SVM-based learning method for improving colour adjustment in automotive basecoat manufacturing

Francisco J. Ruiz, Núria Agell, Cecilio Angulo

Producció científica: Capítol de llibreContribució a congrés/conferènciaAvaluat per experts

1 Citació (Scopus)

Resum

A new iterative method based on Support Vector Machines to perform automated colour adjustment processing in the automotive industry is proposed in this paper. The iterative methodology relies on a SVM trained with patterns provided by expert colourists and an actions' generator module. The SVM algorithm enables selecting the most adequate action in each step of an iterated feed-forward loop until the final state satisfies colourimetric bounding conditions. Both encouraging results obtained and the significant reduction of non-conformance costs, justify further industrial efforts to develop an automated software tool in this and similar industrial processes.

Idioma originalAnglès
Títol de la publicacióESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning
Pàgines343-348
Nombre de pàgines6
Estat de la publicacióPublicada - 2009
Publicat externament
Esdeveniment17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2009 - Bruges, Belgium
Durada: 22 d’abr. 200924 d’abr. 2009

Sèrie de publicacions

NomESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning

Conferència

Conferència17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2009
País/TerritoriBelgium
CiutatBruges
Període22/04/0924/04/09

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