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

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

Research output: Conference paperContribution

Abstract

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.
Original languageEnglish
Publication statusPublished - 22 Apr 2009
EventEuropean Symposium on Artificial Neural Networks, Bruges 2009 -
Duration: 22 Apr 200924 Apr 2009

Conference

ConferenceEuropean Symposium on Artificial Neural Networks, Bruges 2009
Period22/04/0924/04/09

Fingerprint

Dive into the research topics of 'SVM-based learning method for improving colour adjustment in automotive basecoat manufacturing'. Together they form a unique fingerprint.

Cite this