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

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

Producció científica: Contribució a una conferènciaContribució

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 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 originalAnglès
Estat de la publicacióPublicada - 22 d’abr. 2009
EsdevenimentEuropean Symposium on Artificial Neural Networks, Bruges 2009 -
Durada: 22 d’abr. 200924 d’abr. 2009

Conferència

ConferènciaEuropean Symposium on Artificial Neural Networks, Bruges 2009
Període22/04/0924/04/09

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