Optimizing injection molding parameters to minimize and prediction potential for flashing defects

Ramadhan Araya Ismoyo; Moh. Hartono; Anggit Murdani

doi:10.30811/jpl.v22i2.4576

Optimizing injection molding parameters to minimize and prediction potential for flashing defects

Ramadhan Araya Ismoyo, Moh. Hartono, Anggit Murdani

Abstract

The injection molding process is a manufacturing process that can produce products in a short time in large quantities, in the injection molding process the factor of setting process parameters plays a significant role in product quality, so it requires special treatment. The purpose of this study is to find the optimal parameters in the injection moulding process of yogurth container lid with polypropylene material, so that the process can reduce the incidence of flashing defects that result in the emergence of initial waste in the industrial environment. The method used in this research is to create a Response Surface Methodology Box-Behnken Design (RSM - BBD) optimization model and an Artificial Neural Network (ANN) model approach in analyzing optimal parameters and predicting the appearance of flashing defects in a designed cycle. The results obtained from this research are the optimal parameters from the RSM and ANN model recommendations, namely the clamping force setting of 70 tons, holding time 0.1 seconds, and holding pressure. The ANN model provides the highest level of prediction accuracy with an R² value of 100% and a prediction error rate of 7.9689E-09. In comparison, the RSM model obtains a prediction accuracy level with an R² of 71% with an error rate of 0.24315.

Keywords

injection molding, RSM, ANN, optimization, model

Full Text:

PDF

References

Bulk Chemicals (/industry/bulk-chemicals), “Industrial Plastic Market Size, Share & Trends Analysis Report By Product (ABS, PBT, PEEK), By Application (Building & Construction, Automotive & Transportation), By Region, And Segment Forecasts, 2020 - 2027,” 2020.

M. Hartono, G. Gumono, W. Wirawan, N. Qosim, and H. Wicaksono, “Quality by design of optimum parameter to minimize the weight of plastic products,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1073, no. 1, p. 012076, 2021, doi: 10.1088/1757-899x/1073/1/012076.

B. Ravikiran, D. K. Pradhan, S. Jeet, D. K. Bagal, A. Barua, and S. Nayak, “Parametric optimization of plastic injection moulding for FMCG polymer moulding (PMMA) using hybrid Taguchi-WASPAS-Ant Lion optimization algorithm,” Mater. Today Proc., vol. 56, no. xxxx, pp. 2411–2420, 2022, doi: 10.1016/j.matpr.2021.08.204.

C. Y. Chung, S. S. Hwang, S. C. Chen, and M. C. Lai, “Effects of injection molding process parameters on the chemical foaming behavior of polypropylene and polystyrene,” Polymers (Basel)., vol. 13, no. 14, 2021, doi: 10.3390/polym13142331.

M. Hartono, Gumono, H. Wicaksono, and R. A. Ismoyo, “Characterization of Differences in Cavity Positions Orientation in the Multi Cavity Plastic Molding Process on the Product Defects,” Proc. 2022 Annu. Technol. Appl. Sci. Eng. Conf. (ATASEC 2022), pp. 76–90, 2022, doi: 10.2991/978-94-6463-106-7_8.

S. Saifuddin, R. Usman, and Z. Zuhaimi, “Pembuatan gelas dengan bahan polypropylene dengan menggunakan cetakan plastik,” J. POLIMESIN, vol. 16, no. 2, p. 30, 2018, doi: 10.30811/jpl.v16i2.558.

Q. Wang, J. Wang, C. Yang, K. Du, W. Zhu, and X. Zhang, “Effect of process parameters on repeatability precision of weight for microinjection molding products,” Adv. Polym. Technol., vol. 2019, 2019, doi: 10.1155/2019/2604878.

X. Zhou, Y. Zhang, T. Mao, and H. Zhou, “Monitoring and dynamic control of quality stability for injection molding process,” J. Mater. Process. Technol., vol. 249, no. March, pp. 358–366, 2017, doi: 10.1016/j.jmatprotec.2017.05.038.

M. Hartono, Pratikno, B. Purnomo, and Sugiono, “Optimization of the L 36 mixed-level controllable factors of Taguchi parameter design on the plastic injection molding process for minimizing defects,” pp. 166–171, 2016.

D. O. Kazmer, Injection Mold Design Engineering, 2nd ed. Germany: Hanser Publications, 2016.

M. Hartono, Pratikto, P. B. Santoso, and Sugiono, “Optimization on the Injection Molding Propypopylene Parameters Using Central Composite Design for Minimizing Defects,” J. Southwest Jiaotong Univ., vol. 55, no. 2, 2020, doi: 10.35741/issn.0258-2724.55.2.42.

