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

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.