Supercritical fluid dosage control using Artificial Neural Network (ANN) for microcellular injection molding

  • Xiaofei Sun*
  • , Lih Sheng Turng
  • , Patrick J. Gorton
  • , Eugene P. Dougherty
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Supercritical fluid (SCF) dosage is one of the most important parameters for microcellular injection molding. Highly accurate and repeatable SCF dosage is required to ensure high part quality and consistency. In this study, an artificial neural network (ANN) based SCF dosage control strategy was proposed to predict and compensate for the possible variations in the coming SCF dosing stage to achieve a more repeatable SCF dosage. The result shows that this control strategy can be successfully implemented, and that it leads to significant improvements in dosage consistency and part quality.

Original languageEnglish
Title of host publication70th Annual Technical Conference of the Society of Plastics Engineers 2012, ANTEC 2012
PublisherSociety of Plastics Engineers
Pages1642-1646
Number of pages5
ISBN (Print)9781622760831
StatePublished - 2012
Externally publishedYes
Event70th Annual Technical Conference of the Society of Plastics Engineers 2012, ANTEC 2012 - Orlando, FL, United States
Duration: 02 04 201204 04 2012

Publication series

NameAnnual Technical Conference - ANTEC, Conference Proceedings
Volume2

Conference

Conference70th Annual Technical Conference of the Society of Plastics Engineers 2012, ANTEC 2012
Country/TerritoryUnited States
CityOrlando, FL
Period02/04/1204/04/12

Keywords

  • Artificial neural network
  • Gas dosage control
  • Microcellular injection molding

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