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

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 perfor...

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Bibliographic Details
Published in:Computers in industry 1997-08, Vol.33 (1), p.61-70
Main Authors: Mezgár, I., Egresits, Cs, Monostori, L.
Format: Article
Language:English
Subjects:
AGVS
Artificial neural networks
Design of experiments
Mathematical models
Neural networks
Processing speed
Quality control
Real time
Reconfiguration
Simulation
Studies
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Summary: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.
ISSN:0166-3615
1872-6194
DOI:10.1016/S0166-3615(97)00011-0