Skip to main content
SpringerLink
Log in

Application of supervised machine learning methods in injection molding process for initial parameters setting: prediction of the cooling time parameter

  • Regular Paper
  • Published:
Progress in Artificial Intelligence Aims and scope Submit manuscript

Abstract

The injection molding process is considered as one of the most used process in the plastics industry due to its reliability and its profitability; however nowadays, the injection industry marketplace becomes more and more competitive because of the excessive quality demand and the coast reduction requirement. Production workshops strive constantly to reduce coast and optimizing the process. One key optimization factor involves determining the optimal cooling time parameters during the initial setup phase. The cooling time parameter represents around 65% of the cycle time. The main goal of this study is to explore the implementation of various supervised machine learning methods for predicting the cooling time parameter and to compare their performance. Five algorithms, namely random forest, decision tree, KNN (K-nearest neighbors), XGBoost, and multiple regression, were employed in the analysis. The study aims to assess the effectiveness of these algorithms in predicting the cooling time parameter within the context of the injection molding process. To evaluate their efficiency, the study employed the following metrics: mean absolute error (MAE), root mean square error (RMSE), mean squared error (MSE), and mean average percentage error (MAPE). The dataset was collected from a real industrial workshop, encompassing 70 plastic components, 10 distinct material types, and 7 different types of machines. Despite the complexity and non-linearity among the process parameters, the study indicates that machine learning can still effectively capture and predict cooling time parameters. XGBoost, KNN, and random forest consistently demonstrate superior results across all metrics compared to decision tree and multiple regression, as example, the mean average percentage error (MAPE) of XGBoost is 14.76%, significantly outperforming the 23.96% MAPE associated with the decision tree. These outcomes validate that machine learning methods can play a significant role in predicting cooling time and contribute to the optimization of the overall process.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Selvaraj, S.K., Raj, A., Rishikesh Mahadevan, R., Chadha, U., Paramasivam, V.: A review on machine learning models in injection molding machines. Adv. Mater. Sci. Eng. (2022). https://doi.org/10.1155/2022/1949061

    Article  Google Scholar 

  2. Khan, M., Afaq, S.K., Khan, N.U., Ahmad, S.: Cycle time reduction in injection molding process by selection of robust cooling channel design. ISRN Mech. Eng. 2014, 1–8 (2014). https://doi.org/10.1155/2014/968484

    Article  Google Scholar 

  3. Singh, G., Verma, A.: A brief review on injection moulding manufacturing process. Mater. Today Proc. 4(2), 1423–1433 (2017). https://doi.org/10.1016/j.matpr.2017.01.164

    Article  Google Scholar 

  4. Prashanth Reddy, K., Panitapu, B.: High thermal conductivity mould insert materials for cooling time reduction in thermoplastic injection moulds. Mater. Today Proc. 4(2), 519–526 (2017). https://doi.org/10.1016/j.matpr.2017.01.052

    Article  Google Scholar 

  5. Khosravani, M.R., Nasiri, S.: Injection molding manufacturing process: review of case-based reasoning applications. J. Intell. Manuf. 31(4), 847–864 (2020). https://doi.org/10.1007/s10845-019-01481-0

    Article  Google Scholar 

  6. Kanbur, B.B., Suping, S., Duan, F.: Design and optimization of conformal cooling channels for injection molding: a review. Int. J. Adv. Manuf. Technol. 106(7–8), 3253–3271 (2020). https://doi.org/10.1007/s00170-019-04697-9

    Article  Google Scholar 

  7. Gao, Z., Dong, G., Tang, Y., Zhao, Y.F.: Machine learning aided design of conformal cooling channels for injection molding. J. Intell. Manuf. (2021). https://doi.org/10.1007/s10845-021-01841-9

    Article  Google Scholar 

  8. Fernandes, C., Pontes, A.J., Viana, J.C., Gaspar-Cunha, A.: Modeling and optimization of the injection-molding process: a review. Adv. Polym. Technol. 37(2), 429–449 (2018). https://doi.org/10.1002/adv.21683

    Article  Google Scholar 

  9. Pratap, B., Gupta, R.K., Yadav, A., Nag, M.: Plastic injection molding and its process parameters. In: Presented at the Proceedings of Advanced Material, Engineering & Technology, Seoul, South Korea, p. 050053 (2020). https://doi.org/10.1063/5.0024291.

