Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Interactive optimization of the resin transfer molding using a general-purpose tool: a case study

  • 33 Accesses

Abstract

Simulation tools for liquid composite molding processes are a key to predict and solve manufacturing issues of composite materials. Numerical processes are commonly used to analyse and predict mould filling, considering also resin cure and exothermic reactions. These evaluations are usually performed through dedicated software tools that require highly specialized operators and purchasing costs. The present study relates to a multi-objective optimization approach for evaluating the effect of different process parameters of the resin transfer molding (RTM) process using a multi-purpose tool. Starting from a simple case, useful for analysing the effect of mesh type and size on the simulations, and then increasing the complexity of the models, virtual simulations have been validated through real tests. Afterward, this approach has been used for the optimization of the RTM process for the manufacturing of an automotive component. Gate positions, injection pressure and resin temperature have been optimized using finite-volume analysis with a multi-objective genetic algorithm. Finally, the parameters have been used in real experiments in order to validate the efficiency and the reliability of multi-purpose tool in simulating RTM processes.

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

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
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

References

  1. 1.

    Marieta, C., Schulz, E., Mondragon, I.: Characterization of interfacial behaviour in carbon-fibre/cyanate composites. Compos. Sci. Technol. 62(2), 299–309 (2002)

  2. 2.

    Dai, Z., Shi, F., Zhang, B., Li, M., Zhang, Z.: Effect of sizing on carbon fiber surface properties and fibers/epoxy interfacial adhesion. Appl. Surf. Sci. 257(15), 6980–6985 (2011)

  3. 3.

    Gutowski, T.G.P.: Advanced Composites Manufacturing. Wiley, Hoboken (1997)

  4. 4.

    Campbell, F.C., Flake, C.: Structural Composite Materials. ASM International, Materials Park (2010)

  5. 5.

    Mallick, P.K.: Fiber-Reinforced Composites: Materials, Manufacturing, and Design. CRC Press, Boca Raton (2008)

  6. 6.

    Fontecha Dulcey, G., Fischer, X., Joyot, P., Fadel, G.: Support for decision making in design of composite laminated structures. Part 2: reduced parametric model-based optimization. Appl. Compos. Mater. 26(2), 663–681 (2019). https://doi.org/10.1007/s10443-018-9742-9

  7. 7.

    Fontecha Dulcey, G., Fischer, X., Joyot, P., Fadel, G.: Support for decision making in design of composite laminated structures. Part 1: parametric knowledge model. Appl. Compos. Mater. 26(2), 643–662 (2019). https://doi.org/10.1007/s10443-018-9741-x

  8. 8.

    Advani, S.G., Hsiao, K.-T.: Manufacturing Techniques for Polymer Matrix Composites (PMCs). Woodhead Publishing, Cambridge (2012)

  9. 9.

    Palardy, G., Hubert, P., Ruiz, E., Haider, M., Lessard, L.: Numerical simulations for class A surface finish in resin transfer moulding process. Compos. Part B Eng. 43(2), 819–824 (2012)

  10. 10.

    Vita, A., Castorani, V., Germani, M., Marconi, M.: Comparative life cycle assessment of low-pressure RTM, compression RTM and high-pressure RTM manufacturing processes to produce CFRP car hoods. Procedia CIRP 80, 352–357 (2019)

  11. 11.

    Okabe, T., Oya, Y., Yamamoto, G., Sato, J., Matsumiya, T., Matsuzaki, R., et al.: Multi-objective optimization for resin transfer molding process. Compos. Part A Appl. Sci. Manuf. 92, 1–9 (2017). https://doi.org/10.1016/j.compositesa.2016.09.023

  12. 12.

    Swan, S., Yuksel, T., Kim, D., Gurocak, H.: Automation of the vacuum assisted resin transfer molding process for recreational composite yachts. Polym. Compos. 38(11), 2411–2424 (2017). https://doi.org/10.1002/pc.23826

  13. 13.

    Matsuzaki, R., Shiota, M.: Data assimilation through integration of stochastic resin flow simulation with visual observation during vacuum-assisted resin transfer molding: a numerical study. Compos. Part A Appl. Sci. Manuf. 84, 43–52 (2016)

  14. 14.

    Pearce, N., Guild, F., Summerscales, J.: A study of the effects of convergent flow fronts on the properties of fibre reinforced composites produced by RTM. Compos. Part A Appl. Sci. Manuf. 29(1–2), 141–152 (1998)

  15. 15.

    Leclerc, J.S., Ruiz, E.: Porosity reduction using optimized flow velocity in resin transfer molding. Compos. Part A Appl. Sci. Manuf. 39(12), 1859–1868 (2008)

  16. 16.

    Restrepo, O., Hsiao, K.-T., Rodriguez, A., Minaie, B.: Development of adaptive injection flow rate and pressure control algorithms for resin transfer molding. Compos. Part A Appl. Sci. Manuf. 38(6), 1547–1568 (2007)

  17. 17.

    Lee, D.H., Lee, W.I., Kang, M.K.: Analysis and minimization of void formation during resin transfer molding process. Compos. Sci. Technol. 66(16), 3281–3289 (2006)

  18. 18.

    Matsuzaki, R., Kobayashi, S., Todoroki, A., Mizutani, Y.: Flow control by progressive forecasting using numerical simulation during vacuum-assisted resin transfer molding. Compos. Part A Appl. Sci. Manuf. 45, 79–87 (2013)

  19. 19.

