Skip to main content
SpringerLink
Log in

Process optimization and in-mold sensing enabled dimensional prediction for high precision injection molding

  • Original Paper
  • Published:
International Journal on Interactive Design and Manufacturing (IJIDeM) Aims and scope Submit manuscript

Abstract

Using different raw material in injection molding could happen in situations where the original material becomes unavailable, material cost rises, or in response to customer demands. However, applying different materials on the same mold often leads to excessive dimensional deviation, causing quality degradation. To reduce defect rate and circumvent high cost expenditure on new molds, this paper presents an experimental framework aiming to implement process optimization efficiently and attain a predictable level for the quality characteristics. The methodology starts from a Taguchi experimental design where process parameters including both controllable factors and uncontrollable factors were arranged into an orthogonal array. Driven by its efficiency, Taguchi method was able to produce optimal process parameter levels that significantly improved the process capability. Subsequently, data collected by an in-mold sensing system was analyzed to extract the contribution from in-mold process variables that are not externally accessible. In order to quantitatively rank the impacts from in-mold process variables, a multiple linear regression (MLR) were performed with top influential factors identified. The selected influential variables allowed for the quality characteristic to be predicted through a fuzzy logic based predictive model. In conclusion, the methodology presented in this paper has the potential of reducing or eliminating defect rate caused by material variation, and at the same time allows dimension prediction of injection molded parts with real time sensed in-mold conditions.

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

Similar content being viewed by others

References

  1. Kalpakjian, S., Schmid, S.: Manufacturing engineering and technology, 8th Edition, Pearson, (2020)

  2. Kazmer D. O.: Injection mold design engineering, 2nd Edition, Hanser Fachbuchverlag (2016)

  3. He, X., Wu, W.: A practical numerical approach to characterizing non-linear shrinkage and optimizing dimensional deviation of injection-molded small module plastic gears. Polymers 13(13), 2092 (2021)

    Article  Google Scholar 

  4. Song, Z., Liu, S., Wang, X., Hu, Z.: Optimization and prediction of volume shrinkage and warpage of injection-molded thin-walled parts based on neural network. Int. J. Adv. Manuf. Technol. 109(3–4), 755–769 (2020)

    Article  Google Scholar 

  5. Chang, R.-Y., Yang, W.-H.: Numerical simulation of mold filling in injection molding using a three-dimensional finite volume approach. Int. J. Numer. Meth. Fluids 37, 125–148 (2001)

    Article  MATH  Google Scholar 

  6. Hétu, J.-F., Gao, D.M., Garcia-Rejon, A., Salloum, G.: 3D finite element method for the simulation of the filling stage in injection molding. Polym. Eng. Sci. 38, 223–236 (1998)

    Article  Google Scholar 

  7. Ilinca, F., Hétu, J.-F.: Finite element solution of three-dimensional turbulent flows applied to mold-filling problems. Int. J. Numer. Meth. Fluids 34, 729–750 (2000)

    Article  MATH  Google Scholar 

  8. Mavridis, H., Hrymak, A.N., Vlachopoulos, J.: Finite element simulation of fountain flow in injection molding. Polym Eng Sci 26, 449–454 (1986)

    Article  Google Scholar 

  9. Zhou, J., Turng, L.-S.: Three-dimensional numerical simulation of injection mold filling with a finite-volume method and parallel computing. Adv. Polym. Technol. 25, 247–258 (2006)

    Article  Google Scholar 

  10. Johnston, S.P., Kazmer, D.O., Gao, R.X.: Online simulation-based process control for injection molding. Polym. Eng. Sci. 49(12), 2482–2491 (2009)

    Article  Google Scholar 

  11. Guo, G., Li, Y., Zhao, X., Rizvi, R.: Tensile and longitudinal shrinkage behaviors of polylactide/wood-fiber composites via direct injection molding. Polym. Compos. 41, 4663–4677 (2020)

    Article  Google Scholar 

  12. Mukras, S.M.S.: Experimental-based optimization of injection molding process parameters for short product cycle time. Adv. Polym. Technol. 2020, 1–15 (2020)

    Article  Google Scholar 

  13. Azdast, Taher; Hasanzadeh, Rezgar, Experimental assessment and optimization of shrinkage behavior of injection molded polycarbonate parts, Materials Research Express, 2019, Vol.6(11), p.115334

  14. Lin, C.-M., Chen, W.-C.: Optimization of injection-molding processing conditions for plastic double-convex Fresnel lens using grey-based Taguchi method. Microsyst. Technol. 26(8), 2575–2588 (2020)

