Design and real-time reconfiguration of robust manufacturing systems by using design of experiments and artificial neural networks

István Mezgár, Csaba Egresits, László Monostori

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

A methodology for design and real-time reconfiguration of robust manufacturing systems is described which combines design of experimental technology, Taguchi method and knowledge-based simulation techniques. Artificial neural networks are proposed for mapping between design factors and system performance. The applicability of the approach is analysed through experiments for the estimation of the throughput time, and the determination of Automated Guided Vehicle (AGV) speed in a given system. In contrast to the simulation-based approach, the solution using artificial neural networks can also be used in real-time circumstances.

Original languageEnglish
Pages (from-to)61-70
Number of pages10
JournalComputers in Industry
Volume33
Issue number1
Publication statusPublished - Aug 1 1997

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Keywords

  • Artificial neural networks
  • Design of experiments
  • Quality control
  • Reconfiguration
  • Simulation

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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