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.

Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System

“…This came as a surprise to us since the decision tree algorithm is often recommended to be used in industrial settings, since it is easy to use and understand and is also claimed to perform tolerably well. [39,40] The explanatory properties of decision trees are useful in an applied setting, so again, it is needed to explain classifications and predictions, and we aim to pursue this in subsequent work.…”