Advertisement

Automated and robust multi-objective optimal design of thin-walled product injection process based on hybrid RBF-MOGA

  • QingQing Feng
  • Xionghui ZhouEmail author
ORIGINAL ARTICLE
  • 55 Downloads

Abstract

An automated and robust multi-objective optimal design system has been developed and carried out on the optimization of injection process parameters. A hybrid multi-objective optimization of process parameters in plastic injection molding (PIM) is implemented for a synchronous decrease in multi objectives. The main defects indicating plastic part quality including warpage, volume shrinkage, and weldline are the objectives of optimization. Melt temperature, injection time, cooling time, mold temperature, packing pressure, and packing time are considered as optimization parameters. Orthogonal array-Latin hypercube sampling (OA-LHS) method is adopted to generate the initial set of parameter points. Metamodeling technique, radial basis functions (RBF), is utilized to construct the response surface fitting process parameters and simulation responses. RBF replaces expensive simulation experiments and reduces the time and computation cost. For a quick search of optimal point, Pareto-ranking-based multi-objective genetic algorithm (MOGA) is performed to make a trade-off among three objectives. Accuracy analysis shows a lower prediction error of the proposed algorithm, and sensitivity analysis identifies the parameters which have significant influence on response. Simulation experiments are implemented for verification of the optimization. The proposed optimization is automated with the help of visual basic scripts, python, Applications Programming Interface (API) of Moldflow and Dakota. The system is applied to an industrial application in thin-walled automobile air-conditioner vent injection process which increases efficiency and accuracy in optimal design compared to conventional trial-and-error design process.

Keywords

PIM Multi-objective optimization Automation Sensitivity analysis Hybrid RBF-MOGA 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

The authors would like to acknowledge Sandia National Laboratories for sharing DAKOTA as open-source software.

