Design strategy for optimal iterative learning control applied on a deep drawing process: Recognising that stamping and deep-drawing operations are repetitive processes—which can learn and improve based on process history

Research output: Contribution to journalJournal articleResearchpeer-review

10 Citations (Scopus)

Abstract

Metal forming processes in general can be characterised as repetitive processes; this work will take advantage of this characteristic by developing an algorithm or control system which transfers process information from part to part, reducing the impact of repetitive uncertainties, e.g. a gradual changes in the material properties. The process is highly non-linear and the system plant is modelled using a non-linear finite element and the gain factors for the iterative learning controller is identified solving a non-linear optimal control problem. The optimal control problem is formulated as a non-linear least square problem where the system response is evaluated using a non-linear finite element model of the process.
Original languageEnglish
JournalInternational Journal of Advanced Manufacturing Technology
Volume88
Issue number1-4
Pages (from-to)3–18
Number of pages16
ISSN0268-3768
DOIs
Publication statusPublished - Jan 2017

Keywords

  • Machine learning
  • Iterative learning control
  • In-process control
  • Feedback control
  • Metal forming
  • Deep drawing
  • Finite element method
  • Process robustness

Cite this