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Intelligent dimensional prediction systems with real-time monitoring sensors for injection molding via statistical regression and artificial neural networks

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Abstract

Dimensional defect is one of critical issues in injection molding. The consistency of processing conditions can affect the dimensional stability of molded parts. In this paper, a systematic approach is proposed to achieve two objectives. The main objective is to predict the diameter of injection molded parts according to real-time processing data. The second objective is to provide on-line insight from process monitoring by utilizing a monitoring system with in-mold sensors. To meet these objectives, artificial neural network (ANN) and multiple linear regression (MLR) were used to build the prediction model that was integrated with the monitoring system. Taguchi experiments were presented in the study for choosing the optimal parameter settings to meet the target diameter of 50 mm. Five controllable parameters in the study include shot size, nozzle temperature, barrel temperature, cooling time, and holding time. With the processing data collected from the sensors embedded in the surface of mold cavity, regression analysis was employed to build and test the relationship between the processing data and the diameter. The real-time variables from the sensor-based monitoring system such as mold temperature and flow rate were selected as the inputs of the predictive model. The feedforward ANN and MLR models were established to predict the outcome based on the data extracted from sensors, with the prediction accuracy of 99.79% and 99.78%, respectively. It would provide a fast measure and good control of dimensional issues in injection molding.

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References

  1. Huang, Z.M., Kim, H.M., Youn, J.R., Song, Y.S.: Injection molding of carbon fiber composite automotive wheel. Fibers Polym. 20(12), 2665–2671 (2019)

    Article  Google Scholar 

  2. Caltagirone, P.E., Ginder, R.S., Ozcan, S., Li, K., Gay, A.M., Stonecash, J., Steirer, K.X., Cousins, D., Kline, S.P., Maxey, A.T., Stebner, A.P.: Substitution of virgin carbon fiber with low-cost recycled fiber in automotive grade injection molding polyamide 66 for equivalent composite mechanical performance with improved sustainability. Compos. B Eng. 221, 109007 (2021)

    Article  Google Scholar 

  3. Guo, G., Finkenstadt, V.L., Nimmagadda, Y.: Mechanical properties and water absorption behavior of injection-molded wood fiber/carbon fiber high-density polyethylene hybrid composites. Adv. Compos. Hybrid Mater. 2(4), 690–700 (2019)

    Article  Google Scholar 

  4. Guo, G., Kethineni, C.: Direct injection molding of hybrid polypropylene/wood-fiber composites reinforced with glass fiber and carbon fiber. Int. J. Adv. Manuf. Technol. 106(1), 201–209 (2020)

    Article  Google Scholar 

  5. Guo, G., Chen, J.C., Gong, G.: Injection molding of polypropylene hybrid composites reinforced with carbon fiber and wood fiber. Polym. Compos. 39(9), 3329–3335 (2018)

    Article  Google Scholar 

  6. Gong, G., Chen, J.C., Guo, G.: Enhancing tensile strength of injection molded fiber reinforced composites using the Taguchi-based six sigma approach. Int. J. Adv. Manuf. Technol. 91(9), 3385–3393 (2017)

    Article  Google Scholar 

  7. Guo, G.: Investigation on surface roughness of injection molded polypropylene parts with 3D optical metrology. Int. J. Interact. Des. Manuf. (IJIDeM) 16(1), 17–23 (2022)

    Article  MathSciNet  Google Scholar 

  8. 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. (IJIDeM) 14(2), 345–357 (2020)

    Article  Google Scholar 

  9. Guo, G., Li, Y., Zhao, X., Rizvi, R.: Tensile and longitudinal shrinkage behaviors of polylactide/wood-fiber composites via direct injection molding. Polym. Compos. 41(11), 4663–4677 (2020)

    Article  Google Scholar 

  10. Syed, S.F., Chen, J.C., Guo, G.: Optimization of tensile strength and shrinkage of talc-filled polypropylene as a packaging material in injection molding. J. Packag. Technol. Res. 4(1), 69–78 (2020)

    Article  Google Scholar 

  11. Zhao, P., Zhang, J., Dong, Z., Huang, J., Zhou, H., Fu, J., Turng, L.S.: Intelligent injection molding on sensing, optimization, and control. Adv. Polym. Technol. 2020, 7023616 (2020). https://doi.org/10.1155/2020/70236163

  12. Yu, S., Zhang, T., Zhang, Y., Huang, Z., Gao, H., Han, W., Turng, L.S., Zhou, H.: Intelligent setting of process parameters for injection molding based on case-based reasoning of molding features. J. Intell. Manuf. 33(1), 77–89 (2022)

    Article  Google Scholar 

  13. Zhang, Y., Mao, T., Huang, Z., Gao, H., Li, D.: A statistical quality monitoring method for plastic injection molding using machine built-in sensors. Int. J. Adv. Manuf. Technol. 85(9), 2483–2494 (2016)

