The Effect of Processing Parameters on Density and Weight during the Injection Molding Process
Received: 24 February 2025 | Revised: 5 April 2025 | Accepted: 9 April 2025 | Online: 4 June 2025
Corresponding author: Van Long Trinh
Abstract
The Injection Molding Process (IMP) produces a mass amount of plastic products with high complex shape, low cost, and feasible machinability. However, there are certain problems during IMP linked to product weight and density, affecting the stability and harness of the product, and thus its main quality. This paper examines the influence of injection molding parameters on plastic product density and weight during IMP. The material used for the present study is Medium Density Polyethylene (MDPE) with distinguished properties, namely chemical resistance, low cost, and easy processing. The experiment is designed based on the Taguchi method along with the crucial injection molding parameters, including melt temperature (A), packing pressure (B), packing time (C), cooling time (D), and mold temperature (E). The experiments are carried out using Moldex3D software. The Analysis of Variance (ANOVA) provides the results of the aforementioned parameters’ influence on product density and weight. It is demonstrated that packing time (C), cooling time (D), and mold temperature (E) are the main factors influencing density, while melt temperature (A) and packing pressure (B) are the main factors affecting weight.
Keywords:
IMP, processing parameters, optimization, ANOVA, Taguchi, MDPEDownloads
References
W. Michaeli and A. Schreiber, "Online control of the injection molding process based on process variables," Advances in Polymer Technology, vol. 28, no. 2, pp. 65–76, 2009.
H. Hassan, "An experimental work on the effect of injection molding parameters on the cavity pressure and product weight," The International Journal of Advanced Manufacturing Technology, vol. 67, no. 1, pp. 675–686, Jul. 2013.
T. Ageyeva, S. Horváth, and J. G. Kovács, "In-Mold Sensors for Injection Molding: On the Way to Industry 4.0," Sensors, vol. 19, no. 16, Jan. 2019, Art. no. 3551.
Y. Yang and F. Gao, "Injection molding product weight: Online prediction and control based on a nonlinear principal component regression model," Polymer Engineering & Science, vol. 46, no. 4, pp. 540–548, 2006.
Y. Peng, H. Li, and L.-S. Turng, "Development of a rheo-dielectric sensor for online shear stress measurement during the injection molding process," Polymer Engineering & Science, vol. 50, no. 1, pp. 61–68, 2010.
T. Takayama and R. Shibazaki, "Evaluating mechanical anisotropy of injection molded polymer products using short beam shear testing," Results in Materials, vol. 19, Sep. 2023, Art. no. 100434.
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.
X. Zhou, Y. Zhang, T. Mao, and H. Zhou, "Monitoring and dynamic control of quality stability for injection molding process," Journal of Materials Processing Technology, vol. 249, pp. 358–366, Nov. 2017.
K.-M. Tsai, C.-Y. Hsieh, and W.-C. Lo, "A study of the effects of process parameters for injection molding on surface quality of optical lenses," Journal of Materials Processing Technology, vol. 209, no. 7, pp. 3469–3477, Apr. 2009.
C. Fernandes, A. J. Pontes, J. C. Viana, and A. G. Cunha, "Modeling and Optimization of the Injection‐Molding Process: A Review," Advances in Polymer Technology, vol. 37, no. 2, pp. 429–449, Jan. 2016.
Y. Ma, X. Wang, K. Dang, Y. Zhou, W. Yang, and P. Xie, "Intelligent recommendation system of the injection molding process parameters based on CAE simulation, process window, and machine learning," The International Journal of Advanced Manufacturing Technology, vol. 128, no. 9, pp. 4703–4716, Oct. 2023.
F. Hentati, I. Hadriche, N. Masmoudi, and C. Bradai, "Optimization of the injection molding process for the PC/ABS parts by integrating Taguchi approach and CAE simulation," The International Journal of Advanced Manufacturing Technology, vol. 104, no. 9, pp. 4353–4363, Oct. 2019.
F. De Santis, L. Boragno, D. Jeremic, and A. R. Albunia, "Trimodal polyethylene polymer design for more sustainable packaging applications," Polymer, vol. 295, Mar. 2024, Art. no. 126740.
C. Metha, S. Pawar, and V. Suvarna, "Recent advancements in alginate-based films for active food packaging applications," Sustainable Food Technology, vol. 2, no. 5, pp. 1246–1265, Sep. 2024.
S. Sivalingam, A. Devaraju, V. Asokan, and Y. Vaidhyanathan, "Optimization of Ultrasonically Welded High-Density Polyethylene with Medium-Density Polyethylene," in Recent Advances in Materials and Modern Manufacturing, Singapore, 2022, pp. 493–506.
L. Chen and Z. Lin, "Polyethylene: Properties, Production and Applications," in 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST), Guangzhou, China, Dec. 2021, pp. 1191–1196.
X. Li, B. Hu, and R. Du, "Predicting the Parts Weight in Plastic Injection Molding Using Least Squares Support Vector Regression," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 38, no. 6, pp. 827–833, Nov. 2008.
W. Ding, H. Ali, K. Gao, Z. Zhang, and F. Gao, "Novel deep learning based soft sensor feature extraction for part weight prediction in injection molding processes," Journal of Manufacturing Systems, vol. 78, pp. 58–68, Feb. 2025.
S. Horváth and J. G. Kovács, "Real-time product weight estimation based on internal pressure monitoring in injection molding," Polymer Engineering & Science, vol. 65, no. 4, pp. 1693–1701, 2025.
Z. Dong, P. Zhao, K. Ji, Y. Chen, S. Gao, and J. Fu, "In-situ density measurement for plastic injection molding via ultrasonic technology," Frontiers of Mechanical Engineering, vol. 17, no. 4, Jan. 2023, Art. no. 58.
N. Zhao, J. Lian, P. Wang, and Z. Xu, "Recent progress in minimizing the warpage and shrinkage deformations by the optimization of process parameters in plastic injection molding: a review," The International Journal of Advanced Manufacturing Technology, vol. 120, no. 1, pp. 85–101, May 2022.
A. R. Hayyawi, H. Al-Ethari, and A. H. Haleem, "Optimization of the PM-EDM Process Parameters for Ti-35Nb-7Zr-5Ta Bio Alloy," Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 13982–13989, Jun. 2024.
S. Phokha, C. Sasen, P. Nasawat, and N. Kanchanaruangrong, "Optimization of Rubber Sheet Rolling Machine Parameters using a Taguchi-based TOPSIS Linear Programming Model," Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 20508–20516, Feb. 2025.
B. L. Mahdi, A. H. Ali, H. K. Hussein, and O. F. Abdulateef, "The Influence of Cutting Parameters on the Surface Hardness in Turning of 6061 Aluminum Alloy," Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 17118–17124, Oct. 2024.
S. S. Abhilash, R. Luckose, and D. Lenin Singaravelu, "Processing and characterization of HDPE and MDPE processed by rotational moulding," Materials Today: Proceedings, vol. 27, pp. 2029–2032, Jan. 2020.
M. W. Hisam, A. A. Dar, M. O. Elrasheed, M. S. Khan, R. Gera, and I. Azad, "The Versatility of the Taguchi Method: Optimizing Experiments Across Diverse Disciplines," Journal of Statistical Theory and Applications, vol. 23, no. 4, pp. 365–389, Dec. 2024.
R. N. Yerrawar and R. R. Arakerimath, "Parametric Analysis of Magnetorheological Strut for Semiactive Suspension System Using Taguchi Method," Engineering, Technology & Applied Science Research, vol. 8, no. 4, pp. 3218–3222, Aug. 2018.
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