Predicting the Warpage of Plastic Products during the Injection Molding Process using the BBM Method

Authors

  • Van-Long Trinh School of Mechanical and Automotive Engineering, Hanoi University of Industry, Hanoi, Vietnam
  • Xuan-Chung Nguyen Vietnam-Japan Center, Hanoi University of Industry, Hanoi, Vietnam
  • Tien-Sy Nguyen School of Mechanical and Automotive Engineering, Hanoi University of Industry, Hanoi, Vietnam
  • Viet-Hoi Tran School of Mechanical and Automotive Engineering, Hanoi University of Industry, Hanoi, Vietnam
  • Huy-Kien Nguyen Department of Science and Technology, Hanoi University of Industry, Hanoi, Vietnam
  • Van-Nam Hoang Vietnam-Japan Center, Hanoi University of Industry, Hanoi, Vietnam
Volume: 15 | Issue: 3 | Pages: 22895-22900 | June 2025 | https://doi.org/10.48084/etasr.10838

Abstract

During the injection molding process, the warpage prediction plays a significant role in order to improve the quality of a product. This procedure depends on a variety of parameters, such as pressure, melting temperature, packing, and filling time. Polyethylene Terephthalate Glycol (PETG) is a thermoplastic material that exhibits a great bending attribute, heat resistance, and durability. This paper examines a method for predicting the warpage defect using the Box-Behnken Method (BBM). The mathematical model is formed to predict the warpage with high reliability, resulting in R-Sq of 97.81%, R-Sq(adj) of 95.26%, and R-Sq(pred) of 87.39%. The optimal processing parameters are a packing time of 5.5 s, a melting temperature of 270 ᵒC, an injection pressure of 217.4 MPa, and a filling time of 0.8 s. The results show that the proposed method is effective in processing engineering problems related to the prediction and optimization of manufacturing systems for improving sustainability and product quality.

Keywords:

PETG, injection molding, Response Surface Methodology (RSM), processing parameters, warpage, optimization

Downloads

Download data is not yet available.

References

Y. Lockner and C. Hopmann, "Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks," The International Journal of Advanced Manufacturing Technology, vol. 112, no. 11, pp. 3501–3513, Feb. 2021. DOI: https://doi.org/10.1007/s00170-020-06511-3

D. H. Chun, B. H. You, and D. J. Song, "Injection molding analysis of a needle cover — Optimum filling for gate location design," Fibers and Polymers, vol. 13, no. 9, pp. 1185–1189, Nov. 2012. DOI: https://doi.org/10.1007/s12221-012-1185-6

C. Yan et al., "PETG: Applications in Modern Medicine," Engineered Regeneration, vol. 5, no. 1, pp. 45–55, Mar. 2024. DOI: https://doi.org/10.1016/j.engreg.2023.11.001

M. T. Sepahi, H. Abusalma, V. Jovanovic, and H. Eisazadeh, "Mechanical Properties of 3D-Printed Parts Made of Polyethylene Terephthalate Glycol," Journal of Materials Engineering and Performance, vol. 30, no. 9, pp. 6851–6861, Sep. 2021. DOI: https://doi.org/10.1007/s11665-021-06032-4

R. A. Bubeck and M. A. Barger, "Injection Blow Molding Technology for Polyethylene Terephthalate," International Polymer Processing, vol. 15, no. 4, pp. 337–342, Dec. 2000. DOI: https://doi.org/10.3139/217.1616

I. Pratto, M. C. A. Busato, and P. R. S. Bittencourt, "Thermal and mechanical characterization of thermoplastic orthodontic aligners discs after molding process," Journal of the Mechanical Behavior of Biomedical Materials, vol. 126, Feb. 2022, Art. no. 104991. DOI: https://doi.org/10.1016/j.jmbbm.2021.104991

D. Annicchiarico and J. R. Alcock, "Review of Factors that Affect Shrinkage of Molded Part in Injection Molding," Materials and Manufacturing Processes, vol. 29, no. 6, pp. 662–682, Jun. 2014. DOI: https://doi.org/10.1080/10426914.2014.880467

