Method for extracting and classifying sheet variation...

G - Physics – 01 – N

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G01N 37/00 (2006.01) G01N 21/89 (2006.01) G06T 7/00 (2006.01)

Patent

CA 2318196

Multiple two-dimensional variation patterns are extracted from two-dimensional sheet measurement data of sheet material being manufactured with the variation patterns being classified to identify the causes of the extracted patterns. The extracted two-dimensional variation patterns are identified with the elements or components of the process or machine which caused the patterns. The machine components which cause the variation patterns can then be adjusted and/or controlled so that the patterns can be reduced or substantially eliminated in sheets of material produced by the machine. In addition to adjustment and/or control of the process or machine producing the sheet of material, the extracted variation patterns can be used as new representations of sheet quality, process or machine quality and the patterns will provide more in-depth understanding for operators of the machine.

L'invention concerne plusieurs profils d'écart bi-dimensionnels que l'on extrait de données de mesure de feuilles bi-dimensionnelles relatives à un matériau en feuilles en cours de fabrication, les profils d'écart étant classés de manière à repérer l'origine des profils extraits. Les profils d'écart bi-dimensionnels extraits sont repérés en regard d'éléments ou de composants du procédé industriel ou de la machine à l'origine des profils d'écart. Les composants de la machine à l'origine des profils d'écart peuvent alors faire l'objet de reprises ou de commandes de façon à ne plus retrouver, en tout ou partie, les profils dans les feuilles de matériau produites par la machine. Outre la reprise et/ou la commande du procédé industriel ou de la machine produisant les feuilles de matériau, les profils d'écart extraits peuvent être utilisés pour représenter de nouvelles qualités de feuilles, la qualité du procédé industriel ou de la machine, et les profils apportent aux utilisateurs de la machine une meilleure connaissance du processus.

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