Flushing analysis by machine vision and fuzzy logic at molten steel for the automation process

Christian Pfob, Kurt S. Niel, Roman Roessler

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

1 Citation (Scopus)


For the homogenisation of the molten steel it is necessary to rinse the melting bath. Therefore two porous plugs are installed in the bottom of the casting ladle through which the gas is blown into the ladle. The movement of the melting surface is chaotic. Other process stages, which are distortions to the image processing system, like steam or mechanical parts moving within the scene have to be taken into consideration too. Standard straight forward analytic algorithms fail. The uncertainties cannot be handled in a proper way. We decided to use a RGB binary converter followed by a fuzzy classifier. If the flushing is active molten steel breaks through the slag. This molten steel areas show a certain colour spectrum. The RGB-binary conversation is necessary to detect the molten cast breaking the slag. The size of these colour areas is direct proportional to the intensity of the flushing. The fuzzy block felts the results of the binary conversation and splits them into the intensity grades. This method allows the detection of five stages of the flushing under the given conditions at the melting process and it is able to detect steam or other disturbing parts moving through the scene as well.

Original languageEnglish
Title of host publicationMachine Vision Applications in Industrial Inspection XIV - Proceedings of SPIE-IS and T Electronic Imaging
Publication statusPublished - 2006
EventMachine Vision Applications in Industrial Inspection XIV - San Jose, CA, United States
Duration: 16 Jan 200617 Jan 2006

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


ConferenceMachine Vision Applications in Industrial Inspection XIV
Country/TerritoryUnited States
CitySan Jose, CA


  • Fuzzy logic
  • Harsh conditions
  • Industrial environment
  • Machine vision
  • Steel production


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