A mill set-up model using a multi-output regression tree for a tandem cold mill producing stainless steel

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Abstract

In a tandem cold mill for stainless steel, an optimum reduction rate is necessary for each stand. A conventional mill set-up uses a lookup-table to optimize the rolling schedule. However, to reflecting all the input conditions and manual interventions on a model is difficult. In this paper, we propose a mill set-up model that can efficiently predict the reduction rate for each stand by considering various input conditions. The proposed prediction model has a multi-output tree structure with a smaller time complexity for easy interpretation. The key contribution to the proposed algorithm is variable selection. According to the results of an analysis of the time-complexity, the proposed algorithm is less time consuming and is capable of learning datasets with a large number of variables more efficiently than the single-output CART (classification and regression trees). To evaluate the performance of the proposed algorithm, we applied it to the rolling reduction rate of a tandem cold mill in POSCO. The proposed algorithm achieves a similar level of R-squared in only 18% of the computing time required for an existing single-output CART algorithm.

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Kang, H. S., & Jun, C. H. (2019). A mill set-up model using a multi-output regression tree for a tandem cold mill producing stainless steel. ISIJ International, 59(9), 1582–1590. https://doi.org/10.2355/isijinternational.ISIJINT-2018-770

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