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Optimization of the injection molding process for the PC/ABS parts by integrating Taguchi approach and CAE simulation

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Abstract

This study was an application of the Taguchi method to optimize injection molding (IM) process parameters of the polycarbonate/acrylonitrile-butadiene styrene (PC/ABS) blends. A 4-factor, 3-level injection experiment was conducted using Taguchi L9 orthogonal array through the statistical design method. The parameters considered were material temperature, injection pressure, holding time, and mold temperature. The signal-to-noise (S/N) ratio was performed to obtain higher shear stress by defining the optimum injection parameters. The Taguchi method showed that injection pressure was the most influential parameter on the shear stress for the PC/ABS blends. The optimal injection parameter levels tested via Taguchi were verified via CAE simulation. SW plastics software was used to predict if the injected part contained injection defects through computation of key parameters such as the easy filling, the flow front central temperature, and the residual stress. The numerical simulation showed no injection defects observed on the injected PC/ABS parts. Therefore, the optimal combination parameters provided injected PC/ABS parts with a better shear stress and no injection defects. Such conditions would enhance the metallization process.

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Abbreviations

CAE:

computer-aided engineering

IM:

injection molding

DOE:

design of experiment

S/N:

signal-to-noise ratio

Y n :

the responses data of the shear stress

n :

the number of replicates

OA:

the orthogonal array

τ :

the shear stress

T :

tangential force in Newton

S :

section of the specimen in mm2

SW:

SolidWorks

C :

torsion torque in Nm

R :

radius of the specimen in mm

γ :

the shear strain (%)

θ :

the torsion angle calculated in rad/mm

α :

torsion angle measured in rad

x :

length of the active area in mm

H 1 :

the height of the torsion specimen

H 2 :

the height of the tensile specimen

H 3 :

the height of the dynamic tensile specimen

E 2 :

the thickness of the tensile specimen

E 3 :

the thickness of the dynamic tensile specimen

E r :

the runner system’s thickness

I p :

the injection point

T ma :

the material temperature

T mo :

the mold temperature

P inj :

the injection pressure

t h :

the holding time

MPa:

mega Pascal

°C:

Celsius temperature scale

bar:

bar pressure scale

sec:

second time scale

References

  1. Wen X, Akhter S, Afacan A et al (2007) CFD modeling of columns equipped with structured packings: I. Approach based on detailed packing geometry. https://doi.org/10.1002/apj

    Book  Google Scholar 

  2. 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. https://doi.org/10.1007/s00170-004-2321-2

    Article  Google Scholar 

  3. Jan M, Khalid MS, Awan AA, Nisar S (2016) Optimization of injection molding process for sink marks reduction by integrating response surface design methodology & taguchi approach. J Qual Technol Manag Volume XII, Issue I:45–79

  4. Chen WC, Nguyen MH, Chiu WH, Chen TN, Tai PH (2016) Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO. Int J Adv Manuf Technol 83:1873–1886. https://doi.org/10.1007/s00170-015-7683-0

    Article  Google Scholar 

  5. Custódio FJMF, Anderson PD, Peters GWM, Cunha AM, Meijer HEH (2010) Residual stresses in gas-assisted injection molding. Rheol Acta 49:23–44. https://doi.org/10.1007/s00397-009-0392-6

    Article  Google Scholar 

  6. Kim SY, Kim CH, Kim SH, Oh HJ, Youn JR (2009) Measurement of residual stresses in film insert molded parts with complex geometry. Polym Test 28:500–507

    Article  Google Scholar 

  7. Dwiwedi AK, Kumar S, Rahbar NN., & Kumar D (2015) Practical application of Taguchi method for optimization of process parameters in injection molding machine for PP material. International Research Journal of Engineering and Technology (IRJET) e-ISSN, 2395–0056

  8. Zhang Z, Jiang B (2007) Optimal process design of shrinkage and sink marks in injection molding. J Wuhan Univ Technol Mater Sci Ed 22:404–407. https://doi.org/10.1007/s11595-006-3404-8

    Article  Google Scholar 

  9. Ozcelik B (2011) Optimization of injection parameters for mechanical properties of specimens with weld line of polypropylene using Taguchi method. Int Commun Heat Mass Transf 38:1067–1072. https://doi.org/10.1016/j.icheatmasstransfer.2011.04.025

    Article  Google Scholar 

  10. Shen C, Wang L, Cao W, Qian L (2007) Investigation of the effect of molding variables on sink marks of plastic injection molded parts using Taguchi DOE technique. Polym - Plast Technol Eng 46:219–225. https://doi.org/10.1080/03602550601152887

