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Development of a Control System for a Hydraulic Injection Molding Machine Using an AFC Controller and Utilization of

Takahiro Shinpuku1,2, Takumi Kobayashi1, Shota Yabui1

  • 1Mechanical Systems Engineering, Tokyo City University, Tokyo 158-8557, Japan.

Polymers
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PubMed
Summary
This summary is machine-generated.

Adaptive feedforward control (AFC) in injection molding uses learning parameters to monitor molten resin states. This method enhances injection velocity tracking and improves molding quality by reflecting material variations.

Keywords:
adaptive feedforward controldata-driven controlhydraulic injection molding machineprocess state indicatorshot-to-shot learning

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Area of Science:

  • Polymer Engineering
  • Manufacturing Process Control
  • Materials Science

Background:

  • Maintaining stable molding quality in hydraulic injection molding is challenging due to unobservable internal resin states and varying hydraulic characteristics.
  • Oil temperature fluctuations further complicate process control by altering hydraulic properties.

Purpose of the Study:

  • To develop an adaptive feedforward control (AFC) framework for hydraulic injection molding machines.
  • To utilize AFC learning parameters as indicators of the molten resin state for improved process control.

Main Methods:

  • Implemented a multi-frequency feedforward controller with parameters updated over repetitive injection cycles.
  • Employed a shot-to-shot compensation mechanism to accumulate and transfer learning across consecutive molding cycles.
  • Conducted experiments using polypropylene with varying viscosities on a hydraulic injection molding machine.

Main Results:

  • Converged AFC learning parameters systematically varied with material viscosity changes and correlated with molded product appearance.
  • Adjusting cylinder temperature for one material resulted in AFC parameters similar to a reference material, yielding comparable products.
  • Demonstrated that AFC learning parameters effectively reflect resin state variations.

Conclusions:

  • The proposed AFC framework successfully improves injection velocity tracking in hydraulic injection molding.
  • AFC learning parameters serve as practical indicators of resin state, enabling alignment of molding conditions.
  • This approach offers a method for enhancing quality control in injection molding processes.