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

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.