Soft Computing Decision Support for a Steel Sheet Incremental Cold Shaping Process

  • José Ramon Villar
  • Javier Sedano
  • Emilio Corchado
  • Laura Puigpinós
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6936)


It is known that the complexity inherited in most of the new real world problems, for example, the cold rolled steel industrial process, increases as the computer capacity does. Higher performance requirements with a lower amount of data samples are needed due to the costs of generating new instances, specially in those processes where new technologies arise. This study is focused on the analysis and design of a novel decision support system for an incremental steel cold shaping process, where there is a lack of knowledge of which operating conditions are suitable for obtaining high quality results. The most suitable features have been found using a wrapper feature selection method, in which genetic algorithms and neural networks are hybridized. Some facts concerning the enhanced experimentation needed and the improvements in the algorithm are drawn.


Wrapper Feature Selection Genetic Algorithms Neural Networks Support Vector Machines Incremental Cold Shaping 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • José Ramon Villar
    • 1
  • Javier Sedano
    • 2
  • Emilio Corchado
    • 3
  • Laura Puigpinós
    • 4
  1. 1.University of OviedoGijónSpain
  2. 2.Instituto Tecnoló de Castilla y LeónPoligono Industrial de VillalonquejarBurgosSpain
  3. 3.Computer Science and Automatica DepartmentUniversity of SalamancaSalamancaSpain
  4. 4.Fundación Privada AscammCerdanyola del VallésSpain

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