Skip to main content
SpringerLink
Log in

Warpage reduction with variable pressure profile in plastic injection molding via sequential approximate optimization

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Warpage reduction is one of the important issues in plastic injection molding (PIM). In order to resolve this issue, there are mainly two ways to reduce warpage: One is to design the mold, and the other is to optimize the process parameters such as the mold temperature, the melt temperature, and so on. In this paper, the latter approach is employed. In particular, variable pressure profile approach is adopted for the warpage reduction. Besides the variable pressure profile, the melt temperature and the mold temperature are taken as the design variables. Also, short shot that the melt plastic is not filled into the cavity is one of the fatal defects in PIM. Unlike the literature, in this paper, the short shot is handled as the design constraint. PIM simulation is generally so costly and time consuming, and then the surrogate-based optimization technique is used. The radial basis function (RBF) network is used throughout sequential approximate optimization (SAO) procedure. Moldex3D is used for PIM simulation. In order to compare the effectiveness of the variable pressure profile, the traditional process parameter optimization considered in the literature is also carried out. Numerical results show that the variable pressure profile is one of the effective ways to warpage reduction compared to the traditional process parameter optimization.

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

Similar content being viewed by others

References

  1. Sadeghi BHM (2000) A BP-neural network predictor model for plastic injection molding process. J Mater Process Technol 103:411–416

    Article  Google Scholar 

  2. Huang MC, Tai CC (2001) The effective factors in the warpage problem of an injection-molded part with a thin shell feature. J Mater Process Technol 110:1–9

    Article  Google Scholar 

  3. Yarlagadda PKDV, Khong CAT (2001) Development of a hybrid neural network system for prediction of process parameters in injection moulding. J Mater Process Technol 118:110–116

    Article  Google Scholar 

  4. Bikas A, Pantelelis N, Kanarachos A (2001) Computational tools for the optimal design of the injection moulding process. J Mater Process Technol 122:112–126

    Article  Google Scholar 

  5. Shi F, Lou ZL, Lu JG, Zhang YQ (2003) Optimisation of plastic injection moulding process with soft computing. Int J Adv Manuf Technol 21:656–661

    Article  Google Scholar 

  6. Kurtaran H, Ozcelik B, Erzurumlu T (2005) Warpage optimization of a bus ceiling lamp base using neural network and genetic algorithm. J Mater Process Technol 169:314–319

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Changyu S, Lixia W, Qian L (2007) Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method. J Mater Process Technol 183:412–418

    Article  Google Scholar 

  9. Yin F, Mao H, Hua L, Guo W, Shu M (2011) Back propagation neural network modeling for warpage prediction and optimization of plastic products during injection molding. Mater Des 32:1844–1850

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Kurtaran H, Erzurumlu T (2006) Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm. Int J Adv Manuf Technol 27:468–472

    Article  Google Scholar 

  12. Chiang KT, Chang FP (2007) Analysis of shrinkage and warpage in an injection-molded part with a thin shell feature using the response surface methodology. Int J Adv Manuf Technol 35:468–479

    Article  Google Scholar 

  13. Mathivanan D, Parthasarathy NS (2009) Sink-mark minimization in injection molding through response surface regression modeling and genetic algorithm. Int J Adv Manuf Technol 45:867–874

    Article  Google Scholar 

  14. Gao Y, Wang X (2008) An effective warpage optimization method in injection molding based on the Kriging model. Int J Adv Manuf Technol 37:953–960

    Article  Google Scholar 

  15. Shie JR (2008) Optimization of injection molding process for contour distortions of polypropylene composite components by a radial basis neural network. Int J Adv Manuf Technol 36:1091–1103

    Article  MathSciNet  Google Scholar 

  16. Jones D, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13:455–492

    Article  MATH  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  18. Li C, Wang FL, Chang YQ, Liu Y (2010) A modified global optimization method based on surrogate model and its application in packing profile optimization of injection molding process. Int J Adv Manuf Technol 48:505–511

    Article  Google Scholar 

  19. Shi H, Gao Y, Wang X (2010) Optimization of injection molding process parameters using integrated artificial neural network model and expected improvement function method. Int J Adv Manuf Technol 48:955–962

    Article  Google Scholar 

  20. Deng YM, Zhang Y, Lam YC (2011) A hybrid of mode-pursuing sampling method and genetic algorithm for minimization of injection molding warpage. Mater Des 31:2118–2123

    Article  Google Scholar 

  21. Zhou J, Turng LS (2007) Process optimization of injection molding using an adaptive surrogate model with Gaussian process approach. Polym Eng Sci 47(5):684–694

    Article  Google Scholar 

  22. Xia W, Luo B, Liao XP (2011) An enhanced optimization approach based on Gaussian process surrogate model for process control in injection molding. Int Adv Manuf Technol 56(9–12):929–942

    Article  Google Scholar 

  23. Erzurumlu T, Ozcelik B (2006) Minimization of warpage and sink index in injection-molded thermoplastic parts using Taguchi optimization method. Mater Des 27:853–861

    Article  Google Scholar 

  24. Oktem H, Erzurumlu T, Uzman I (2007) Application of Taguchi optimization technique in determining plastic injection molding process parameters for a thin-shell part. Mater Des 28:1271–1278

    Article  Google Scholar 

  25. Oktem H (2012) Optimum process conditions on shrinkage of an injected-molded part of DVD-ROM cover using Taguchi robust method. Int J Adv Manuf Technol 61:519–528

    Article  Google Scholar 

  26. Chen WC, Wang MW, Chen CT, Fu GL (2009) An integrated parameter optimization system for MISO plastic injection molding. Int J Adv Manuf Technol 44:501–511

    Article  Google Scholar 

  27. Lam YC, Brittom GA, Liu DS (2004) Optimisation of gate location with design constraints. Int J Adv Manuf Technol 24:560–566

    Article  Google Scholar 

  28. Castro CE, Rios MC, Castro JM, Lilly B (2007) Multiple criteria optimization with variability considerations in injection molding. Polym Eng Sci 47(4):400–409

    Article  Google Scholar 

  29. Kitayama S, Arakawa M, Yamazaki K (2011) Sequential approximate optimization using radial basis function network for engineering optimization. Optim Eng 12(4):535–557

    Article  MATH  MathSciNet  Google Scholar 

  30. Nakayama H, Arakawa M, Sasaki R (2002) Simulation-based optimization using computational intelligence. Optim Eng 3:201–214

    Article  MATH  MathSciNet  Google Scholar 

  31. Kitayama S, Yamazaki K (2011) Simple estimate of the width in Gaussian kernel with adaptive scaling technique. Appl Soft Comput 11(8):4726–4737

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kanazawa University, Kakuma-machi, Kanazawa, 920-1192, Japan

    Satoshi Kitayama & Koetsu Yamazaki

  2. Graduate School of Natural Science & Technology, Kanazawa University, Kakuma-machi, Kanazawa, 920-1192, Japan

    Ryosuke Onuki

Authors
  1. Satoshi Kitayama
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ryosuke Onuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Koetsu Yamazaki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Satoshi Kitayama.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kitayama, S., Onuki, R. & Yamazaki, K. Warpage reduction with variable pressure profile in plastic injection molding via sequential approximate optimization. Int J Adv Manuf Technol 72, 827–838 (2014). https://doi.org/10.1007/s00170-014-5697-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-5697-7

Keywords

Search

Navigation