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

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.