Data Mining Methods in Hot Steel Rolling for Scale Defect Prediction

J.J. Haapamäki, S.M. Tamminen, and J.J. Röning (Finland)


data mining, neural networks, hot steel rolling, scale defects


Scale defects are common surface defects in hot steel rolling. The reasons for such defects are not straightforward. With data mining methods, the multidimensional dependencies between process variables and product composition can be discovered. For this research, a high-dimensional data set from Rautaruukki Oyj, Raahe, Finland was gathered. The data contained both averaged values and process values measured with different frequencies. The synchronisation of the variables as well as the allocation of the measurements on the steel strip were solved before the modelling phase. The research enabled the visualisation of the rolling process and scale defect modelling. Self organizing maps (SOM) were used for these tasks.

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