A neural network based approach to optimize rubber extrusion lines

  1. Marcos, A.G. 1
  2. Espinoza, A.V.P. 2
  3. Elas, F.A. 2
  4. García Forcada, A. 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 Metzeler Automotive Profile Systems Iberica, S.A. la Portalada, Barrio de Varea, 26006 - Logroño, La Rioja, Spain
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Revista:
International Journal of Computer Integrated Manufacturing

ISSN: 0951-192X

Año de publicación: 2007

Volumen: 20

Número: 8

Páginas: 828-837

Tipo: Artículo

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DOI: 10.1080/09511920601108808 SCOPUS: 2-s2.0-35448952613 WoS: WOS:000250346000008 GOOGLE SCHOLAR

Otras publicaciones en: International Journal of Computer Integrated Manufacturing

Objetivos de desarrollo sostenible

Proyectos relacionados

CONTROL DE CALIDAD GLOBAL EN TRENES DE FABRICACION DE ELASTOMEROS

2004/00086/001

Resumen

The current study shows how data mining and artificial intelligence techniques can be used to introduce improvements in the rubber extrusion production process. One of the keys for planning manufacturing values is prior knowledge of the properties of the material to be extruded. At present, such information is obtained through laboratory trials performed on samples taken off line after the elastomers have been manufactured, with the subsequent cost and delays. In view of these problems, the present study proposes a neural model capable of predicting the characteristics of rubber from the composition of the mixture and the mixing conditions, without having to wait for laboratory results, thus guaranteeing the traceability of the product in the process and the values according to their specific characteristics and also achieving a reduction in costs deriving from smaller amounts of discarded material during the performance of the tests, etc.