B. Ozcelik, A. Ozbay, and E. Demirbas, "Influence of injection parameters and mold materials on mechanical properties of ABS in plastic injection molding," International Communications in Heat and Mass Transfer, vol. 37, no. 9, pp. 1359–1365, Nov. 2010. DOI: https://doi.org/10.1016/j.icheatmasstransfer.2010.07.001

V. V. Prabhakaran and A. Singh, "Enhancing Power Quality in PV-SOFC Microgrids Using Improved Particle Swarm Optimization," Engineering, Technology & Applied Science Research, vol. 9, no. 5, pp. 4616–4622, Oct. 2019. DOI: https://doi.org/10.48084/etasr.2963

N. C. Eli-Chukwu, J. M. Aloh, and C. O. Ezeagwu, "A Systematic Review of Artificial Intelligence Applications in Cellular Networks," Engineering, Technology & Applied Science Research, vol. 9, no. 4, pp. 4504–4510, Aug. 2019. DOI: https://doi.org/10.48084/etasr.2788

C. Shen, L. Wang, and Q. Li, "Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method," Journal of Materials Processing Technology, vol. 183, no. 2, pp. 412–418, Mar. 2007. DOI: https://doi.org/10.1016/j.jmatprotec.2006.10.036

Y.-S. Lai, W.-Z. Lin, Y.-C. Lin, and J.-P. Hung, "Development of Surface Roughness Prediction and Monitoring System in Milling Process," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12797–12805, Feb. 2024. DOI: https://doi.org/10.48084/etasr.6664

S. Kashyap and D. Datta, "Process parameter optimization of plastic injection molding: a review," International Journal of Plastics Technology, vol. 19, no. 1, pp. 1–18, Jun. 2015. DOI: https://doi.org/10.1007/s12588-015-9115-2

A. López, J. Aisa, A. Martinez, and D. Mercado, "Injection moulding parameters influence on weight quality of complex parts by means of DOE application: Case study," Measurement, vol. 90, pp. 349–356, Aug. 2016. DOI: https://doi.org/10.1016/j.measurement.2016.04.072

A. M. Saadoon, M. A. Gharawi, and A. Al-Mosawe, "Effect of Elevated Temperature on Microstructure and Mechanical Properties of Hot-Rolled Steel," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 18756–18766, Dec. 2024. DOI: https://doi.org/10.48084/etasr.9108

Y. Gao and X. Wang, "An effective warpage optimization method in injection molding based on the Kriging model," The International Journal of Advanced Manufacturing Technology, vol. 37, no. 9, pp. 953–960, Jun. 2008. DOI: https://doi.org/10.1007/s00170-007-1044-6

V. A. Yiga, M. Lubwama, S. Pagel, P. W. Olupot, J. Benz, and C. Bonten, "Optimization of tensile strength of PLA/clay/rice husk composites using Box Behnken design," Biomass Conversion and Biorefinery, vol. 13, no. 4, pp. 11727–11753, 2021. DOI: https://doi.org/10.1007/s13399-021-01971-3

M. Alhajabdalla, H. Mahmoud, M. S. Nasser, I. A. Hussein, R. Ahmed, and H. Karami, "Application of Response Surface Methodology and Box–Behnken Design for the Optimization of the Stability of Fibrous Dispersion Used in Drilling and Completion Operations," ACS Omega, vol. 6, no. 4, pp. 2513–2525, Feb. 2021. DOI: https://doi.org/10.1021/acsomega.0c04272

S. Afshin et al., "Application of Box–Behnken design for optimizing parameters of hexavalent chromium removal from aqueous solutions using Fe3O4 loaded on activated carbon prepared from alga: Kinetics and equilibrium study," Journal of Water Process Engineering, vol. 42, Aug. 2021, Art. no. 102113. DOI: https://doi.org/10.1016/j.jwpe.2021.102113

