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Intelligent recommendation system of the injection molding process parameters based on CAE simulation, process window, and machine learning

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Abstract

In this research, a recommendation system was designed for optimizing the injection molding process parameters. The system incorporates the utilization of process windows, eXtreme Gradient Boosting (XGBoost), and genetic algorithms. Computer-aided engineering (CAE) simulations were conducted to generate process window data and simulation data. Automatic hyperparameter optimization of the XGBoost was performed using grid search and cross-validation methods. The system employs 5 injection molding feature parameters as input and one product feature as output, and the strengthen elitist genetic algorithms (SEGA) was used for predicting the optimal injection molding process parameters. The performance of the prediction model was evaluated using an RMSE of 0.0202 and an R2 of 0.9826. The accuracy of the system was verified by conducting real production. The deviation of the product weight obtained from real production from the desired weight is 0.22%, which means that the prediction model achieves a correct rate of 99.78%. This recommendation system has a significant application value in reducing production costs and cycle time, as it can provide initial injection process parameter suggestions solely through the mold’s digital data.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Black JT, Kohser RA (2017) DeGarmo’s materials and processes in manufacturing. John Wiley & Sons

    Google Scholar 

  2. Johannaber F (2016) Injection molding machines: a user’s guide. Carl Hanser Verlag GmbH Co KG

  3. Han SY, Kwag JK, Kim CJ, Park TW, Jeong YD (2004) A new process of gas-assisted injection molding for faster cooling. J Mater Process Technol 155:1201–1206

    Article  Google Scholar 

  4. Dang XP (2014) General frameworks for optimization of plastic injection molding process parameters. Simul Model Pract Theory 41:15–27

    Article  Google Scholar 

  5. Xu YY, Xie PC, Fu NH, Jiao XL, Wang JL, Liu G, Dou XY, Zha Y, Dang KF, Yang WM (2022) Self-optimization of the V/P switchover and packing pressure for online viscosity compensation during injection molding. Polym Eng Sci 62(4):1114–1123

    Article  Google Scholar 

  6. Ma YT, Xu YY, Dang KF, Fu NH, Jiao XL, Xie PC, Yang WM (2022) Study on the evaluation and compensating strategy for the wear damage of non-return valve during injection molding process. Polym Eng Sci 63(3):811–820

    Article  Google Scholar 

  7. Párizs RD, Török D, Ageyeva T, Kovács JG (2022) Machine learning in injection molding: an industry 4.0 method of quality prediction. Sensors 22(7):2704

    Article  Google Scholar 

  8. Wang ZH, Wen FC, Li YT, Tsou HH (2023) A novel sensing feature extraction based on mold temperature and melt pressure for Plastic Injection Molding Quality Assessment. IEEE Sens J 23(7):7451–7459

    Article  Google Scholar 

  9. Silva B, Sousa J, Alenya G (2021) Machine learning methods for quality prediction in thermoplastics injection molding. International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, pp 1–6

  10. Luo L, Yao Y, Gao F, Zhao C (2018) Mixed-effects Gaussian process modeling approach with application in injection molding processes. J Process Control 62:37–43

    Article  Google Scholar 

  11. Zhou J, Turng LS (2007) Process optimization of injection molding using an adaptive surrogate model with Gaussian process approach. Polym Eng Sci 47(5):684–694

    Article  Google Scholar 

  12. Yang D, Lee J, Yoon K, Kim J (2020) A study on the prediction of optimized injection molding condition using artificial neural network (ANN). Trans Mater Process 29(4):218–228

    Google Scholar 

  13. Chen JC, Guo G, Wang WN (2020) 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 110(7):2023–2033

    Article  Google Scholar 

  14. Hashimoto S, Kitayama S, Takano M, Kubo Y, Aiba S (2020) Simultaneous optimization of variable injection velocity profile and process parameters in plastic injection molding for minimizing weldline and cycle time. J Adv Mech Des Syst Manuf 14(3):JAMDSM0029-JAMDSM0029