    Article  Google Scholar 

  15. Usman, J.Q.M., Habib, T., Noor, S., Abas, M., Azim, S., Yaseen, Q.M., Pham, D.: Multi response optimization of injection moulding process parameters of polystyrene and polypropylene to minimize surface roughness and shrinkage’s using integrated approach of S/N ratio and composite desirability function. Cogent Eng 7(1), 1781424 (2020)

    Article  Google Scholar 

  16. Chen, J.C., Guo, G., Wang, W.N.: Artificial neural network based online defect detection system with in mold temperature and pressure sensors for high precision injection molding. Int. J. Adv. Manuf. Technol. 110, 2023–2033 (2020)

    Article  Google Scholar 

  17. Abdul, R., Guo, G., Chen, J.C., Yoo, J.J.-W.: Shrinkage prediction of injection molded high density polyethylene parts with taguchi/artificial neural network hybrid experimental design. Int. J. Interact. Design Manuf. (IJIDeM) 14(2), 345–357 (2020)

    Article  Google Scholar 

  18. Liao, S.J., Hsieh, W.H., Wang, J.T., Su, Y.C.: Shrinkage and warpage prediction of injection-molded thin-wall parts using artificial neural networks. Polym. Eng. Sci. 44(11), 2029–2040 (2004)

    Article  Google Scholar 

  19. Wang, R., Zeng, J., Feng, X., Xia, Y.: Evaluation of effect of plastic injection molding process parameters on shrinkage based on neural network simulation. J. Macromol. Sci. 52(1), 206–221 (2013)

    Article  Google Scholar 

  20. Lee, S.C., Youn, J.R.: Shrinkage analysis of molded parts using neural network. J. Reinf. Plastic. Compos. 18(2), 186–195 (1999)

    Article  MathSciNet  Google Scholar 

  21. Lotti, C., Ueki, M.M., Bretas, R.E.S.: Prediction of the shrinkage of injection molded iPP plaques using artificial neural networks. J. Inject. Mold. Technol. 6(3), 157–176 (2002)

    Google Scholar 

  22. Xu, G., Yang, Z.-T., Long, G.-D.: Multi-objective optimization of MIMO plastic injection molding process conditions based on particle swarm optimization. Int. J. Adv. Manuf. Technol. 58(5–8), 521–531 (2012)

    Article  Google Scholar 

  23. Cao, Y., Fan, X., Guo, Y., Li, S., Huang, H.: Multi-objective optimization of injection-molded plastic parts using entropy weight, random forest, and genetic algorithm methods. J. Polym. Eng. 40(4), 360–371 (2020)

    Article  Google Scholar 

  24. Farahani, S., Brown, N., Loftis, J., Krick, C., Pichl, F., Vaculik, R., Pilla, S.: Evaluation of in-mold sensors and machine data towards enhancing product quality and process monitoring via Industry 4. 0. Int. J. Adv. Manuf. Technol. 105(1–4), 1371–1389 (2019)

    Article  Google Scholar 

  25. Ageyeva, T., Horváth, S., Kovács, J.G.: In-mold sensors for injection molding: on the way to industry 4. 0. Sensors 9(16), 3551 (2019)

    Article  Google Scholar 

  26. Chen, J.-Y., Yang, K.-J., Huang, M.-S.: Online quality monitoring of molten resin in injection molding. Int. J. Heat Mass Transf. 122, 681–693 (2018)

    Article  Google Scholar 

  27. Hopmann, C., Heinisch, J.: Process control strategies for injection molding processes with changing raw material viscosity. J. Polym. Eng. 38(5), 483–492 (2018)

    Article  Google Scholar 

  28. Nam, J.S., Na, C.R., Jo, H.H., Song, J.Y., Ha, T.H., Lee, S.W.: Injection-moulded lens form error prediction using cavity pressure and temperature signals based on k-fold cross validation. Proceed. Inst. Mech. Eng., Part B: J. Eng. Manuf. 232(5), 928–934 (2018)

    Article  Google Scholar 

  29. Zhang, J.Z., Chen, J.C., Kirby, D.E.: Surface roughness optimization in an end-milling operation using the Taguchi design method. J. Mater. Process. Technol. 184(1–3), 233–239 (2007)

    Article  Google Scholar 

  30. Kuram, E., Ozcelik, B.: Optimization of machining parameters during micro-milling of Ti6Al4V titanium alloy and Inconel 718 materials using Taguchi method. Proceed. Inst. Mech. Eng., Part B: J. Eng. Manuf. 231(2), 228–242 (2015)

    Article  Google Scholar 

  31. Kim, N.P., Cho, D., Zielewski, M.: Optimization of 3D printing parameters of Screw Type Extrusion (STE) for ceramics using the Taguchi method. Ceramics Int 45, 2351–2360 (2019)

