Conference Paper

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

Conference: ESANN 2009, 17th European Symposium on Artificial Neural Networks, Bruges, Belgium, April 22-24, 2009, Proceedings
Source: DBLP


A new iterative method based on Support Vector Machines to perform automated colour adjustment processing in the automotive in- dustry is proposed in this paper. The iterative methodology relies on a SVM trained with patterns provided by expert colourists and an ac- tions' 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.

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