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

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 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.

Año de publicación:

2009

Keywords:

    Fuente:

    scopusscopus

    Tipo de documento:

    Conference Object

    Estado:

    Acceso restringido

    Áreas de conocimiento:

    • Ingeniería de manufactura
    • Aprendizaje automático

    Áreas temáticas:

    • Mobiliario y talleres domésticos

    Contribuidores:

    Cecilio Angulo BahónCecilio Angulo Bahón
    Agell N.Agell N.
    Ruiz F.J.Ruiz F.J.