| Peer-Reviewed

Injection Speed Optimization Based on Improved Generalized Predictive Control

Received: 28 October 2022     Accepted: 14 November 2022     Published: 29 November 2022
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Abstract

Injection molding is a typical nonlinear system, in which there is a need for high-precision control of injection velocity to produce sophisticated products. In view of the shortcomings in control precision of existing control systems, this paper proposes an improved generalized predictive control (GPC) model for high-precision injection velocity control. The velocity response curves are studied and corresponding control action coefficients under step disturbance with different velocity constants are determined based on the characteristics of curves. To overcome large overshoot and insufficient accuracy when controlling large delay processes, the softening factor is changed to a dynamic softening factor and the initial value of reference trajectory is determined with a new manner. To verify the performance of the propsed model, extensive simulation and experimental analysis are conducted considering parameters including horizon length, prediction horizon length, control horizon length, control weighting factor and softening coefficient. The resultsreveal that the improved GPC model achieves fairly high accuracy for the control of injection velocity, the errors is controlled within 0.05 cm/s, which can meet the injection precision requirement of actual injection molding machines. Moreover, the model can guarantee the starting and finishing ends of prediction horizon to overcome the over-regulation occurring in high precision control with other algorithms, meanwhile, the model also improves the control response velocity.

Published in Engineering and Applied Sciences (Volume 7, Issue 6)
DOI 10.11648/j.eas.20220706.11
Page(s) 77-84
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Generalized Predictive Control, Nonlinear System, Velocity Control, Control Precision

References
[1] Rabbi M S, Islam T, Islam G M. Injection-molded natural fiber-reinforced polymer composites–a review [J]. International Journal of Mechanical and Materials Engineering, 2021, 16 (1): 1-21.
[2] Froehlich C, Kemmetmüller W, Kugi A. Model-predictive control of servo-pump driven injection molding machines [J]. IEEE transactions on control systems technology, 2019, 28 (5): 1665-1680.
[3] Banka N, Devasia S. Application of iterative machine learning for output tracking with magnetic soft actuators [J]. IEEE/ASME Transactions on Mechatronics, 2018, 23 (5): 2186-2195.
[4] Liao J, Yuan H, Song W, et al. Adaptive robust fault detection and control for injection machine mold closing process with accurate parameter estimations [C]//2021 IEEE International Conference on Mechatronics (ICM). IEEE, 2021: 1-6.
[5] Billah M M, Rabbi M S, Hasan A. Injection molded discontinuous and continuous rattan fiber reinforced polypropylene composite: Development, experimental and analytical investigations [J]. Results in Materials, 2022, 13: 100261.
[6] Lee S, Lim J, Yu J, et al. Engineering tumor vasculature on an injection-molded plastic array 3D culture (IMPACT) platform [J]. Lab on a Chip, 2019, 19 (12): 2071-2080.
[7] Zhao P, Ji K, Zhang J, et al. In-situ ultrasonic measurement of molten polymers during injection molding [J]. Journal of Materials Processing Technology, 2021, 293: 117081.
[8] Khosravani M R, Nasiri S. Injection molding manufacturing process: Review of case-based reasoning applications [J]. Journal of Intelligent Manufacturing, 2020, 31 (4): 847-864.
[9] Kitayama S, Hashimoto S, Takano M, et al. Multi-objective optimization for minimizing weldline and cycle time using variable injection velocity and variable pressure profile in plastic injection molding [J]. The International Journal of Advanced Manufacturing Technology, 2020, 107 (7): 3351-3361.
[10] Hashimoto S, Kitayama S, Takano M, et al. Simultaneous optimization of variable injection velocity profile and process parameters in plastic injection molding for minimizing weldline and cycle time [J]. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2020, 14 (3): JAMDSM0029-JAMDSM0029.
[11] Palutkiewicz P, Trzaskalska M, Bociąga E. The influence of blowing agent addition, talc filler content, and injection velocity on selected properties, surface state, and structure of polypropylene injection molded parts [J]. Cellular Polymers, 2020, 39 (1): 3-30.
[12] Tosello G, Costa F S. High precision validation of micro injection molding process simulations [J]. Journal of Manufacturing Processes, 2019, 48: 236-248.
[13] Farahani S, Khade V, Basu S, et al. A data-driven predictive maintenance framework for injection molding process [J]. Journal of Manufacturing Processes, 2022, 80: 887-897.
[14] Tan K K, Huang S N, Jiang X. Adaptive control of ram velocity for the injection moulding machine [J]. IEEE Transactions on Control Systems Technology, 2001, 9 (4): 663-671.
[15] Gao F, Yang Y, Shao C. Robust iterative learning control with applications to injection molding process [J]. Chemical Engineering Science, 2001, 56 (24): 7025-7034.
[16] S. N. Huang, K. K. Tan and T. H. Lee, "Neural-network-based predictive learning control of ram velocity in injection molding," in IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 34, no. 3, pp. 363-368, Aug. 2004.
[17] Tsai C C, Hsieh S M, Kao H E. Mechatronic design and injection speed control of an ultra high-speed plastic injection molding machine [J]. Mechatronics, 2009, 19 (2): 147-155.
[18] Gordon G, Kazmer D O, Tang X, et al. Quality control using a multivariate injection molding sensor [J]. The International Journal of Advanced Manufacturing Technology, 2015, 78 (9): 1381-1391.
[19] Lughofer E, Pollak R, Zavoianu A C, et al. Self-adaptive evolving forecast models with incremental PLS space updating for on-line prediction of micro-fluidic chip quality [J]. Engineering Applications of Artificial Intelligence, 2018, 68: 131-151.
[20] Errouissi R, Yang J, Chen W H, et al. Robust Nonlinear Generalized Predictive Control for a Class of Uncertain Nonlinear Systems via an Integral Sliding Mode Approach [J]. International Journal of Control, 2016, 89 (8): 1698-1710.
[21] Hui Y, Chi R, Huang B, et al. Extended state observer-based data-driven iterative learning control for permanent magnet linear motor with initial shifts and disturbances [J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 51 (3): 1881-1891.
[22] Wu Z, Li D, Xue Y, et al. Gain scheduling design based on active disturbance rejection control for thermal power plant under full operating conditions [J]. Energy, 2019, 185: 744-762.
[23] Dubay R, Hu B, Hernandez J M, et al. Controlling Process Parameters during Plastication in Plastic Injection Molding Using Model Predictive Control [J]. Advances in Polymer Technology, 2014, 33 (S1).
[24] Stemmler S, Vukovic M, Ay M, et al. Quality control in injection molding based on norm-optimal iterative learning cavity pressure control [J]. IFAC-Papers On Line, 2020, 53 (2): 10380-10387.
Cite This Article
  • APA Style