    Article  Google Scholar 

  14. Zhou, X., Zhang, Y., Mao, T., Zhou, H.: Monitoring and dynamic control of quality stability for injection molding process. J. Mater. Process. Technol. 249, 358–366 (2017)

    Article  Google Scholar 

  15. Yang, Y., Yang, B., Zhu, S., Chen, X.: Online quality optimization of the injection molding process via digital image processing and model-free optimization. J. Mater. Process. Technol. 226, 85–98 (2015)

    Article  Google Scholar 

  16. Guo, F., Zhou, X., Liu, J., Zhang, Y., Li, D., Zhou, H.: A reinforcement learning decision model for online process parameters optimization from offline data in injection molding. Appl. Soft Comput. 85, 105828 (2019)

    Article  Google Scholar 

  17. Zhao, P., Zhou, H., He, Y., Cai, K., Fu, J.: A nondestructive online method for monitoring the injection molding process by collecting and analyzing machine running data. Int. J. Adv. Manuf. Technol. 72(5–8), 765–777 (2014)

    Article  Google Scholar 

  18. Peng, Y., Li, H., Turng, L.S.: Development of a rheo-dielectric sensor for online shear stress measurement during the injection molding process. Polym. Eng. Sci. 50(1), 61–68 (2010)

    Article  Google Scholar 

  19. Chen, J.Y., Yang, K.J., Huang, M.S.: Online quality monitoring of molten resin in injection molding. Int. J. Heat Mass Transf. 122, 681–693 (2018)

    Article  Google Scholar 

  20. Kazmer, D.O., Johnston, S.P., Gao, R.X., Fan, Z.: Feasibility analysis of an in-mold multivariate sensor. Int. Polym. Proc. 26(1), 63–72 (2011)

    Article  Google Scholar 

  21. Johnston, S., McCready, C., Hazen, D., VanDerwalker, D., Kazmer, D.: On-line multivariate optimization of injection molding. Polym. Eng. Sci. 55(12), 2743–2750 (2015)

    Article  Google Scholar 

  22. Johnston, S.P., Kazmer, D.O., Gao, R.X.: Online simulation-based process control for injection molding. Polym. Eng. Sci. 49(12), 2482–2491 (2009)

    Article  Google Scholar 

  23. Gordon, G., Kazmer, D.O., Tang, X., Fan, Z., Gao, R.X.: Quality control using a multivariate injection molding sensor. Int. J. Adv. Manuf. Technol. 78(9–12), 1381–1391 (2015)

    Article  Google Scholar 

  24. Karagöz, İ: An effect of mold surface temperature on final product properties in the injection molding of high-density polyethylene materials. Polym. Bull. 78(5), 2627–2644 (2021)

    Article  Google Scholar 

  25. Chen, J.C., Guo, G., Wang, W.N.: 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 (2020)

    Article  Google Scholar 

  26. Li, Y., Chen, J.C., Ali, W.M.: Process optimization and in-mold sensing enabled dimensional prediction for high precision injection molding. Int. J. Interact. Des. Manuf. (IJIDeM) 16, 997–1013 (2021)

    Article  Google Scholar 

  27. RJG eDART Process Control Software v10 Manual. https://rjginc.com/product/edart-software-v10/ Accessed 14 Aug 2022

  28. Taguchi, G., Chowdhury, S., Wu, Y.: Taguchi’s quality engineering handbook. Wiley, New York (2004)

    Book  MATH  Google Scholar 

  29. Zhang, J.Z., Chen, J.C., Kirby, E.D.: Surface roughness optimization in an end-milling operation using the Taguchi design method. J. Mater. Process. Technol. 184(1–3), 233–239 (2007)

    Article  Google Scholar 

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Acknowledgements

The authors are grateful to Winzeler Gear for providing the eDART system and supplying the Delrin 511DP acetal resin used in this study. The authors are also grateful to the financial support from Illinois Manufacturing Excellence Center (IMEC). The corresponding author is grateful to the Caterpillar Fellowship from Bradley University.

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  1. Department of Industrial and Manufacturing Engineering and Technology, Bradley University, Peoria, IL, 61625, USA

    Joseph C. Chen, Gangjian Guo & Yung-Hui Chang

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  1. Joseph C. Chen
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  2. Gangjian Guo
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Correspondence to Gangjian Guo.

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Chen, J.C., Guo, G. & Chang, YH. Intelligent dimensional prediction systems with real-time monitoring sensors for injection molding via statistical regression and artificial neural networks. Int J Interact Des Manuf 17, 1265–1276 (2023). https://doi.org/10.1007/s12008-022-01115-5

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