Polímeros: Ciência e Tecnologia
https://revistapolimeros.org.br/article/doi/10.1590/0104-1428.20230099
Polímeros: Ciência e Tecnologia
Original Article

A methodology for determination the inlet velocity in injection molding simulations

Diego Alves de Miranda; Willian Kévin Rauber; Miguel Vaz Jr.; Paulo Sergio Berving Zdanski

Downloads: 0
Views: 124

Abstract

The inlet velocity of thermoplastic in injection molds plays a crucial role in obtaining high-quality polymer parts and the final performance of the product. It is known that the way the polymer is injected into the mold can directly affect important properties, such as the distribution of internal stresses, the cooling rate and the formation of surface defects. However, there are injection molding machines that only control injection pressure and dosage, making it difficult to obtain the gate inlet velocity into the mold cavity. Besides, some molds have many injection channels as well as complex inlet geometries, which make a challenging task to identify the inlet velocity. This study presents numerical and experimental approaches on how to determine the entry velocity in thermoplastic injection molds. The main results showed that these methods are highly efficient and contribute to identifying the gate inlet velocity with good accuracy.

 

 

Keywords

inlet velocity, injection molds, numerical/experimental methodologies

References

1 Krantz, J., Nieduzak, Z., Kazmer, E., Licata, J., Ferki, O., Gao, P., Sobkowicz, M. J., & Masato, D. (2023). Investigation of pressure-controlled injection molding on the mechanical properties and embodied energy of recycled high-density polyethylene. Sustainable Materials and Technologies, 36, e00651. http://dx.doi.org/10.1016/j.susmat.2023.e00651.

2 Tsou, H.-H., Huang, C.-C., Zhao, T.-W., & Wang, Z.-H. (2022). Design and validation of sensor installation for online injection molding sidewall deformation monitoring. Measurement, 205, 112200. http://dx.doi.org/10.1016/j.measurement.2022.112200.

3 Bianchi, M. F., Gameros, A. A., Axinte, D. A., Lowth, S., Cendrowicz, A. M., & Welch, S. T. (2021). Regional temperature control in ceramic injection moulding: an approach based on cooling rate optimization. Journal of Manufacturing Processes, 68, 1767-1783. http://dx.doi.org/10.1016/j.jmapro.2021.06.069.

4 Fu, J., Zhang, X., Quan, L., & Ma, Y. (2022). Concurrent structural topology and injection gate location optimization for injection molding multi-material parts. Advances in Engineering Software, 165, 103088. http://dx.doi.org/10.1016/j.advengsoft.2022.103088.

5 He, H., Xing, Y., Wang, R., Lu, Y., Zhang, L., & Li, F. (2023). Optimization design of cooling system for injection molding mold of non-pneumatic tire. Thermal Science and Engineering Progress, 42, 101866. http://dx.doi.org/10.1016/j.tsep.2023.101866.

6 Marques, S., Souza, A. F., Miranda, J., & Yadroitsau, I. (2015). Design of conformal cooling for plastic injection moulding by heat transfer simulation. Polímeros: Ciência e Tecnologia, 25(6), 564-574. http://dx.doi.org/10.1590/0104-1428.2047.

7 Baum, M., Jasser, F., Stricker, M., Anders, D., & Lake, S. (2022). Numerical simulation of the mold filling process and its experimental validation. International Journal of Advanced Manufacturing Technology, 120(5), 3065-3076. http://dx.doi.org/10.1007/s00170-022-08888-9.

8 Chai, B. X., Eisenbart, B., Nikzad, M., Fox, B., Blythe, A., Blanchard, P., & Dahl, J. (2021). Simulation-based optimisation for injection configuration design of liquid composite moulding processes: a review. Composites. Part A, Applied Science and Manufacturing, 149, 106540. http://dx.doi.org/10.1016/j.compositesa.2021.106540.

9 Zhang, Y., Liu, F., Huang, Z., Xie, X., Shan, B., & Zhou, H. (2015). Dispersed phase deformation modeling of immiscible polymer blends in injection molding. Advances in Polymer Technology, 34(4), 21515. http://dx.doi.org/10.1002/adv.21515.

10 Morak, M., Tscharnuter, D., Lucyshyn, T., Hahn, W., Göttlinger, M., Kummer, M., Steinberger, R., & Gross, T. (2018). Optimization of fiber prediction model coefficients in injection molding simulation based on micro computed tomography. Polymer Engineering and Science, 52(2), 152-160. http://dx.doi.org/10.1002/pen.25013.

