Predicting the rolling force in hot steel rolling mill using an ensemble model
Frayman, Yakov, Rolfe, Bernard, Hodgson, Peter and Webb, G.I 2002, Predicting the rolling force in hot steel rolling mill using an ensemble model, in Artificial intelligence and applications: proceedings of the second IASTED International Conference, ACTA Press, New York, N.Y., pp. 143-148.
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Predicting the rolling force in hot steel rolling mill using an ensemble model
Accurate prediction of the roll separating force is critical to assuring the quality of the final product in steel manufacturing. This paper presents an ensemble model that addresses these concerns. A stacked generalisation approach to ensemble modeling is used with two sets of the ensemble model members, the first set being learnt from the current input-output data of the hot rolling finishing mill, while another uses the available information on the previous coil in addition to the current information. Both sets of ensemble members include linear regression, multilayer perceptron, and k-nearest neighbor algorithms. A competitive selection model (multilayer perceptron) is then used to select the output from one of the ensemble members to be the final output of the ensemble model. The ensemble model created by such a stacked generalization is able to achieve extremely high accuracy in predicting the roll separation force with the average relative accuracy being within 1% of the actual measured roll force.
ISBN
0889863520 9780889863521
Language
eng
Field of Research
091099 Manufacturing Engineering not elsewhere classified
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