    Kedari, V.R., Farah, B.I., Hsiao, K.-T.: Effects of vacuum pressure, inlet pressure, and mold temperature on the void content, volume fraction of polyester/e-glass fiber composites manufactured with VARTM process. J. Compos. Mater. 45(26), 2727–2742 (2011). https://doi.org/10.1177/0021998311415442

  20. 20.

    Nalla, A.R., Fuqua, M., Glancey, J., Lelievre, B.: A multi-segment injection line and real-time adaptive, model-based controller for vacuum assisted resin transfer molding. Compos. Part A Appl. Sci. Manuf. 38(3), 1058–1069 (2007)

  21. 21.

    Byoung, Y., Kim, G.I., Joon, N., Lee, J.W.: Optimization of filling process in RTM using a genetic algorithm and experimental design method. Polym. Compos. 23(1), 72–86 (2002)

  22. 22.

    García-Hernández, C., Gella-Marín, R., Huertas-Talón, J.L., Berges-Muro, L.: Algorithm for measuring gears implemented with general-purpose spreadsheet software. Measurement 85, 1–12 (2016)

  23. 23.

    Castorani, V., Vita, A., Mandolini, M., Germani, M.: A CAD-based method for multi-objectives optimization of mechanical products. In: Proceedings of CAD’16, pp. 168–172 (2016)

  24. 24.

    Pellissetti, M.F., Schuëller, G.I.: On general purpose software in structural reliability—an overview. Struct. Saf. 28(1–2), 3–16 (2006)

  25. 25.

    Fortmann-Roe, S.: Insight Maker: a general-purpose tool for web-based modeling & simulation. Simul. Model. Pract. Theory 47, 28–45 (2014)

  26. 26.

    Poodts, E., Minak, G., Mazzocchetti, L., Giorgini, L.: Fabrication, process simulation and testing of a thick CFRP component using the RTM process. Compos. Part B Eng. 56, 673–680 (2014). https://doi.org/10.1016/j.compositesb.2013.08.088

  27. 27.

    Lanzotti, A., Carbone, F., Grazioso, S., Renno, F., Staiano, M.: A new interactive design approach for concept selection based on expert opinion. Int. J. Interact. Des. Manuf. 12(4), 1189–1199 (2018). https://doi.org/10.1007/s12008-018-0482-8

  28. 28.

    Hoes, K., Dinescu, D., Sol, H., Vanheule, M., Parnas, R.S., Luo, Y., et al.: New set-up for measurement of permeability properties of fibrous reinforcements for RTM. Compos. Part A Appl. Sci. Manuf. 33(7), 959–969 (2002)

  29. 29.

    Arbter, R., Beraud, J.M., Binetruy, C., Bizet, L., Bréard, J., Comas-Cardona, S., et al.: Experimental determination of the permeability of textiles: a benchmark exercise. Compos. Part A Appl. Sci. Manuf. 42(9), 1157–1168 (2011)

  30. 30.

    American Society of Mechanical Engineers, Materials Division (Publication) MD, vol. 19. In: Winter Annual Meeting of the American Society of Mechanical Engineers; Dallas, TX, USA; 25 November 1990–30 November 1990, pp. 73–90 (1990)

  31. 31.

    Samir, J., Echaabi, J., Hattabi, M.: Numerical algorithm and adaptive meshing for simulation the effect of variation thickness in resin transfer molding process. Compos. Part B Eng. 42(5), 1015–1028 (2011)

  32. 32.

    Habashi, W.G., Dompierre, J., Bourgault, Y., Ait-Ali-Yahia, D., Fortin, M., Vallet, M.-G.: Anisotropic mesh adaptation: towards user-independent, mesh-independent and solver-independent CFD. Part I: general principles. Int. J. Numer. Methods Fluids 32(6), 725–744 (2000)

  33. 33.

    ANSYS FLUENT 12.0 User’s Guide. Cited 2018 Dec 17. http://www.afs.enea.it/project/neptunius/docs/fluent/html/ug/main_pre.htm

  34. 34.

    Antonucci, V., Esposito, M., Ricciardi, M.R., Raffone, M., Zarrelli, M., Giordano, M.: Permeability characterization of stitched carbon fiber preforms by fiber optic sensors. Express Polym. Lett. 5(12), 1075–1084 (2011)

  35. 35.

    Box, G.E.P., Hunter, J.S., Hunter, W.G.: Statistics for Experimenters: Design, Innovation, and Discovery. Wiley-Interscience, Hoboken (2005)

  36. 36.

    Taguchi, G., Yokoyama, Y.: Taguchi Methods: Design of Experiments. ASI Press, Dearborn (1993)

  37. 37.

    Taguchi, G., Elsayed, E.A., Hsiang, T.C.: Quality Engineering in Production Systems. McGraw-Hill, New York (1988)

Download references

Author information

Correspondence to Daniele Landi.

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

Verify currency and authenticity via CrossMark

Cite this article

Landi, D., Vita, A. & Germani, M. Interactive optimization of the resin transfer molding using a general-purpose tool: a case study. Int J Interact Des Manuf 14, 295–308 (2020). https://doi.org/10.1007/s12008-019-00631-1

Download citation

Keywords

  • RTM
  • Process simulation
  • Virtual prototyping
  • Multi-objective optimization