Skip to main content

Advertisement

SpringerLink
Log in

Experimental-Based Multi-objective Optimization of Injection Molding Process Parameters

  • Research Article - Mechanical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

In this paper, the framework for determining the optimum injection molding process parameters for minimized product defects through experimental-based multi-objective optimization is presented. Two defects with regard to product quality, namely warpage and volumetric shrinkage, were examined. Seven injection molding process parameters are considered including the mold temperature, melt temperature, packing pressure, packing time, cooling time, injection speed and injection pressure. Specific test points determined, within a defined domain, using the face-centered central composite design approach were used to conduct actual injection molding experiments. Warpage and volumetric shrinkage were computed for the resulting injection-molded experimental products. Two relationships between the two defects and the process parameters, respectively, were constructed and formed the basis for the optimization. Using the two relationships, a multi-objective problem entailing minimization of the two defects was formulated and solved using the genetic algorithm. The results indicate a significant tradeoff between the warpage and the volumetric shrinkage. Assuming equal importance in minimizing both defects, additional experiments were conducted to validate the corresponding optimum. The experimental results revealed close agreements with the optimization results differing by about 7%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Han, S.-Y., et al.: A new process of gas-assisted injection molding for faster cooling. J. Mater. Process. Technol. 155–156, 1201–1206 (2004)

    Article  Google Scholar 

  2. Black, J.T.; Kohser, R.A.; DeGarmo, E.P.: DeGarmo’s Materials and Processes in Manufacturing. Wiley, Hoboken (2013)

    Google Scholar 

  3. Johannaber, F.: Injection Molding Machines: A User’s Guide. Hanser Publication, Munich (2008)

    Google Scholar 

  4. Dang, X.-P.: General frameworks for optimization of plastic injection molding process parameters. Simul. Model. Pract. Theory 41, 15–27 (2014)

    Article  Google Scholar 

  5. Brydson, J.: Plastics Materials. Elsevier, Amsterdam (1995)

    Google Scholar 

  6. Pötsch, G.; Michaeli, W.: Injection Molding: An Introduction. Hanser Publication, Munich (2008)

    Google Scholar 

  7. Yin, F.; Mao, H.; Hua, L.: A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters. Mater. Des. 32(6), 3457–3464 (2011)

    Article  Google Scholar 

  8. Shoemaker, J.: Moldflow Design Guide: A Resource for Plastics Engineers. Moldflow Design Guide. Hanser Publication, Munich (2006)

    Book  Google Scholar 

  9. Farshi, B.; Gheshmi, S.; Miandoabchi, E.: Optimization of injection molding process parameters using sequential simplex algorithm. Mater. Des. 32, 414–423 (2011)

    Article  Google Scholar 

  10. Zhao, J., et al.: Multi-objective optimization design of injection molding process parameters based on the improved efficient global optimization algorithm and non-dominated sorting-based genetic algorithm. Int. J. Adv. Manuf. Technol. 78, 1813–1826 (2015)

    Article  Google Scholar 

  11. Xu, Y., et al.: Optimization of injection molding process parameters to improve the mechanical performance of polymer product against impact. Int. J. Adv. Manuf. Technol. 76(9–12), 2199–2208 (2014)

    Google Scholar 

  12. Altan, M.: Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods. Mater. Des. 31(1), 599–604 (2010)

    Article  Google Scholar 

  13. Chen, C.-C.; Su, P.-L.; Lin, Y.-C.: Analysis and modeling of effective parameters for dimension shrinkage variation of injection molded part with thin shell feature using response surface methodology. Int. J. Adv. Manuf. Technol. 45(11–12), 1087–1095 (2009)

    Article  Google Scholar 

  14. Gao, Y.; Wang, X.: An effective warpage optimization method in injection molding based on the Kriging model. Int. J. Adv. Manuf. Technol. 37(9–10), 953–960 (2007)

    Google Scholar 

  15. Gao, Y.; Wang, X.: Surrogate-based process optimization for reducing warpage in injection molding. J. Mater. Process. Technol. 209(3), 1302–1309 (2009)

    Article  Google Scholar 

  16. Kurtaran, H.; Erzurumlu, T.: Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm. Int. J. Adv. Manuf. Technol. 27(5–6), 468–472 (2005)

    Google Scholar 

  17. 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. 169(2), 314–319 (2005)

    Article  Google Scholar 

  18. Mathivanan, D.; Parthasarathy, N.S.: Prediction of sink depths using nonlinear modeling of injection molding variables. Int. J. Adv. Manuf. Technol. 43(7–8), 654–663 (2008)

    Google Scholar 

  19. Ozcelik, B.; Erzurumlu, T.: Determination of effecting dimensional parameters on warpage of thin shell plastic parts using integrated response surface method and genetic algorithm. Int. Commun. Heat Mass Transf. 32, 1085–1094 (2005)

    Article  Google Scholar 

  20. Park, G.-J.: Analytic Methods for Design Practice. Springer, Berlin (2007)

    MATH  Google Scholar 

  21. Shen, C.; Wang, L.; Li, Q.: Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method. J. Mater. Process. Technol. 183(2–3), 412–418 (2007)

    Article  Google Scholar 

  22. Yin, F., et al.: Back propagation neural network modeling for warpage prediction and optimization of plastic products during injection molding. Mater. Des. 34, 1844–1850 (2011)

    Article  Google Scholar 

  23. Choi, G.H., et al.: Optimization of process parameters of injection molding with neural network application in a process simulation environment. Ann. CIRP 43, 449–452 (1994)

