Improvement of a Setup Accuracy for Hot Strip Finishing Mills Using Clustering Based on Linear Regression and Local Regression

Abstract

Improving the initial setup accuracy of various hot strip mill actuators has been an issue for a long time. This study proposes new methods to enhance the accuracy if setup values based on the just-in-time method that uses a large amount of stored operational data. The key to the proposed methods is to extract the appropriate neighborhood data necessary for the setup calculations. The proposed method combines local regression, neighborhood data extracted via clustering based on linear regression, and the setup value calculated through the conventional method. We confirmed that the proposed method better improves the accuracy compared to the conventional method.