Skip to main content
Log in

Product quality prognosis in plastic injection moulding

  • Quality Assurance
  • Published:
Production Engineering Aims and scope Submit manuscript

Abstract

Plastic injection moulding is a typical complex manufacturing process. Its product quality is difficult to assure because of the nonuniform material shrinkage. This research introduces a prognostic concept for predicting the product quality (four edge shrinkages). The prognostic model is developed by path analysis through LISREL which can define the model’s reliability when only few sensory data are retrieved from the smoothly running system. The prognostic model is validated by 25 test runs. In each run, each of the nine manufacturing conditions is randomly set at the extreme limit, i.e., either the lower limit or the upper limit. By comparing the actual edge shrinkages defined by the finite element software Moldflow with the results predicted by the prognostic model, this research concludes the prognostic model can successfully predict the quality of products and prevent the production of defective products.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Dong M, He D (2007) Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis. Eur J Oper Res 178:858–878

    Article  MATH  Google Scholar 

  2. Chinnam RB, Baruah P (2008) Autonomous diagnostics and prognostics in machining processes through competitive learning-driven HMM-based clustering. Int J Prod Res 47:6739–6758

    Article  Google Scholar 

  3. Roemer MJ, Kacprzynski GJ, Schoeller MH (2001) Improved diagnostic and prognostic assessments using health management information fusion. In: AUTOTESTCON proceedings, 2001. IEEE systems readiness technology conference. pp 365–377

  4. Engel SJ, Gilmartin BJ, Bongort K, Hess A (2000) Prognostics, the real issues involved with predicting life remaining. In: Aerospace conference proceedings, 2000 IEEE. vol. 456, pp 457–469

  5. Hardman W, Hess A, Blunt D (2001) A USN development strategy and demonstration results for propulsion and mechanical systems diagnostics, prognostics and health management. In: Aerospace conference, 2001, IEEE proceedings, vol. 3056, pp 3059–3068

  6. Dong M, He D, Banerjee P, Keller J (2006) Equipment health diagnosis and prognosis using hidden semi-Markov models. Int J Adv Manuf Technol 30:738–749

    Article  Google Scholar 

  7. Winkler H, Heins M, Nyhuis P (2007) A controlling system based on cause–effect relationships for the ramp-up of production systems. Prod Eng 1:103–111

    Article  Google Scholar 

  8. Liao SJ, Chang DY, Chen HJ, Tsou LS, Ho JR, Yau HT, Hsieh WH, Wang JT, Su YC (2004) Optimal process conditions of shrinkage and warpage of thin-wall parts. Polym Eng Sci 44:917–928

    Article  Google Scholar 

  9. 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 

  10. Mok SL, Kwong CK (2002) Application of artificial neural network and fuzzy logic in a case-based system for initial process parameter setting of injection molding. J Intell Manuf 13:165–176

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Liao SJ, Hsieh WH, Wang JT, Su YC (2004) Shrinkage and warpage prediction of injection-molded thin-wall parts using artificial neural networks. Polym Eng Sci 44:2029–2040

    Article  Google Scholar 

  13. He W, Zhang YF, Lee KS, Fuh JYH, Nee AYC (1998) Automated process parameter resetting for injection moulding: a fuzzy-neuro approach. J Intell Manuf 9:17–27

    Article  Google Scholar 

  14. 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 

  15. 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 

  16. 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 

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

    Article  Google Scholar 

  18. Chen W-L, Huang C-Y, Hung C-W (2010) Optimization of plastic injection molding process by dual response surface method with non-linear programming. Engineering computations 27: to appear

  19. Chen ZB, Turng LS (2005) A review of current developments in process and quality control for injection molding. Adv Polym Technol 24:165–182

    Article  Google Scholar 

  20. Heim HP (2002) The statistical regression calculation in plastics processing—Process analysis, optimization and monitoring. Macromol Mater Eng 287:773–783

    Article  Google Scholar 

  21. Autodesk (2009) Autodesk moldflow insight. http://usa.autodesk.com/adsk/servlet/index?siteID=123112&id=13195432. Accessed 15 July 2009

  22. Walsh SF (1993) Shrinkage and warpage prediction for injection-molded components. J Reinf Plast Compos 12:769–777

    Article  Google Scholar 

  23. MoldFlow (2006) MoldFlow plastic insight release 6.0

  24. Jeoreskog KG, Seorbom D (1996) LISREL 8: User’s reference guide scientific software international, Chicago, IL

  25. Raykov T, Marcoulides GA (2006) A first course in structural equation modeling. Lawrence Erlbaum Associates, Mahwah

    Google Scholar 

  26. Browne MW, Cudeck R (1993) Alternative ways of assessing model fit. In: Bollen KA, Long JS (eds) Testing structural equation models. SAGE Publications, Newbury Park, CA, pp 445–455

  27. Byrne BM (2001) Structural equation modeling with AMOS: basic concepts, applications, and programming. Lawrence Erlbaum Associates, Mahwah

    Google Scholar 

  28. Gatignon H (2003) Statistical analysis of management data. Kluwer Academic Publishers, Boston

    Google Scholar 

  29. Hu L, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling 6:1–55

    Article  Google Scholar 

  30. Carvounis CP (2000) Handbook of biostatistics: a review and text. Parthenon Pub. Group, New York

  31. Ertunc HM, Loparo KA (2001) A decision fusion algorithm for tool wear condition monitoring in drilling. Int J Mach Tools Manuf 41:1347–1362

    Article  Google Scholar 

  32. Baruah P, Chinnam RB (2005) HMMs for diagnostics and prognostics in machining processes. Int J Prod Res 43:1275–1293

    Article  MATH  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Moldflow Corporation in Taiwan for the permission to use the plastic part been analyzed in this research. Special thanks to the financial support from the research projects 96-2221-E-029-024 and 98-2221-E-029-019, National Science Council of Taiwan. The authors are grateful to the expert anonymous reviewers whose comments and suggestions considerably improved this article.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chin-Yin Huang.

Appendices

Appendix 1

See Table 10.

Table 10 Thirty data sets for developing the prognostic model

Appendix 2

See Table 11.

Table 11 Inputs of Manufacturing conditions for robustness test (each row represents a case)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Huang, CY., Chen, WL., Cheng, CM. et al. Product quality prognosis in plastic injection moulding. Prod. Eng. Res. Devel. 5, 59–71 (2011). https://doi.org/10.1007/s11740-010-0269-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11740-010-0269-7

Keywords

Navigation