    Article  Google Scholar 

  11. 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. https://doi.org/10.1016/j.matdes.2005.03.017

    Article  Google Scholar 

  12. Shayfull Z, Fathullah M, Sharif S, Nasir SM, Shuaib NA (2011) Warpage analysis on ultra-thin shell by using Taguchi method and analysis of variance (ANOVA) for three-plate mold. Int Rev Mech Eng 5(6):1116–1124

    Google Scholar 

  13. Kuo CJ, Su T Kuo (2006). 7:404–413

  14. Chen CP, Chuang MT, Hsiao YH, Yang YK, Tsai CH (2009) Simulation and experimental study in determining injection molding process parameters for thin-shell plastic parts via design of experiments analysis. Expert Syst Appl 36:10752–10759. https://doi.org/10.1016/j.eswa.2009.02.017

    Article  Google Scholar 

  15. Guo W, Hua L, Mao H, Meng Z (2012) Prediction of warpage in plastic injection molding based on design of experiments. J Mech Sci Technol 26:1133–1139. https://doi.org/10.1007/s12206-012-0214-0

    Article  Google Scholar 

  16. 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. https://doi.org/10.1007/s00170-002-1374-3

    Article  Google Scholar 

  17. University) DT( D, Polylefins) SPB( M process and tooling factors affecting sink marks for amorphous and crystallune resins. Vol 4, N°3

  18. Iyer N (School of MEPU, Purdue KR (School of ME A study of localized shrinkage in injection molding with high thermal conductivity molds. p Vol 6 N°2, 73

  19. 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. https://doi.org/10.1007/s00170-009-2021-z

    Article  Google Scholar 

  20. Mathivanan D, Parthasarathy NS (2009) Prediction of sink depths using nonlinear modeling of injection molding variables. Int J Adv Manuf Technol 43:654–663. https://doi.org/10.1007/s00170-008-1749-1

    Article  Google Scholar 

  21. 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. https://doi.org/10.1007/s00170-006-0739-4

    Article  Google Scholar 

  22. Chou CS, Wu CY, Yeh CH, Yang RY, Chen JH (2012) The optimum conditions for solid-state-prepared (Y 3-xCe x)Al 5O 12 phosphor using the Taguchi method. Adv Powder Technol 23:97–103. https://doi.org/10.1016/j.apt.2010.12.016

    Article  Google Scholar 

  23. Ghorbel E, Hadriche I, Casalino G, Masmoudi N (2014) Characterization of thermo-mechanical and fracture behaviors of thermoplastic polymers. Materials (Basel) 7:375–398. https://doi.org/10.3390/ma7010375

    Article  Google Scholar 

  24. Marcellan A, Bunsell AR, Laiarinandrasana L, Piques R (2006) A multi-scale analysis of the microstructure and the tensile mechanical behaviour of polyamide 66 fibre. Polymer (Guildf) 47:367–378. https://doi.org/10.1016/j.polymer.2005.10.093

    Article  Google Scholar 

  25. Neşeli S, Yaldiz S, Türkeş E (2011) Optimization of tool geometry parameters for turning operations based on the response surface methodology. Meas J Int Meas Confed 44:580–587. https://doi.org/10.1016/j.measurement.2010.11.018

    Article  Google Scholar 

  26. Lakshminarayanan AK, Balasubramania V (2008) Process parameter optimization for friction stir welding of aluminium 2014-t651 alloy using taguchi technique. Trans Nonferrous Met Soc China 18:548–554

    Article  Google Scholar 

  27. Li TS, Chen SH, Chen HL (2009) Thermal-flow techniques for sub-35 nm contact-hole fabrication using Taguchi method in electron-beam lithography. Microelectron Eng 86:2170–2175. https://doi.org/10.1016/j.mee.2009.03.049

    Article  Google Scholar 

  28. Östergen A (2013) Prediction of residual stresses in injection moulded parts, pp 1–66

    Google Scholar 

Download references

Acknowledgments

The authors are grateful for the help of the SKG staff during the materialization of this study.

We also would like to thank Dr. Ayadi Hajji for his help in proofreading, correcting, and improving the English of the manuscript.

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Correspondence to Fatma Hentati.

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Hentati, F., Hadriche, I., Masmoudi, N. et al. Optimization of the injection molding process for the PC/ABS parts by integrating Taguchi approach and CAE simulation. Int J Adv Manuf Technol 104, 4353–4363 (2019). https://doi.org/10.1007/s00170-019-04283-z

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