References

  1. 1.
    Nian SC, Wu CY, Huang MS (2015) Warpage control of thin-walled injection molding using local mold temperatures. Int Commun Heat Mass 61:102–110CrossRefGoogle Scholar
  2. 2.
    Kurt M, Saban Kamber O, Kaynak Y, Atakok G, Girit O (2009) Experimental investigation of plastic injection molding: assessment of the effects of cavity pressure and mold temperature on the quality of the final products. Mater Des 30(8):3217–3224CrossRefGoogle Scholar
  3. 3.
    Kurtaran H, Erzurumlu T (2005) Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm. Int J Adv Manuf Technol 27(5–6):468–472Google Scholar
  4. 4.
    Gao Y, Wang X (2007) An effective warpage optimization method in injection molding based on the Kriging model. Int J Adv Manuf Technol 37(9–10):953–960Google Scholar
  5. 5.
    Chen C, Chuang M, Hsiao Y, Yang Y, Tsai C (2009) 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):10752–10759CrossRefGoogle Scholar
  6. 6.
    Farshi B, Gheshmi S, Miandoabchi E (2011) Optimization of injection molding process parameters using sequential simplex algorithm. Mater Des 32(1):414–423CrossRefGoogle Scholar
  7. 7.
    Hakimian E, Sulong AB (2012) Analysis of warpage and shrinkage properties of injection-molded micro gears polymer composites using numerical simulations assisted by the Taguchi method. Mater Des 42:62–71CrossRefGoogle Scholar
  8. 8.
    Masato D, Rathore J, Sorgato M, Carmignato S, Lucchetta G (2017) Analysis of the shrinkage of injection-molded fiber-reinforced thin-wall parts. Mater Des 132:496–504CrossRefGoogle Scholar
  9. 9.
    Kitayama S, Yokoyama M, Takano M, Aiba S (2017) Multi-objective optimization of variable packing pressure profile and process parameters in plastic injection molding for minimizing warpage and cycle time. Int J Adv Manuf Technol 92(9–12):3991–3999CrossRefGoogle Scholar
  10. 10.
    Che ZH (2010) PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding. Comput Ind Eng 58(4):625–637CrossRefGoogle Scholar
  11. 11.
    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(6):3457–3464CrossRefGoogle Scholar
  12. 12.
    Deng Y, Zhang Y, Lam YC (2010) A hybrid of mode-pursuing sampling method and genetic algorithm for minimization of injection molding warpage. Mater Des 31(4):2118–2123CrossRefGoogle Scholar
  13. 13.
    Moayyedian M, Abhary K, Marian R (2018) Optimization of injection molding process based on fuzzy quality evaluation and Taguchi experimental design. CIRP J Manuf Sci Technol 21:150–160CrossRefGoogle Scholar
  14. 14.
    Oliaei E, Heidari BS, Davachi SM, Bahrami M, Davoodi S, Hejazi I, Seyfi J (2016) Warpage and shrinkage optimization of injection-molded plastic spoon parts for biodegradable polymers using Taguchi, ANOVA and artificial neural network methods. J Mater Sci Technol 32(8):710–720CrossRefGoogle Scholar
  15. 15.
    Xia W, Luo B, Liao X (2011) An enhanced optimization approach based on Gaussian process surrogate model for process control in injection molding. Int J Adv Manuf Technol 56(9–12):929–942CrossRefGoogle Scholar
  16. 16.
    Cheng J, Liu Z, Tan J (2012) Multiobjective optimization of injection molding parameters based on soft computing and variable complexity method. Int J Adv Manuf Technol 66(5–8):907–916Google Scholar
  17. 17.
    Chen W, Liou P, Chou S (2014) An integrated parameter optimization system for MIMO plastic injection molding using soft computing. Int J Adv Manuf Technol 73(9–12):1465–1474CrossRefGoogle Scholar
  18. 18.
    Wang Y, Kim J, Song J (2014) Optimization of plastic injection molding process parameters for manufacturing a brake booster valve body. Mater Des 56:313–317CrossRefGoogle Scholar
  19. 19.
    Kitayama S, Natsume S (2014) Multi-objective optimization of volume shrinkage and clamping force for plastic injection molding via sequential approximate optimization. Simul Model Pract Theory 48:35–44CrossRefGoogle Scholar
  20. 20.
    Kitayama S, Tamada K, Takano M, Aiba S (2018) Numerical and experimental investigation on process parameters optimization in plastic injection molding for weldlines reduction and clamping force minimization. Int J Adv Manuf Technol 97:2087–2098CrossRefGoogle Scholar
  21. 21.
    Zhang J, Wang J, Lin J, Guo Q, Chen K, Ma L (2015) Multiobjective optimization of injection molding process parameters based on Opt LHD, EBFNN, and MOPSO. Int J Adv Manuf Technol 85(9–12):2857–2872Google Scholar
  22. 22.
    Wu C, Ku C, Pai H (2010) Injection molding optimization with weld line design constraint using distributed multi-population genetic algorithm. Int J Adv Manuf Technol 52(1–4):131–141Google Scholar
  23. 23.
    Heidari BS, Davachi SM, Moghaddam AH, Seyfi J, Hejazi I, Sahraeian R, Rashedi H (2018) Optimization simulated injection molding process for ultrahigh molecular weight polyethylene nanocomposite hip liner using response surface methodology and simulation of mechanical behavior. J Mech Behav Biomed Mater 81:95–105CrossRefGoogle Scholar
  24. 24.
    Park HS, Dang XP (2010) Structural optimization based on CAD–CAE integration and metamodeling techniques. Comput Aided Des 42(10):889–902CrossRefGoogle Scholar
  25. 25.
    Wang Y, Yu K, Wang CCL (2015) Spiral and conformal cooling in plastic injection molding. Comput Aided Des 63:1–11CrossRefGoogle Scholar
  26. 26.
    Kitayama S, Miyakawa H, Takano M, Aiba S (2016) 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):1735–1744Google Scholar
  27. 27.
    Dang XP (2014) General frameworks for optimization of plastic injection molding process parameters. Simul Model Pract Theory 41:15–27CrossRefGoogle Scholar
  28. 28.
    Zhao J, Cheng G, Ruan S, Li Z (2015) 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(9–12):1813–1826CrossRefGoogle Scholar
  29. 29.
    Owen AB (1992) Orthogonal arrays for computer experiments, integration, and visualization. Statistica Sinica 14Google Scholar
  30. 30.
    Konak A, Coit DW, Smith AE (2006) Multi-objective optimization using genetic algorithms: a tutorial. Reliab Eng Syst Saf 91(9):992–1007CrossRefGoogle Scholar
  31. 31.
    Brian M, Adams MSE, Michael S. Eldred, Gianluca Geraci, John D. Jakeman KAM, Jason A. Monschke, J. Adam Stephens, Laura P. Swiler DMV, Timothy M. Wildey (2009) Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, And sensitivity analysis: version 6.8 User’s manualGoogle Scholar
  32. 32.
    Kim KH, Park JC, Suh YS, Koo BH (2016) Interactive robust optimal design of plastic injection products with minimum weldlines. Int J Adv Manuf Technol 88(5–8):1333–1344Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.National Engineering Research Center of Die and Mold CADShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations