Study and Application on Internal Quality Model Based on Intelligence Methods in Continuous Casting Billet

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In continuous casting, it is very important to predict and detect the internal defects of billet in time for ensuring continuous production, improving product quality and reducing production costs. Clustering analysis (CA) method was adopted to do feature extraction and classification for on-site data, by which ladder parameter tables of processing parameters and defect grades of internal quality were got. Fault tree analysis (FTA) method was adopted to analyze the effects of processing parameters on internal defects. Then in the optimization of secondary cooling, the internal quality models were used as the objective function, and adapted cooperative optimization algorithm based on dynamic penalty factors was adopted. The dynamic control of secondary cooling has been realized based on the online heat transfer and solidification model, which provides they are suitable for industry application.