Control Model for an Elastomer Extrusion Process Obtained via a Comparative Analysis of Data Mining and Artificial Intelligence Techniques

  1. Martínez-de-Pisón, F.J. 1
  2. Pernía, A.V. 1
  3. Blanco, J. 1
  4. González, A. 1
  5. Lostado, R. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Revue:
Polymer - Plastics Technology and Engineering

ISSN: 0360-2559

Année de publication: 2010

Volumen: 49

Número: 8

Pages: 779-790

Type: Article

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DOI: 10.1080/03602551003749585 SCOPUS: 2-s2.0-77954270726 WoS: WOS:000284640500004 GOOGLE SCHOLAR

D'autres publications dans: Polymer - Plastics Technology and Engineering

Objectifs de Développement Durable

Résumé

Some elastomer profile extrusion processes in the automotive industry are still hard to control, generally because they are open loop systems with continual changes in manufacturing conditions. It is at the start-up stage that most time and raw materials are lost. This article describes the development of a dynamic extruder velocity control model that is capable of learning from good start-ups in earlier in manufacturing processes. The process of creating the model focuses on selecting the best technique from a set of data mining (DM) and artificial intelligence (AI) algorithms, which are put to the test with a database containing historical data on the start-up processes that have reached the steady state most quickly in the past. With the new models obtained, the process can be automated, and the time required for start-up in profile manufacturing can be reduced. This will result in increased output, higher quality, less faulty material and lower stress levels among production workers. © Taylor & Francis Group, LLC.