Skip to main content
SpringerLink
Log in

Strategies for adjusting process parameters in CAE simulation to meet real injection molding condition of screw positions and cavity pressure curves

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

Abstract

Numerical simulations of polymer melt flow behavior in mold cavities help optimize process parameters. However, mathematical models, processing conditions, material property settings, and machine aging can cause simulations to differ from experimental results. The accuracy of simulations can indicate injection mold quality, which is used to determine the optimal process parameters. However, the optimal process parameters for simulations are very different from the real situation and cannot be directly applied in practice. Therefore, the simulation setup in the manufacturing process requires additional molding trials, resulting in time and cost consumption. This study used high-precision injection molding machines, material property settings, mold dimensions, and mold temperatures to assess the difference in pressure curves between simulations and real molding. This study also developed a method for adjusting the injection molding process parameters of a simulation to decrease the difference in pressure and screw position curves between the simulation and real molding, thus quantitatively improving the quality prediction capacity of simulations. The results contribute to the research on directly applying the injection molding process parameters of a simulation to real molding to achieve smart manufacturing.

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data availability

The authors confirm that the data supporting the findings of this study are available within the article.

References

  1. Fassett K (2009) Scientific molding, in-cavity sensors, and data management. RJG Inc

  2. Chen JY, Yang KJ, Huang MS (2018) Online quality monitoring of molten resin in injection molding. Int J Heat Mass Transf 122:681–693

    Article  Google Scholar 

  3. Wang J, Mao Q (2013) A novel process control methodology based on the PVT behavior of polymer for injection molding. Adv Polym Technol 32(S1):474–485

    Article  Google Scholar 

  4. Zhang N, Gilchrist MD (2012) Characterization of thermos-rheological behavior of polymer melts during the micro injection molding process. Polym Test 31:748–758

    Article  Google Scholar 

  5. ENGEL (2019) Self-adjusting assistance systems for machines and robots. ENGEL Tech Rep

  6. Kurt M, Saban KO, 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 30:3217–3224

    Article  Google Scholar 

  7. Wang J, Xie P, Ding Y, Yang W (2009) On-line testing equipment of P-V-T properties of polymers based on an injection molding machine. Polym Test 28(3):228–234

    Article  Google Scholar 

  8. Lin CC, Wang WT, Kuo CC, Wuet CL (2014) Experimental and theoretical study of melt viscosity in injection process. Int J Mech Mechatronics Eng 8(7):687–691

    Google Scholar 

  9. Gornik C (2008) Viscosity measuring methods for feedstocks directly on injection molding machines. Mater Sci Forum 591–593:174–178

    Article  Google Scholar 

  10. Chen JY, Tseng CC (2019) Huang MS (2019) Quality indexes design for online monitoring polymer injection molding. Adv Polym Technol 419:1–20

    Google Scholar 

  11. Zhou X, Zhang Y, Mao T, Zhou H (2017) Monitoring and dynamic control of quality stability for injection molding process. J Mater Process Technol 249:385–366

    Article  Google Scholar 

  12. Aho J, Syrjälä S (2011) Shear viscosity measurements of polymer melts using injection molding machine with adjustable slit die. Polym Test 30:595–601

    Article  Google Scholar 

  13. Huang MS, Nian SC, Chen JY, Lin CY (2018) Influence of clamping force on tie-bar elongation, mold separation, and part dimensions in injection molding. Precis Eng 51:647–658

    Article  Google Scholar 

  14. Huang MS, Lin CY (2017) A novel clamping force searching method based on sensing tie-bar elongation for injection molding. Int J Heat Mass Transf 109:223–1230

    Article  Google Scholar 

  15. Chen JY, Yang KJ, Huang MS (2020) Optimization of clamping force for low-viscosity polymer injection molding. Polym Test 90:106700

    Article  Google Scholar 

  16. Zhang JF, Zhao P, Zhao Y, Xia N, Fu JZ (2019) On-line measurement of cavity pressure during injection molding via ultrasonic investigation of tie bar. Sens Actuator A-Phys 285:118–126

    Article  Google Scholar 

  17. Zhao P, Zhou H, He Y, Cai K, Fu J (2014) A nondestructive online method for monitoring the injection molding process by collecting and analyzing machine running data. Int J Adv Manuf Technol 72:765–777

