Soft computing decision support for a steel sheet incremental cold shaping process

Authors:
José Ramón Villar;Javier Sedano;Emilio Corchado;Laura Puigpinós
Affiliations:
University of Oviedo, Gijón Asturias, Spain;Instituto Tecnoló de Castilla y León, Poligono Industrial de Villalonquejar, Burgos, Spain;Computer Science and Automatica Department with the University of Salamanca, Salamanca, Spain;Fundación Privada Ascamm, Avda. Universitat Autònoma, Cerdanyola del Vallés, Spain
Venue:
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Year:
2011

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Abstract

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.