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April 18, 2026Journal of Process ControlOpen Access

Part-mass control in injection molding of recycled thermoplastics by learning-enabled model predictive cavity-pressure control

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Authors

JAJ. AhlersRGRobert GöllingerMMMoritz Mascher

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Overview

Demonstrates automated control of cavity pressure and part-mass in recycled thermoplastics, suggesting improved quality consistency.

Key Points

  • The aim is to enhance the consistency of part quality in injection molding of recycled thermoplastics through advanced control methods.
  • Developed a learning-enabled nonlinear model predictive controller (NMPC) for cavity pressure.
  • Updated model parameters after each injection molding cycle using sequential quadratic programming.
  • Incorporated a Gaussian process regression model in the part-mass controller to adapt to varying material properties.
  • Tested the control algorithm on plate-mold geometry with both virgin polypropylene and multiple PCR batches.
  • Maintained a mean part-mass deviation of 0.21% relative to the part-mass reference during transitions.
  • Successfully automated the process-model adaptation between virgin material and PCR batches over 50 production cycles.
  • Demonstrated strong potential for maintaining consistent quality in recycled plastic processing.

Cite This Study

Ahlers et al. (2026) studied this question.

synapsesocial.com/papers/69e3215140886becb65407bahttps://doi.org/10.1016/j.jprocont.2026.103725
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