11 Onken, J., Verwaayen, S., & Hopmann, C. (2020). Evaluation of healing models to predict the weld line strength of the amorphous thermoplastic polystyrene by injection molding simulation. Polymer Engineering and Science, 61(3), 754-766. http://dx.doi.org/10.1002/pen.25614.

12 Gruber, P. A., & Miranda, D. A. (2020). Heat transfer simulation for decision making in plastic injection mold design. Polímeros: Ciência e Tecnologia, 30(1), e2020005. http://dx.doi.org/10.1590/0104-1428.08319.

13 Cao, W., Shen, Y., Wang, P., Yang, H., Zhao, S., & Shen, C. (2019). Viscoelastic modeling and simulation for polymer melt flow in injection/compression molding. Journal of Non-Newtonian Fluid Mechanics, 274, 104186. http://dx.doi.org/10.1016/j.jnnfm.2019.104186.

14 Xu, X., & Yu, P. (2017). Modeling and simulation of injection molding process of polymer melt by a robust SPH method. Applied Mathematical Modelling, 48, 384-409. http://dx.doi.org/10.1016/j.apm.2017.04.007.

15 Marion, S., Sardo, L., Joffre, T., & Pigeonneau, F. (2023). First steps of the melting of an amorphous polymer through a hot-end of a material extrusion additive manufacturing. Additive Manufacturing, 65, 103435. http://dx.doi.org/10.1016/j.addma.2023.103435.

16 Young, W.-B. (2017). Lattice Boltzmann simulation of polymer melt flow with a low Reynolds number. International Journal of Heat and Mass Transfer, 115, 784-792. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2017.08.080.

17 Zdanski, P. S. B., & Vaz, M., Jr. (2011). A numerical method for simulation of incompressible three-dimensional newtonian and non-newtonian flows. Numerical Heat Transfer Part B, 59(5), 360-380. http://dx.doi.org/10.1080/10407790.2011.572727.

18 Tutar, M., & Karakus, A. (2014). Numerical study of polymer melt flow in a three-dimensional sudden expansion: viscous dissipation effects. Journal of Polymer Engineering, 34(8), 755-764. http://dx.doi.org/10.1515/polyeng-2013-0273.

19 Gao, P. (2022). Three dimensional finite element computation of the non-isothermal polymer filling process by the phase field model. Advances in Engineering Software, 172, 103207. http://dx.doi.org/10.1016/j.advengsoft.2022.103207.

20 Fernandes, C., Pontes, A. J., Viana, J. C., & Gaspar-Cunha, A. (2016). Modeling and optimization of the injection-molding process: a review. Advances in Polymer Technology, 37(2), 429-449. http://dx.doi.org/10.1002/adv.21683.

21 Liao, T., Zhao, X., Yang, X., Whiteside, B., Coates, P., Jiang, Z., & Men, Y. (2019). Predicting the location of weld line in microinjection‐molded polyethylene via molecular orientation distribution. Journal of Polymer Science. Part B, Polymer Physics, 57(24), 1705-1715. http://dx.doi.org/10.1002/polb.24905.

22 Mourya, A., Nanda, A., Parashar, K., Sushant, & Kumar, R. (2022). An explanatory study on defects in plastic molding parts caused by machine parameters in injection molding process. Materials Today: Proceedings, 78(Pt 3), 656-661. http://dx.doi.org/10.1016/j.matpr.2022.12.070.

23 Corazza, E. J., Sacchelli, C. M., & Marangoni, C. (2012). Cycle time reduction of thermoplastic injection using nitriding treatment surface molds. Información Tecnológica, 23(3), 51-58. http://dx.doi.org/10.4067/S0718-07642012000300007.

24 Miranda, D. A., Rauber, W. K., Vaz, M., Jr., Alves, M. V. C., Lafratta, F. H., Nogueira, A. L., & Zdanski, P. S. B. (2023). Analysis of numerical modeling strategies to improve the accuracy of polymer injection molding simulations. Journal of Non-Newtonian Fluid Mechanics, 315, 105033. http://dx.doi.org/10.1016/j.jnnfm.2023.105033.

