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Abstract

In order to meet the challenge of steady growing pressure to improve product quality, rolling mills employ extensive automation and sophisticated on-line data sampling techniques. However, the steel sheet quality is influenced by the entire ‘life history’ of the rolled strip.

Application of the self-organizing maps helps to discover hidden dependencies influencing the quality parameters, such as flatness, profile, wedge, thickness and width deviations and surface quality, as well as mechanical and magnetic properties. A dataset collected from the measured parameters in a strip location corresponds to a proper process state, described by a point in the state space. Time series of points corresponding to each sampled data set show how the system ‘moves’ in the state space and how the predicted product quality is changing within the strip.

Keywords

rolling data mining self-organizing maps OLAP on-line control

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Fine-tuning of the rolling mill using an Internet-based on-line application-processing interface

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