    Article  Google Scholar 

  24. Kamoun, A.; Jaziri, M.; Chaabouni, M.: The use of the simplex method and its derivatives to the on-line optimization of the parameters of an injection moulding process. Chemometr. Intell. Lab. Syst. 96(2), 117–122 (2009)

    Article  Google Scholar 

  25. Ozcelik, B.; Sonat, I.: Warpage and structural analysis of thin shell plastic in the plastic injection molding. Mater. Des. 30(2), 367–375 (2009)

    Article  Google Scholar 

  26. Zhou, J.; Turng, L.-S.; Kramschuster, A.: Single and multi objective optimization for injection molding using numerical simulation with surrogate models and genetic algorithms. Int. Polym. Proc. 21, 509–520 (2006)

    Article  Google Scholar 

  27. Ozcelik, B.; Erzurumlu, T.: Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm. J. Mater. Process. Technol. 171(3), 437–445 (2006)

    Article  Google Scholar 

  28. Shen, C.Y.; Wang, L.X.; Zhang, Q.X.: Process optimization of injection molding by the combining ANN/HGA method. Polym. Mater. Sci. Eng. 21(5), 23–27 (2005)

    Google Scholar 

  29. Wu, C.-Y.; Ku, C.-C.; Pai, H.-Y.: Injection molding optimization with weld line design constraint using distributed multi-population genetic algorithm. Int. J. Adv. Manuf. Technol. 52(1–4), 131–141 (2010)

    Google Scholar 

  30. Chen, C.P., et al.: Simulation and experimental study in determining injection molding process parameters for thin-shell plastic parts via design of experiments analysis. Expert Syst. Appl. 36(7), 10752–10759 (2009)

    Article  Google Scholar 

  31. Lam, Y.C.; Deng, Y.M.; Au, C.K.: A GA/gradient hybrid approach for injection moulding conditions optimisation. Eng. Comput. 21(3), 193–202 (2006)

    Article  Google Scholar 

  32. Kitayama, S., et al.: Multi-objective optimization of injection molding process parameters for short cycle time and warpage reduction using conformal cooling channel. Int. J. Adv. Manuf. Technol. 88(5–8), 1735–1744 (2016)

    Google Scholar 

  33. Mohan, M.; Ansari, M.N.M.; Shanks, R.A.: Review on the effects of process parameters on strength, shrinkage, and warpage of injection molding plastic component. Polym. Plast. Technol. Eng. 56, 1–12 (2017)

    Article  Google Scholar 

  34. Deng, W.-J., et al.: An effective approach for process parameter optimization in injection molding of plastic housing components. Polym. Plast. Technol. Eng. 47, 910–919 (2008)

    Article  Google Scholar 

  35. Arburg: Allrounder 420 C Golden Edition. 2016 [cited 2018 June]; Available from: https://www.arburg.com/fileadmin/redaktion/Mediathek/Technische_Daten/ARBURG_ALLROUNDER_420C_GOLDEN_EDITION_TD_523677_en_GB.pdf

  36. Sabic: SABIC HDPE M80064. [cited 2018 June]; Available from: https://cdn2.hubspot.net/hubfs/465603/Dokumenter/SABIC_M80064.pdf?t=1497093858810.

  37. Oehlert, G.W.: A first course in design and analysis of experiments. Am. Stat. 57, 66–67 (2003)

    Google Scholar 

  38. Dean, A., et al.: Design and Analysis of Experiments. Nature BiotechnologySpringer, Berlin (1999)

    Book  MATH  Google Scholar 

  39. Ramachandra, K.: Kalman Filtering Techniques for Radar Tracking, p. 256. Marcel Dekker, Inc., Basel (2000)

    Book  Google Scholar 

  40. Martin, J.D.; Simpson, T.W.: Use of kriging models to approximate deterministic computer models. AIAA J. 43(4), 853–863 (2005)

    Article  Google Scholar 

  41. Lophaven, S.N.; Søndergaard, J.; Nielsen, H.B.: DACE A Matlab Kriging Toolbox. IMM Informatics and Mathematical Modelling, The Technical University of Denmark, Lyngby (2002)

    Google Scholar 

  42. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, Boston (1989)

    MATH  Google Scholar 

  43. Goldberg, D.E.; Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. Found Genet Algorithms 1, 69–93 (1991)

    MathSciNet  Google Scholar 

  44. Chipperfield, A.J., et al.: Genetic Algorithm Toolbox: for Use with MATLAB; User’s Guide (Version 1.2). Department of Automatic Control and Systems Engineering, University of Sheffield (1994)

Download references

Acknowledgements

The work in this research has been supported by the Scientific Research Deanship at Qassim University under Grant No. 1705qec-2016-1-12-S. The authors would also like to acknowledge the technical support from the higher institute for plastic fabrication (HIPF) in Riyadh (Kingdom of Saudi Arabia).

Author information

Authors and Affiliations

  1. Mechanical Engineering Department, Qassim University, PO Box 6677, Buraydah, Qassim, 51452, Kingdom of Saudi Arabia

    Saad M. S. Mukras, Hanafy M. Omar & Fahad A. al-Mufadi

Authors
  1. Saad M. S. Mukras
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hanafy M. Omar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fahad A. al-Mufadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saad M. S. Mukras.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mukras, S.M.S., Omar, H.M. & al-Mufadi, F.A. Experimental-Based Multi-objective Optimization of Injection Molding Process Parameters. Arab J Sci Eng 44, 7653–7665 (2019). https://doi.org/10.1007/s13369-019-03855-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-019-03855-1

Keywords

Search

Navigation