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

Núria Agell Jané, Cecilio Angulo Bahón, Francisco Javier Ruiz Vegas

Producción científica: Contribución a una conferenciaContribución

Resumen

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 colorimetric 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 originalInglés
EstadoPublicada - 22 abr 2009
EventoEuropean Symposium on Artificial Neural Networks, Bruges 2009 -
Duración: 22 abr 200924 abr 2009

Conferencia

ConferenciaEuropean Symposium on Artificial Neural Networks, Bruges 2009
Período22/04/0924/04/09

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