  10. Jung, H., Jeon, J., Choi, D., Park, J.-Y.: Application of machine learning techniques in injection molding quality prediction: implications on sustainable manufacturing industry. Sustainability 13(8), 4120 (2021). https://doi.org/10.3390/su13084120

    Article  Google Scholar 

  11. Kashyap, S., Datta, D.: Process parameter optimization of plastic injection molding: a review. Int. J. Plast. Technol. 19(1), 1–18 (2015). https://doi.org/10.1007/s12588-015-9115-2

    Article  Google Scholar 

  12. Biron, M.: Transformation des matières plastiques.

  13. Tayalati, F., Azmani, M., Azmani, A.: Artificial intelligence based plastic injection process for initial parameters setting and process monitoring-review. In: Smart Applications and Data Analysis, Cham, pp. 294–307 (2022). https://doi.org/10.1007/978-3-031-20490-6_24

  14. Zarkadas, D.M., Xanthos, M.: Prediction of cooling time in injection molding by means of a simplified semianalytical equation. Adv. Polym. Technol. 22(3), 188–208 (2003). https://doi.org/10.1002/adv.10048

    Article  Google Scholar 

  15. Sánchez, R., Aisa, J., Martinez, A., Mercado, D.: On the relationship between cooling setup and warpage in injection molding. Measurement 45(5), 1051–1056 (2012). https://doi.org/10.1016/j.measurement.2012.01.039

    Article  Google Scholar 

  16. Hopmann, C., Xiao, C., Kahve, C.E., Fellerhoff, J.: Prediction and validation of the specific volume for inline warpage control in injection molding. Polym. Test. 104, 107393 (2021). https://doi.org/10.1016/j.polymertesting.2021.107393

    Article  Google Scholar 

  17. Annicchiarico, D., Alcock, J.R.: Review of factors that affect shrinkage of molded part in injection molding. Mater. Manuf. Processes 29(6), 662–682 (2014). https://doi.org/10.1080/10426914.2014.880467

    Article  Google Scholar 

  18. Kuo, C.-C., Xu, Y.-X.: A simple method of improving warpage and cooling time of injection molded parts simultaneously. Int. J. Adv. Manuf. Technol. (2022). https://doi.org/10.1007/s00170-022-09925-3

    Article  Google Scholar 

  19. Fetecãu, C., Cosma, L., Stan, F.: Study of the cooling time for the injection of the plastic materials. Materiale Plastice. IJETAE 44(2) (2007).

  20. Kuo, C.-C., Jiang, Z.-F., Lee, J.-H.: Effects of cooling time of molded parts on rapid injection molds with different layouts and surface roughness of conformal cooling channels. Int. J. Adv. Manuf. Technol. 103(5–8), 2169–2182 (2019). https://doi.org/10.1007/s00170-019-03694-2

    Article  Google Scholar 

  21. Kanbur, B.B., Suping, S., Duan, F.: Design and optimization of conformal cooling channels for injection molding: a review. Int. J. Adv. Manuf. Technol. (2020). Accessed 09 Jan 2023. [Online]. https://www.semanticscholar.org/paper/Design-and-optimization-of-conformal-cooling-for-a-Kanbur-Suping/7f5a9ad21a37a5b22e8997c8a0dc6628994b3170

  22. Öktem, H., Shinde, D.: Determination of optimal process parameters for plastic injection molding of polymer materials using multi-objective optimization. J. Mater. Eng. Perform. 30(11), 8616–8632 (2021). https://doi.org/10.1007/s11665-021-06029-z

    Article  Google Scholar 

  23. Stelson, K.A.: Calculating cooling times for polymer injection moulding. Proc. Inst. Mech. Engineers B J. Eng. Manuf. 217(5), 709–713 (2003). https://doi.org/10.1243/095440503322011443

    Article  Google Scholar 

  24. Yu, C.J., Sunderland, J.E.: Determination of ejection temperature and cooling time in injection molding. Polym. Eng. Sci. 32(3), 191–197 (1992). https://doi.org/10.1002/pen.760320305

    Article  Google Scholar 

  25. Fetecãu, C., Cosma, L., Stan, F.: Study of the cooling time for the injection of the plastic materials. Materiale Plastice (2007).