  15. Yang J, Yu S (2020) Prediction of process parameters of water-assisted injection molding based on inverse radial basis function neural network. Polym Eng Sci 60(12):3159–3169

    Article  Google Scholar 

  16. Mok S, Kwong CK, Lau W (2001) A hybrid neural network and genetic algorithm approach to the determination of initial process parameters for injection moulding. Int J Adv Manuf Technol 18(6):404–409

    Article  Google Scholar 

  17. Yin F, Mao H, Hua L (2011) A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters. Mater Des 32(6):3457–3464

    Article  Google Scholar 

  18. Lee C, Na J, Park K, Yu H, Kim J, Choi K, Park D, Park S, Rho J, Lee S (2020) Development of artificial neural network system to recommend process conditions of injection molding for various geometries. Adv Intell Syst 2(10):2000037

    Article  Google Scholar 

  19. Lockner Y, Christian H (2021) Induced network-based transfer learning in injection molding for process modelling and optimization with artificial neural networks. Int J Adv Manuf Technol 112:3501–3513

    Article  Google Scholar 

  20. Lockner Y, Hopmann C, Zhao W (2022) Transfer learning with artificial neural networks between injection molding processes and different polymer materials. J Manuf Process 73:395–408

    Article  Google Scholar 

  21. Kumar S, Park HS, Lee CM (2020) Data-driven smart control of injection molding process. CIRP J Manuf Sci Technol 31:439–449

    Article  Google Scholar 

  22. Harry DH (1991) Injection molding machine control algorithms. ANTEC 91:383

    Google Scholar 

  23. Min BH (2003) A study on quality monitoring of injection-molded parts. J Mater Process Technol 136(1–3):1–6

    Article  Google Scholar 

  24. Kulkarni S (2017) Robust process development and scientific molding: theory and practice. Carl Hanser Verlag GmbH Co KG

  25. Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp 785–794

  26. Yu T, Zhu H (2020) Hyper-parameter optimization: a review of algorithms and applications arXiv preprint arXiv:2003.05689

  27. Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281–305

  28. Holland JH (1974) Erratum: genetic algorithms and the optimal allocation of trials. SIAM J Comput 3(4):326–326

    Article  MathSciNet  MATH  Google Scholar 

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Funding

This work is supported by the National Key Research and Development Program of China (2020YFB1506102), Beijing Natural Science Foundation (2222069), and the Science and Technology Major Project of Ningbo (2023Z029).

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, 100029, China

    Yitao Ma, Xinming Wang, Kaifang Dang, Yang Zhou, Weimin Yang & Pengcheng Xie

  2. State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing, 100029, China

    Weimin Yang & Pengcheng Xie

  3. Interdisciplinary Research Center for Artificial Intelligence, Beijing University of Chemical Technology, Beijing, 100029, China

    Pengcheng Xie

Authors
  1. Yitao Ma
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  2. Xinming Wang
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  3. Kaifang Dang
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  4. Yang Zhou
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  5. Weimin Yang
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  6. Pengcheng Xie
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Contributions

Yitao Ma was accountable for the study’s conception and the writing of the manuscript. Xinming Wang was responsible for developing and testing the predictive models. Kaifang Dang assisted in the analysis of the experimental data. Yang Zhou was responsible for carrying out the experiments. Weiming Yang aided in the revision of the manuscript. Pengcheng Xie led the study and reviewed the data and manuscript.

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Correspondence to Kaifang Dang or Pengcheng Xie.

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Yitao Ma and Xinming Wang are co-first authors.

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Ma, Y., Wang, X., Dang, K. et al. Intelligent recommendation system of the injection molding process parameters based on CAE simulation, process window, and machine learning. Int J Adv Manuf Technol 128, 4703–4716 (2023). https://doi.org/10.1007/s00170-023-12264-6

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  • DOI: https://doi.org/10.1007/s00170-023-12264-6

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