using the latin hypercube sampling (LHS) technique. Once the surrogate model can satisfactorily capture the characteristics of simulations with much less computing resources, a hybrid optimization genetic algorithm (GA) or a multi-objective optimization GA is then used to evaluate the surrogate model to search the global optimal solutions for the single or multiple objectives, respectively. The performance and capabilities of other surrogate modeling approaches, such as polynomial regression (PR) and artificial neural network (ANN), are also investigated in terms of accuracy, robustness, efficiency, and requirements for training samples. Experimental validations and applications of this work for process optimization of a special box mold and a precision optical lens are presented.


* Mail address: L.-S. Turng, Polymer Engineering Center, Department of Mechanical Engineering, University of Wisconsin-Madison, 1513 University Avenue, Madision WI 53706, USA


Acknowledgements

The authors would like to thank Moldex3D for generously providing the computer simulation software for this study. This work was partially supported by the 3M Precision Optics and the National Science Foundation (DMI-0323509).

References

1 Kim, S. W., Turng, L.-S.: Modeling Simul. Mater. Sci. Eng. 12, p. S151 (2004)10.1088/0965-0393/12/3/S07Suche in Google Scholar

2 Tucker, C. L.: Fundamentals of Computer Modeling for Polymer Processing. Hanser, Munich (1989)Suche in Google Scholar

3 Chang, R. Y., Yang, W. H.: Int. J. Numer. Methods Fluids 37, p. 125 (2001)10.1002/fld.166Suche in Google Scholar

4 Khayat, R. E., Plaskos, C., Genouvrier, D.: Int. J. Numer. Methods Engineering 50, p. 1347 (2001)10.1002/1097-0207(20010228)50:6<1347::AID-NME61>3.0.CO;2-WSuche in Google Scholar

5 Illinca, F., Hetu, J. F.: Int. J. Numer. Methods Engineering 53, p. 2003 (2002)10.1002/nme.370Suche in Google Scholar

6 Park, S. J., Kwon, T. H.: Polym. Eng. Sci. 38, p. 1450 (1998)10.1002/pen.10316Suche in Google Scholar

7 Turng, L. S., Peic, M., Bradley, D. K.: J. Injection Molding Technology 6, p. 143 (2002)Suche in Google Scholar

8 Deng, Y. M., Lam, Y. C., Britton, G. A.: Int. J. Prod. Res. 42 (7), p. 1365 (2004)10.1080/00207540310001632475Suche in Google Scholar

9 Castro, C., Cabrera-Rios, M., Lilly, B., Castro, J., Mount-Campbell, C.: J. Polym. Eng. 25 (6), p. 459 (2005)10.1515/POLYENG.2005.25.6.459Suche in Google Scholar

10 Vapnik, V. N., Chervonenkis, A. Y.: Theory of Probability and Its Applications 17, p. 264 (1971)10.1137/1116025Suche in Google Scholar

11 Vapnik, V. N.: Statistical Learning Theory. Springer, New York (1998)Suche in Google Scholar

12 Smola, A. J., Schölkopf, B.: Statistics and Computing 14, p. 199 (2004)10.1023/B:STCO.0000035301.49549.88Suche in Google Scholar

13 Holland, J.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)Suche in Google Scholar

14 Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, New York (1994)10.1007/978-3-662-07418-3Suche in Google Scholar

15 Goldberg, D. E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, New York (1989)Suche in Google Scholar

16 Xu, Y. G., Li, G. R., Wu, Z. P.: Applied Artificial Intelligence 15, p. 601 (2001)10.1080/088395101750363966Suche in Google Scholar

17 Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: IEEE Trans. Evol. Comp. 6 (2), p.182 (2002)10.1109/4235.996017Suche in Google Scholar

18 Moldex3D. http://www.moldex3d.com CoreTech System Co., Ltd.Suche in Google Scholar

19 Kleijnen, J. P. C., Sanchez, S. M., Lucas, T. W., Cioppa, T. M.: INFORMS Journal on Computing 17, p. 263 (2005)10.1287/ijoc.1050.0136Suche in Google Scholar

20 Kramschuster, A., Cavitt, R., Ermer, D., Chen, Z. B., Turng, L. S.: Polym. Eng. Sci. 45, p. 1408 (2005)10.1002/pen.20410Suche in Google Scholar

21 Kaufman, M., Balabanov, V., Burgee, S. L., Giunta, A. A., Grossman, B., Mason,W. H.,Watson, L. T.: 34th Aerospace Science Meeting and Exhibit, AIAA Paper 96-0089Suche in Google Scholar

Received: 2006-04-07
Accepted: 2006-07-13
Published Online: 2022-05-10

© 2006 Walter de Gruyter GmbH, Berlin/Boston, Germany

Artikel in diesem Heft

  1. Contents
  2. Rapid Communications
  3. A Novel High Flow Rate Pin for Water-assisted Injection Molding of Plastic Parts with a More Uniform Residual Wall Thickness Distribution
  4. Regular Contributed Articles
  5. Structure Property Relationships in PA 6 and PP Copolymers Blended by Single and Twin Screw Extrusion
  6. Stretchability and Properties of Biaxially Oriented Polypropylene Film
  7. Dynamic Mold Surface Temperature Control Using Induction and Heater Heating Combined with Coolant Cooling
  8. Visualization of Melt-Flow Behavior Inside the Runner in Ultra High Speed Injection Molding
  9. Effects of Cavity Conditions on Transcription Molding of Microscale Prism Patterns Using Ultra-High-Speed Injection Molding
  10. Effect of Melt and Mold Temperature on Fiber Orientation during Flow in Injection Molding of Reinforced Plastics
  11. Invited Paper
  12. Polymer/Layered Silicate Nano-composites
  13. Regular Contributed Articles
  14. Paste Extrusion of Polytetrafluoroethylene: Temperature, Blending and Processing Aid Effects
  15. Influence of Viscosity-interface Modifier Interactions on Performance and Processability of Rice Hull PE Composites
  16. Single and Multi Objective Optimization for Injection Molding Using Numerical Simulation with Surrogate Models and Genetic Algorithms
  17. A Process Classification Number for the Solidification of Crystallizing Materials
  18. Effect of Aerodynamics on Film Blowing Process
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  22. Seikei-Kakou Abstracts
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Heruntergeladen am 29.10.2025 von https://www.degruyterbrill.com/document/doi/10.3139/217.0039/pdf
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