M. Hartono, “Meningkatkan Mutu Produk Plastik Dengan Metode Taguchi,” J. Tek. Ind., vol. 13, no. 1, pp. 93–100, 2012, doi: 10.22219/jtiumm.vol13.no1.93-100.

W. R. Jong, Y. M. Huang, Y. Z. Lin, S. C. Chen, and Y. W. Chen, “Integrating Taguchi method and artificial neural network to explore machine learning of computer aided engineering,” J. Chinese Inst. Eng. Trans. Chinese Inst. Eng. A, vol. 43, no. 4, pp. 346–356, 2020, doi: 10.1080/02533839.2019.1708804.

M. Mittal and K. Phutela, “Modeling and parameters optimization of biocomposite using box-Behnken response surface methodology,” J. Appl. Res. Technol., vol. 21, no. 6, pp. 991–1018, 2023, doi: 10.22201/icat.24486736e.2023.21.6.2012.

M. A. Md Ali et al., “Fill Time Optimization Analysis In Flow Simulation Of Injection Molding Using Response Surface Method,” Malaysian J. Compos. Sci. Manuf., vol. 4, no. 1, pp. 28–39, 2021, doi: 10.37934/mjcsm.4.1.2839.

A. T. N. A. Miza, Z. Shayfull, N. Z. Noriman, S. M. Nasir, M. H. N. Hidayah, and R. Norshahira, “Optimization of warpage on front panel housing part using response surface methodology (RSM,” AIP Conf. Proc., vol. 2030, no. July, 2019, doi: 10.1063/1.5066794.

J. Lee, D. Yang, K. Yoon, and J. Kim, “Effects of Input Parameter Range on the Accuracy of Artificial Neural Network Prediction for the Injection Molding Process,” Polymers (Basel)., vol. 14, no. 9, 2022, doi: 10.3390/polym14091724.

D. Leni, A. K, R. Sumiati, Adriansyah, and Haris, “Prediction modeling of low-alloy steel based on chemical composition and heat treatment using artificial neural network,” Polimesin, vol. 20, no. 2, pp. 121–127, 2023, [Online]. Available: https://ejurnal.pnl.ac.id/polimesin/article/view/3626/3230.

M. H. Tsai et al., “Development of an Online Quality Control System for Injection Molding Process,” Polymers (Basel)., vol. 14, no. 8, 2022, doi: 10.3390/polym14081607.

J. C. Chen, G. Guo, and W. N. Wang, “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., vol. 110, no. 7–8, pp. 2023–2033, 2020, doi: 10.1007/s00170-020-06011-4.

Kementerian Lingkungan Hidup dan Kehutanan, PerMen LHK Nomor P.75/MENLHK/SETJEN/KUM.1/10/2019 tentang Peta Jalan Pengurangan Sampah Oleh Produsen. 2019.

J. Lee, D. Yang, K. Yoon, and J. Kim, “Effects of Input Parameter Range on the Accuracy of Artificial Neural Network Prediction for the Injection Molding Process,” Polymers, vol. 14, no. 9. 2022, doi: 10.3390/polym14091724.

R. Kerkstra, Injection Molding Advanced Troubleshooting Guide. Hanser Publications, 2018.

K. Palanikumar, “Introductory Chapter: Response Surface Methodology in Engineering Science,” Response Surf. Methodol. Eng. Sci., 2021, doi: 10.5772/intechopen.100484.

J. Jeon, B. Rhee, and J. Gim, “Melt Temperature Estimation by Machine Learning Model Based on Energy Flow in Injection Molding,” Polymers (Basel)., vol. 14, no. 24, 2022, doi: 10.3390/polym14245548.

M. S. Huang, S. C. Nian, J. Y. Chen, and C. Y. Lin, “Influence of clamping force on tie-bar elongation, mold separation, and part dimensions in injection molding,” Precis. Eng., vol. 51, no. November, pp. 647–658, 2018,doi:10.1016/j.precisioneng.2017.11.007.

G. Trotta, S. Cacace, and Q. Semeraro, “Optimizing process parameters in micro injection moulding considering the part weight and probability of flash formation,” J. Manuf. Process., vol. 79, no. February, pp. 250–258, 2022, doi: 10.1016/j.jmapro.2022.04.048.

G. Trotta, B. Stampone, I. Fassi, and L. Tricarico, “Study of rheological behaviour of polymer melt in micro injection moulding with a miniaturized parallel plate rheometer,” Polym. Test., vol. 96, p. 107068, 2021, doi: 10.1016/j.polymertesting.2021.107068.

J. B. Tranter, P. Refalo, and A. Rochman, “Towards sustainable injection molding of ABS plastic products,” J. Manuf. Process., vol. 29, pp. 399–406, 2017, doi: 10.1016/j.jmapro.2017.08.015.

DOI: http://dx.doi.org/10.30811/jpl.v22i2.4576