Improved pattern clustering algorithm for recognizing transversal distribution of steel strip thickness

Abstract

Transversal distribution of the steel strip thickness in the entry section of the cold rolling mill seriously affects to the flatness and transversal thickness precision of the final products. Pattern clustering method is introduced into the steel rolling field and used in the patterns recognition of transversal distribution of the steel strip thickness. The well-known k-means clustering algorithm has the advantage of being easily completed, but still has some drawbacks. An improved k-means clustering algorithm is presented, and the main improvements include: (1) the initial clustering points are preselected according to the density queue of data objects; and (2) Mahalanobis distance is applied instead of Euclidean distance in the actual application. Compared to the patterns obtained from the common k-means algorithm, the patterns identified by the improved algorithm show that the improved clustering algorithm is well suitable for the patterns recognition of transversal distribution of steel strip thickness and it will be useful in online quality control system.