The inferential monitoring of the screw disturbance torque to predict process fluctuations in polymer extrusion

C. Abeykoona, Marion McAfee, Kang Li, Peter Martin, A. Kelly

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Polymer extrusion is one of the major methods of processing polymer materials and advanced process monitoring is important to ensure good product quality. However, commonly used process monitoring devices, e.g. temperature and pressure sensors, are limited in providing information on process dynamics inside an extruder barrel. Screw load torque dynamics, which may occur due to changes in solids conveying, melting, mixing, melt conveying, etc., are believed to be a useful indicator of process fluctuations inside the extruder barrel. However, practical measurement of the screw load torque is difficult to achieve. In this work, inferential monitoring of the screw load torque signal in an extruder was shown to be possible by monitoring the motor current (armature and/or field) and simulation studies were used to check the accuracy of the proposed method. The ability of this signal to aid identification and diagnosis of process issues was explored through an experimental investigation. Power spectral density and wavelet frequency analysis were implemented together with a covariance analysis. It was shown that the torque signal is dominated by the solid friction in the extruder and hence it did not correlate well with melting fluctuations. However, it is useful for online identification of solids conveying issues.
Original languageEnglish
Pages (from-to)1907-1918
Number of pages12
JournalJournal of Materials Processing Technology
Volume11
Issue number12
DOIs
Publication statusPublished - Dec 2011

ASJC Scopus subject areas

  • Computer Science Applications
  • Modelling and Simulation
  • Ceramics and Composites
  • Metals and Alloys
  • Industrial and Manufacturing Engineering

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