SVM-based learning method for improving colour adjustement in automotive basecoat manufacturing
Visualitza/Obre
Tipus de documentComunicació de congrés
Data publicació2009
Editord-side
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
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.
CitacióRuiz, F.; Nuria, A.; Angulo, C. SVM-based learning method for improving colour adjustement in automotive basecoat manufacturing. A: 17th European Symposium on Artificial Neural Networks. "17th European Symposium on Artificial Neural Networks". Bruges: d-side, 2009, p. 343-348.
ISBN2-930307-09-9
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