Hybrid, al- and simulation-supported optimisation of process chains and production plants

L. Monostori, Zs J. Viharos

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

The paper describes a novel approach for generating multipurpose models of machining operations, combining machine learning and search techniques. A block-oriented framework for modelling and optimisation of process chains is introduced and its applicability is shown by the results of the optimisation of cutting processes. The paper illustrates how the framework can support the simulation-based optimisation of whole production plants. The benefits of substituting the time-consuming simulation by ANN models are also outlined. The applicability of the proposed solution is demonstrated by the results of an industrial project where the task was to optimise the size spectrum of the ordered raw material at a plant producing one- and multi-layered printed wires.

Original languageEnglish
Pages (from-to)353-356
Number of pages4
JournalCIRP Annals - Manufacturing Technology
Volume50
Issue number1
Publication statusPublished - 2001

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Learning systems
Raw materials
Machining
Wire

Keywords

  • Artificial intelligence
  • Manufacturing systems
  • Optimisation

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

Hybrid, al- and simulation-supported optimisation of process chains and production plants. / Monostori, L.; Viharos, Zs J.

In: CIRP Annals - Manufacturing Technology, Vol. 50, No. 1, 2001, p. 353-356.

Research output: Contribution to journalArticle

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