  26. Abdul, R., Guo, G., Chen, J.C., Yoo, J.J.-W.: Shrinkage prediction of injection molded high density polyethylene parts with taguchi/artificial neural network hybrid experimental design. Int. J. Interact. Des. Manuf. 14(2), 345–357 (2020). https://doi.org/10.1007/s12008-019-00593-4

    Article  Google Scholar 

  27. Heinisch, J., Lockner, Y., Hopmann, C.: Comparison of design of experiment methods for modeling injection molding experiments using artificial neural networks. J. Manuf. Process. 61, 357–368 (2021). https://doi.org/10.1016/j.jmapro.2020.11.011

    Article  Google Scholar 

  28. Tercan, H., Guajardo, A., Heinisch, J., Thiele, T., Hopmann, C., Meisen, T.: Transfer-learning: bridging the gap between real and simulation data for machine learning in injection molding. Procedia CIRP 72, 185–190 (2018). https://doi.org/10.1016/j.procir.2018.03.087

    Article  Google Scholar 

  29. Lockner, Y., Hopmann, C.: Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks. Int. J. Adv. Manuf. Technol. 112(11–12), 3501–3513 (2021). https://doi.org/10.1007/s00170-020-06511-3

    Article  Google Scholar 

  30. Meiabadi, M.S., Vafaeesefat, A., Sharifi, F.: Optimization of plastic injection molding process by combination of artificial neural network and genetic algorithm (2013).

  31. Lee, H., Liau, Y., Ryu, K.: Real-time parameter optimization based on neural network for smart injection molding. IOP Conf. Ser: Mater. Sci. Eng. 324, 012076 (2018). https://doi.org/10.1088/1757-899X/324/1/012076

    Article  Google Scholar 

  32. Han, J., Kamber, M.: Data mining: concepts and techniques, 2nd edn. Elsevier, Amsterdam (2006)

    Google Scholar 

  33. Le, T.-T.-H., Kim, H., Kang, H., Kim, H.: Classification and explanation for intrusion detection system based on ensemble trees and SHAP method (2022).

  34. Rodríguez‑Pérez, R., Bajorath, J.: Interpretation of machine learning models using shapley values: application to compound potency and multi‑target activity predictions (2020).

  35. Plevris, V., Solorzano, G., Bakas, N.P.: Investigation of performance metrics in regression analysis and machine learning-based prediction models.

  36. Jierula, A., Wang, S., Oh, T.-M., Wang, P.: Study on accuracy metrics for evaluating the predictions of damage locations in deep piles using artificial neural networks with acoustic emission data (2021).

Download references

Acknowledgements

This research is supported by the Ministry of Higher Education, Scientific Research and Innovation, the Digital Development Agency (DDA), and the National Center for Scientific and Technical Research (CNRST) of Morocco (Smart DLSP Project—AL KHAWARIZMI IA-PROGRAM).

Author information

Authors and Affiliations

  1. Intelligent Automation and Biomed Genomics Laboratory, FST of Tangier, Abdelmalek Essaadi University, Tetouan, Morocco

    Faouzi Tayalati, Abdellah Azmani & Monir Azmani

Authors
  1. Faouzi Tayalati
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdellah Azmani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Monir Azmani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Faouzi Tayalati.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tayalati, F., Azmani, A. & Azmani, M. Application of supervised machine learning methods in injection molding process for initial parameters setting: prediction of the cooling time parameter. Prog Artif Intell (2024). https://doi.org/10.1007/s13748-024-00318-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13748-024-00318-z

Keywords

Search

Navigation