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Model predictive synchronous control of barrel temperature for injection molding machine based on diagonal recurrent neural networks

  • Yonggang Peng*
  • , Wei Wei
  • , Jun Wang
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

A nonlinear model predictive control (NMPC) based on diagonal recurrent neural network (DRNN) was used to control multisection barrel melt temperatures of an injection molding machine. In this method a DRNN was used to construct a nonlinear predictive model of barrel melt temperatures and genetic algorithm (GA) was used as a rolling optimization tool. Simulations and experimental results show that this method not only guarantees the accuracy of temperature control of barrel melt temperatures but also improves synchronization of barrel temperature control and it improves the consistency of the barrel melt polymer and the quality of the molded parts. Copyright © 2013 Taylor and Francis Group, LLC.
Original languageEnglish
Pages (from-to)24-30
JournalMaterials and Manufacturing Processes
Volume28
Issue number1
DOIs
Publication statusPublished - 2013
Externally publishedYes

Research Keywords

  • Barrel temperature
  • Diagonal recurrent neural networks
  • Model predictive control
  • Synchronous control

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