Neural Computing and Applications

, Volume 19, Issue 5, pp 741–754 | Cite as

A comparison of several neural networks to predict the execution times in injection molding production for automotive industry

  • M. Fernández-DelgadoEmail author
  • M. Reboreda
  • E. Cernadas
  • S. Barro
Original Article


In the industrial environment, specifically in the automotive industry, an accurate prediction of execution times for each production task is very useful in order to plan the work and to optimize the human, technical and material resources. In this paper, we applied several regression neural networks to predict the execution times of the tasks in the production of parts for plastic injection molds. These molds are used to make a variety of car components in automotive industry. The prediction is based on the geometric features of the mold parts to be made. The accuracy of the predicted times is high enough to be used as a tool for the design stage of the mold parts, e.g. guiding the design process in order to get the lowest production time.


Automotive industry Plastic injection mold Support vector regression Radial basis function Multi-layer perceptron Generalized regression neural networks Cascade correlation K-nearest neighbors Generalized ART 



This work was supported by the Spanish Ministry of Education and Science (MEC) and the European Regional Development Fund of the European Commission (FEDER) under project TIN2006-15460-C04-02, and by the Xunta de Galicia under project 08MMA010402PR.


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Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • M. Fernández-Delgado
    • 1
    Email author
  • M. Reboreda
    • 2
  • E. Cernadas
    • 1
  • S. Barro
    • 1
  1. 1.Intelligent Systems Group, Department of Electronics and Computer ScienceUniversity of Santiago de CompostelaSantiago de CompostelaSpain
  2. 2.Troqueles y Moldes de Galicia S.A. (TROMOSA)Santiago de CompostelaSpain

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