G. Dwivedi and M. P. Sharma, "Application of Box–Behnken design in optimization of biodiesel yield from Pongamia oil and its stability analysis," Fuel, vol. 145, pp. 256–262, Apr. 2015. DOI: https://doi.org/10.1016/j.fuel.2014.12.063

N. M. Abd-El-Aziz, M. S. Hifnawy, A. A. El-Ashmawy, R. A. Lotfy, and I. Y. Younis, "Application of Box-Behnken design for optimization of phenolics extraction from Leontodon hispidulus in relation to its antioxidant, anti-inflammatory and cytotoxic activities," Scientific Reports, vol. 12, no. 1, May 2022, Art. no. 8829. DOI: https://doi.org/10.1038/s41598-022-12642-2

S. L. C. Ferreira et al., "Box-Behnken design: An alternative for the optimization of analytical methods," Analytica Chimica Acta, vol. 597, no. 2, pp. 179–186, Aug. 2007. DOI: https://doi.org/10.1016/j.aca.2007.07.011

A. Belgada et al., "Optimization of phosphate/kaolinite microfiltration membrane using Box–Behnken design for treatment of industrial wastewater," Journal of Environmental Chemical Engineering, vol. 9, no. 1, Feb. 2021, Art. no. 104972. DOI: https://doi.org/10.1016/j.jece.2020.104972

N. Elboughdiri, D. Ghernaout, K. Kriaa, and B. Jamoussi, "Enhancing the Extraction of Phenolic Compounds from Juniper Berries Using the Box-Behnken Design," ACS Omega, vol. 5, no. 43, pp. 27990–28000, Nov. 2020. DOI: https://doi.org/10.1021/acsomega.0c03396

A. Agi et al., "Process optimization of reservoir fines trapping by mesoporous silica nanoparticles using Box-Behnken design," Alexandria Engineering Journal, vol. 61, no. 11, pp. 8809–8821, Nov. 2022. DOI: https://doi.org/10.1016/j.aej.2022.02.016

P. Yadav, V. Rastogi, and A. Verma, "Application of Box–Behnken design and desirability function in the development and optimization of self-nanoemulsifying drug delivery system for enhanced dissolution of ezetimibe," Future Journal of Pharmaceutical Sciences, vol. 6, no. 1, Mar. 2020, Art. no. 7. DOI: https://doi.org/10.1186/s43094-020-00023-3

B. Freeland et al., "A Review of Polylactic Acid as a Replacement Material for Single-Use Laboratory Components," Materials, vol. 15, no. 9, Jan. 2022, Art. no. 2989. DOI: https://doi.org/10.3390/ma15092989

"PETG materials," Moldflow Adviser 2023, 2023. https://help.autodesk.com/view/MFAA/2023/ENU/?guid=MoldflowAdviser_CLC_Materials_materials_for_inj_molding_part_materials_thermoplastics_materials_PETG_materials_html.

A. Balasubramanian, F. Martin, M. M. Billah, O. Osemwinyen, and A. Belahcen, "Application of Surrogate Optimization Routine with Clustering Technique for Optimal Design of an Induction Motor," Energies, vol. 14, no. 16, Jan. 2021, Art. no. 5042. DOI: https://doi.org/10.3390/en14165042

S. Banerjee, M. Joshi, and A. K. Ghosh, "Optimization of polypropylene/clay nanocomposite processing using Box-Behnken statistical design," Journal of Applied Polymer Science, vol. 123, no. 4, pp. 2042–2051, 2012. DOI: https://doi.org/10.1002/app.34566

Downloads

How to Cite

[1]
V.-L. Trinh, X.-C. Nguyen, T.-S. Nguyen, V.-H. Tran, H.-K. Nguyen, and V.-N. Hoang, “Predicting the Warpage of Plastic Products during the Injection Molding Process using the BBM Method”, Eng. Technol. Appl. Sci. Res., vol. 15, no. 3, pp. 22895–22900, Jun. 2025.

Metrics

Abstract Views: 324
PDF Downloads: 374

Metrics Information