25 Gim, J., & Turng, L.-S. (2022). A review of current advancements in high surface quality injection molding: measurement, influencing factors, prediction, and control. Polymer Testing, 115, 107718. http://dx.doi.org/10.1016/j.polymertesting.2022.107718.

26 Hentati, F., Hadriche, I., Masmoudi, N., & Bradai, C. (2020). Experimental design to enhance the surface appearance, the internal structure, and the shear stresses of injected and metallized polycarbonate/acrylonitrile–butadiene-styrene parts. Journal of Applied Polymer Science, 137(7), 48384. http://dx.doi.org/10.1002/app.48384.

27 Yu, S., Kong, W., Xu, L., Zou, J., Han, W., Liu, Z., Luo, J., Xie, Z., Wu, H., & Zhou, H. (2023). Cellular distribution and warpage deformation in double-sided in-mold decoration combined with microcellular injection molding process. Journal of Materials Processing Technology, 317, 117982. http://dx.doi.org/10.1016/j.jmatprotec.2023.117982.

28 Acrylonitrile Butadiene Styrene – ABS. (2010). ABS –750 - Data Sheet of Acrylonitrile Butadiene Styrene. South Korea: Kumho Petrochemical.

29 Miranda, D. A., Rauber, W. K., Vaz, M., Jr., Nogueira, A. L., Bom, R. P., & Zdanski, P. S. B. (2023). Evaluation of the predictive capacity of viscosity models in polymer melt filling simulations. Journal of Materials Engineering and Performance, 32(4), 1707-1720. http://dx.doi.org/10.1007/s11665-022-07200-w.

30 Marin, F., Souza, A. F., Ahrens, C. H., & Lacalle, L. N. L. (2021). A new hybrid process combining machining and selective laser melting to manufacture an advanced concept of conformal cooling channels for plastic injection molds. International Journal of Advanced Manufacturing Technology, 113(5), 1561-1576. http://dx.doi.org/10.1007/s00170-021-06720-4.

31 Kennedy, P. K. (2008). Practical and scientific aspects of injection molding simulation (Doctoral thesis). Technische Universiteit Eindhoven, Eindhoven, Netherlands. http://dx.doi.org/10.6100/IR634914.

32 Hétu, J.-F., Gao, D. M., Garcia-Rejon, A., & Salloum, G. (1998). 3D finite element method for the simulation of the filling stage in injection molding. Polymer Engineering and Science, 38(2), 223-236. http://dx.doi.org/10.1002/pen.10183.

33 Solanki, B. S., Singh, H., & Sheorey, T. (2022). Towards an accurate pressure estimation in injection molding simulation using surrogate modeling. International Journal on Interactive Design and Manufacturing, 16(4), 1615-1632. http://dx.doi.org/10.1007/s12008-022-00887-0.

34 Saad, S., Sinha, A., Cruz, C., Régnier, G., & Ammar, A. (2022). Towards an accurate pressure estimation in injection molding simulation using surrogate modeling. International Journal of Material Forming, 15(6), 72. http://dx.doi.org/10.1007/s12289-022-01717-0.

35 Trad, M. A. B., Demers, V., Côté, R., Sardarian, M., & Dufresne, L. (2020). Numerical simulation and experimental investigation of mold filling and segregation in low-pressure powder injection molding of metallic feedstock. Advanced Powder Technology, 31(3), 1349-1358. http://dx.doi.org/10.1016/j.apt.2020.01.018.

36 Miranda, D. A., & Nogueira, A. L. (2019). Simulation of an injection process using a CAE tool: assessment of operational conditions and mold design on the process efficiency. Materials Research, 22(2), e20180564. http://dx.doi.org/10.1590/1980-5373-mr-2018-0564.

37 Marin, F., Souza, A. F., Pabst, R. G., & Ahrens, C. H. (2019). Influences of the mesh in the CAE simulation for plastic injection molding. Polímeros, 29(3), e2019043. http://dx.doi.org/10.1590/0104-1428.05019.

38 Richardson, L. F. (1911). The approximate arithmetical solution by finite differences of physical problems involving differential equations with an application to the stresses in a masonary dam. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 210(459-470), 307-357. http://dx.doi.org/10.1098/rsta.1911.0009.
 

660c49d6a953957bc56124e3 polimeros Articles
Links & Downloads

Polímeros: Ciência e Tecnologia

Share this page
Page Sections