A data-driven manufacturing support system for rubber extrusion lines

  1. Cabrera, C.B. 2
  2. Ordieres Meré, J.B. 3
  3. Castejon Limas, M. 4
  4. Coz Díaz, J.J. del 1
  1. 1 Universidad de Oviedo

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

  2. 2 Departamento de Ingeniería Industrial, Instituto Tecnológico de Zacatepec, Zacatepec, Mexico
  3. 3 Universidad Politécnica de Madrid

    Universidad Politécnica de Madrid

    Madrid, España

    ROR https://ror.org/03n6nwv02

  4. 4 Universidad de León

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

International Journal of Production Research

ISSN: 0020-7543

Year of publication: 2010

Volume: 48

Issue: 8

Pages: 2219-2231

Type: Article

DOI: 10.1080/00207540902798780 SCOPUS: 2-s2.0-77951112796 GOOGLE SCHOLAR lock_openRiuNet editor

More publications in: International Journal of Production Research


A better control of extrusion processes offers clear advantages in the manufacturing of rubber profiles for the automotive industry. This work reports our experience in developing a support system aimed to ease the work of the extruder machinist while improving the quality of the profiles obtained. In order to build the system, an approach based on facts was adopted, following ISO 9000 standard quality principles. The data warehouse service available provided a wealth of information on the conditions of the running processes. The collected data, after being analysed with the appropriate data-mining techniques, allowed us to gain a better understanding of the process and to identify the main causes of variance. In particular, principal components analysis, Sammon projection and several classification techniques were applied for exploratory purposes. Different behaviours could be described for the extrusion process, allowing for the definition of a control strategy and, eventually, the development of a manufacturing support system. The estimates displayed by the system greatly improve the responsiveness of the machinist when the process departs from expected behaviour. The results of using this system in a local factory proved highly satisfactory and encouraging. © 2010 Taylor & Francis.