    Jia Bao, Haiyang Hu. (2022). Injection Speed Optimization Based on Improved Generalized Predictive Control. Engineering and Applied Sciences, 7(6), 77-84. https://doi.org/10.11648/j.eas.20220706.11

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    ACS Style

    Jia Bao; Haiyang Hu. Injection Speed Optimization Based on Improved Generalized Predictive Control. Eng. Appl. Sci. 2022, 7(6), 77-84. doi: 10.11648/j.eas.20220706.11

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    AMA Style

    Jia Bao, Haiyang Hu. Injection Speed Optimization Based on Improved Generalized Predictive Control. Eng Appl Sci. 2022;7(6):77-84. doi: 10.11648/j.eas.20220706.11

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  • @article{10.11648/j.eas.20220706.11,
      author = {Jia Bao and Haiyang Hu},
      title = {Injection Speed Optimization Based on Improved Generalized Predictive Control},
      journal = {Engineering and Applied Sciences},
      volume = {7},
      number = {6},
      pages = {77-84},
      doi = {10.11648/j.eas.20220706.11},
      url = {https://doi.org/10.11648/j.eas.20220706.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eas.20220706.11},
      abstract = {Injection molding is a typical nonlinear system, in which there is a need for high-precision control of injection velocity to produce sophisticated products. In view of the shortcomings in control precision of existing control systems, this paper proposes an improved generalized predictive control (GPC) model for high-precision injection velocity control. The velocity response curves are studied and corresponding control action coefficients under step disturbance with different velocity constants are determined based on the characteristics of curves. To overcome large overshoot and insufficient accuracy when controlling large delay processes, the softening factor is changed to a dynamic softening factor and the initial value of reference trajectory is determined with a new manner. To verify the performance of the propsed model, extensive simulation and experimental analysis are conducted considering parameters including horizon length, prediction horizon length, control horizon length, control weighting factor and softening coefficient. The resultsreveal that the improved GPC model achieves fairly high accuracy for the control of injection velocity, the errors is controlled within 0.05 cm/s, which can meet the injection precision requirement of actual injection molding machines. Moreover, the model can guarantee the starting and finishing ends of prediction horizon to overcome the over-regulation occurring in high precision control with other algorithms, meanwhile, the model also improves the control response velocity.},
     year = {2022}
    }
    

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  • TY  - JOUR
    T1  - Injection Speed Optimization Based on Improved Generalized Predictive Control
    AU  - Jia Bao
    AU  - Haiyang Hu
    Y1  - 2022/11/29
    PY  - 2022
    N1  - https://doi.org/10.11648/j.eas.20220706.11
    DO  - 10.11648/j.eas.20220706.11
    T2  - Engineering and Applied Sciences
    JF  - Engineering and Applied Sciences
    JO  - Engineering and Applied Sciences
    SP  - 77
    EP  - 84
    PB  - Science Publishing Group
    SN  - 2575-1468
    UR  - https://doi.org/10.11648/j.eas.20220706.11
    AB  - Injection molding is a typical nonlinear system, in which there is a need for high-precision control of injection velocity to produce sophisticated products. In view of the shortcomings in control precision of existing control systems, this paper proposes an improved generalized predictive control (GPC) model for high-precision injection velocity control. The velocity response curves are studied and corresponding control action coefficients under step disturbance with different velocity constants are determined based on the characteristics of curves. To overcome large overshoot and insufficient accuracy when controlling large delay processes, the softening factor is changed to a dynamic softening factor and the initial value of reference trajectory is determined with a new manner. To verify the performance of the propsed model, extensive simulation and experimental analysis are conducted considering parameters including horizon length, prediction horizon length, control horizon length, control weighting factor and softening coefficient. The resultsreveal that the improved GPC model achieves fairly high accuracy for the control of injection velocity, the errors is controlled within 0.05 cm/s, which can meet the injection precision requirement of actual injection molding machines. Moreover, the model can guarantee the starting and finishing ends of prediction horizon to overcome the over-regulation occurring in high precision control with other algorithms, meanwhile, the model also improves the control response velocity.
    VL  - 7
    IS  - 6
    ER  - 

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Author Information
  • Science Technology Department of Zhejiang Province, Hangzhou, China

  • College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

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