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

N. Agell; Cecilio Angulo Bahón; Francisco Javier Ruiz Vegas

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

N. Agell
, Cecilio Angulo Bahón
, Francisco Javier Ruiz Vegas

Esade

Research output: Conference paper › Contribution

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 language	English
Publication status	Published - 22 Apr 2009
Event	European Symposium on Artificial Neural Networks, Bruges 2009 - Duration: 22 Apr 2009 → 24 Apr 2009

Conference

Conference	European Symposium on Artificial Neural Networks, Bruges 2009
Period	22/04/09 → 24/04/09

Cite this

@conference{e05d1a4b1895472d9e34c8bddafd97be,

title = "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 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.",

author = "N. Agell and \{Angulo Bah{\'o}n\}, Cecilio and \{Ruiz Vegas\}, \{Francisco Javier\}",

year = "2009",

month = apr,

day = "22",

language = "English",

note = "European Symposium on Artificial Neural Networks, Bruges 2009 ; Conference date: 22-04-2009 Through 24-04-2009",

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TY - CONF

T1 - SVM-based learning method for improving colour adjustment in automotive basecoat manufacturing

AU - Agell, N.

AU - Angulo Bahón, Cecilio

AU - Ruiz Vegas, Francisco Javier

PY - 2009/4/22

Y1 - 2009/4/22

N2 - 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.

AB - 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.

M3 - Contribution

T2 - European Symposium on Artificial Neural Networks, Bruges 2009

Y2 - 22 April 2009 through 24 April 2009

ER -

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

Abstract

Conference

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