    Article  Google Scholar 

  32. Dong, G., Wijaya, G., Tang, Y., Zhao, Y.F.: Optimizing process parameters of fused deposition modeling by Taguchi method for the fabrication of lattice structures. Addit. Manuf. 19, 62–72 (2018)

    Google Scholar 

  33. Jiang, H.-Z., et al.: Factor analysis of selective laser melting process parameters with normalised quantities and Taguchi method. Opt. Laser Technol. 119, 105592 (2019)

    Article  Google Scholar 

  34. Fotovvati, B., Balasubramanian, M., Asadi, E.: Modeling and Optimization approaches of laser-based powder-bed fusion process for Ti-6Al-4V alloy. Coatings 10(11), 1104–1129 (2020)

    Article  Google Scholar 

  35. Aravind, S.S., Razmi, J., Mian, M.J., Ladani, L.: Mechanical anisotropy and surface roughness in additively manufactured parts fabricated by stereolithography (SLA) using statistical analysis. Materials 13(11), 2496 (2020)

    Article  Google Scholar 

  36. Guerra, A.J., et al.: Optimization of photocrosslinkable resin components and 3D printing process parameters. Acta Biomater. 97, 154–161 (2019)

    Article  Google Scholar 

  37. Jelokhani, N.R., Fazli, A., Soltanpour, M.: Electromagnetically activated high-speed hydroforming process: a novel process to overcome the limitations of the electromagnetic forming process. CIRP J. Manuf. Sci. Technol. 27, 21–30 (2019)

    Article  Google Scholar 

  38. Ye, Li., Travis, R.: Capability study of 2D Heat-assisted Mill-bend process. Int. J. Interact. Design Manuf. 14, 759–772 (2020)

    Article  Google Scholar 

  39. Chen, J.C., Li, Ye., Cox, R.A.: Taguchi-based six sigma approach to optimize plasma cutting process: an industrial case study. Int. J. Adv. Manuf. Technol. 41, 760–769 (2009)

    Article  Google Scholar 

  40. Gani, A., Ion, W., Yang, E.: Experimental investigation of plasma cutting two separate thin steel sheets simultaneously and parameters optimisation using taguchi approach. J. Manuf. Process. 64, 1013–1023 (2021)

    Article  Google Scholar 

  41. Fischer, J. M., Handbook of Molded Part Shrinkage and Warpage, William Andrew Publishing, (2013)

  42. Kirby, E.D., Chen, J.C.: Development of a fuzzy-nets-based surface roughness prediction system in turning operations. Comput Ind Eng 53, 30–42 (2007)

    Article  Google Scholar 

  43. Chen, J.C., Black, J.T.: A fuzzy-nets in-process (FNIP) system for tool-breakage monitoring in end-milling operations. Int J Mach Tools Manuf 37(6), 783–880 (1997)

    Article  Google Scholar 

  44. Yang, L.D., Chen, J., Chow, H.M.: Fuzzy-nets-based in-process surface roughness adaptive control system in end-milling operations. Int J Adv Manuf Technol 28, 236–248 (2006)

    Article  Google Scholar 

  45. Hua, Y., Choi, J.: Adaptive direct metal/material deposition process using a fuzzy logic-based controller. J. Laser Appl. 17, 200 (2005)

    Article  Google Scholar 

  46. Yongzhe, Li., Xinlei, Li., Guangjun, Z., Imre, H., Qinglin, H.: Interlayer closed-loop control of forming geometries for wire and arc additive manufacturing based on fuzzy-logic inference. J. Manuf. Process. 63, 35–47 (2021)

    Article  Google Scholar 

  47. Hoi-Pang, T., Furong, G.: Control of injection velocity using a fuzzy logic rule-based controller for thermoplastics injection molding. Polym. Eng. Sci. 39(1), 3–17 (1999)

    Article  Google Scholar 

  48. Huang, S.-J., Lee, T.-H.: Fuzzy logic controller for a retrofitted closed-loop injection moulding machine. Proceed. Inst. Mech. Eng. Part I J. Syst. Control Eng. 214(1), 9–22 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial & Manufacturing Engineering & Technology, Bradley University, 1501 West Bradley Avenue, Peoria, IL, USA

    Ye Li, Joseph C. Chen & Wamda Mohamed Ali

Authors
  1. Ye Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joseph C. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wamda Mohamed Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ye Li.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Chen, J.C. & Ali, W.M. Process optimization and in-mold sensing enabled dimensional prediction for high precision injection molding. Int J Interact Des Manuf 16, 997–1013 (2022). https://doi.org/10.1007/s12008-021-00800-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12008-021-00800-1

Keywords

Search

Navigation