A multi-agent data mining system for defect forecasting in a decentralized manufacturing environment

  1. Cendón, J.A. 1
  2. Marcos, A.G. 2
  3. Limas, M.C. 1
  4. Meré, J.O. 3
  1. 1 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

  2. 2 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  3. 3 Universidad Politécnica de Madrid
    info

    Universidad Politécnica de Madrid

    Madrid, España

    ROR https://ror.org/03n6nwv02

Mostrar afiliaciones +
Libro:
Computational Intelligence in Security for Information Systems 2010

ISBN: 978-3-642-16625-9

Año de publicación: 2010

Volumen: 85

Páginas: 43-50

Tipo: Aportación congreso

DOI: 10.1007/978-3-642-16626-6_5 SCOPUS: 2-s2.0-80053044460 WoS: WOS:000289216200005 GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

This paper reports an experience on setting a multi-agent system to control a complex production environment, a steelmaking manufacturing plant. The decentralized character of such a plant fits perfectly with the approach of a control system by means of a multi-agent configuration. The agents devoted to rendering the superficial and internal defects maps, to developing and maintaining the learning context, to evaluating the coils entering the pickling line and to forecasting the remaining defects on the coil are described. Data mining techniques are used by the agents to gain access to the actual status of the manufacturing process, thus helping in the decision-making processes. This proves to be a great aid in improving the quality of the products and reducing both costs and the environmental footprint of the manufacturing process. The results of using such a system reinforce our belief in the approach presented. © 2010 Springer-Verlag Berlin Heidelberg.