Neural networks-Their applications and perspectives in intelligent machining

D. Barschdorff, L. Monostori

Research output: Contribution to journalArticle

32 Citations (Scopus)

Abstract

In intelligent manufacturing systems, unprecedented und unforeseen situations are expected to be solved, within certain limits, even on the basis of incomplete and inprecise information One has tried to achieve this goal since years. Gradually, it seems to be realizable through partial solutions, integrated in today's flexible manufacturing systems. These complexes are fairly complicated material and data processing systems, in which different sensors and actuators are thoroughly distributed. The most important requirements for the intelligent techniques to be applied in these systems are the abilities for integration of multiple sensor information, for real-time functioning, for effective knowledge representation and for learning or adaptivity. Artificial neural networks seem to be able to fulfil some of these requirements and to contribute to the implementation of partial solutions, which can lead to the realization of truly intelligent manufacturing systems. The paper gives a summary of known neural networks applications and perspectives in intelligent manufacturing. Special emphasis is given on intelligent machining, namely on the following fields: multisensor fusion and integration, learning of process models, adaptive control, monitoring, diagnostics and quality control. For the sake of perspicuity a short survey of different artificial neural network structures and learning algorithms is also given, together with frequent applications of neural network techniques in fields different from intelligent manufacturing.

Original languageEnglish
Pages (from-to)101-119
Number of pages19
JournalComputers in Industry
Volume17
Issue number2-3
DOIs
Publication statusPublished - Nov 1991

Keywords

  • Control
  • Diagnostics
  • Digital signal processing
  • Expert systems
  • Intelligent machining
  • Machine tools
  • Manufacturing processes
  • Monitoring
  • Neural networks
  • Pattern recognition

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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