    Article  Google Scholar 

  18. Kazmer DO, Velusamy S, Westerdale S, Johnston S, Gao RX (2010) A comparison of seven filling to packing switchover methods for injection molding. Polym Eng Sci 50:2031–2043

    Article  Google Scholar 

  19. Kazmer D, Barkan P (1997) Multi-cavity pressure control in the filling and packing stages of the injection molding process. Polym Eng Sci 37:1865–1879

    Article  Google Scholar 

  20. Gim J, Rhee B (2021) Novel analysis methodology of cavity pressure profiles in injection-molding processes using interpretation of machine learning model. Polymers 13:3297

    Article  Google Scholar 

  21. Beaumont J (2012) Brand-new test method relates material, mold & machine. Plastics Technol

  22. Hopmann Ch, Zhuang J (2017) Process control strategies for injection molding processes with changing raw material viscosity. J Polym Eng 38(5):483–492

    Article  Google Scholar 

  23. Kulkarni S (2015) Scientific molding the six step study. https://www.fimmtech.com

  24. Karbasi H (2006) Smart mold: real time in cavity data acquisition. First annual technical showcase & third annual workshop, Citeseer, Canada

  25. Nian SC, Fang YC, Huang MS (2019) In-mold and machine sensing and feature extraction for optimized IC-tray manufacturing. Polymers 11(8):1348–1366

    Article  Google Scholar 

  26. Chang YH, Wei TH, Chen SC, Lou YF (2020) The investigation on PVT control method establishment for scientific injection molding parameter setting and its quality control. Polym Eng Sci 60:2895–2907

    Article  Google Scholar 

  27. Guerrier P, Tosello G, Hattel JH (2017) Flow visualization and simulation of the filling process during injection molding. CIRP J Manuf Sci Technol 16:220–222

    Article  Google Scholar 

  28. Regi F, Guerrier P, Zhang Y, Tosello G (2020) Experimental characterization and simulation of thermoplastic polymer flow hesitation in thin-wall injection molding using direct in-mold visualization technique. Micromachines 11(4):428–440

    Article  Google Scholar 

  29. Huang CT, Hsu YH, Chen BS (2018) Investigation on the internal mechanism of the deviation between numerical simulation and experiments in injection molding product development. Polym Test 75:327–336

    Article  Google Scholar 

  30. Huang CT, Xu RT, Chen PH, Jong WR, Chen SC (2020) Investigation on the machine calibration effect on the optimization through design of experiments (DOE) in injection molding parts. Polym Test 90:106703

    Article  Google Scholar 

  31. Huang MS, Liu CY, Ke KC (2021) Calibration of cavity pressure simulation using autoencoder and multilayer perceptron neural networks. Polym Eng Sci 61:2511–2521

    Article  Google Scholar 

  32. Chen JY, Hung PH, Huang MS (2021) Determination of process parameters based on cavity pressure characteristics to enhance quality uniformity in injection molding. Int J Heat Mass Transf 180:121788

    Article  Google Scholar 

  33. Coretech System Co Ltd (2019) Computer-implemented simulation method for injection-molding process. US Patent 16:587858

Download references

Author information

Authors and Affiliations

  1. Department of Mechatronics Engineering, Yanchao Dist, National Kaohsiung University of Science and Technology, 1 University Road, Kaohsiung City 824, Taiwan

    Bo-Wei Wang & Ming-Shyan Huang

  2. Department of Power Mechanical Engineering, National Taitung College, No. 911 Zhengqi North Road, Taitung City 950, Taiwan

    Shih-Chih Nian

Authors
  1. Bo-Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shih-Chih Nian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ming-Shyan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.-C. Nian and M.-S. Huang were responsible for deriving conceptualization and methodology. B.-W. Wang was responsible for simulation and experiment. B.-W. Wang and M.-S. Huang were involved in the discussion and significantly contributed to making the final draft of the article. All the authors read and approved the final manuscript.

Corresponding author

Correspondence to Ming-Shyan Huang.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, BW., Nian, SC. & Huang, MS. Strategies for adjusting process parameters in CAE simulation to meet real injection molding condition of screw positions and cavity pressure curves. Int J Adv Manuf Technol 122, 1339–1351 (2022). https://doi.org/10.1007/s00170-022-09992-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-09992-6

Keywords

Search

Navigation