超宽冷轧机带钢板形特征聚类分析

Cluster analysis of strip flatness characteristics for ultra-wide cold rolling mills

为准确掌握超宽冷轧机不同宽度带钢的板形特征,以某2180 mm超宽冷轧机1900 mm宽度带钢实测板形数据为研究对象,借鉴'大数据'的思想,结合数据挖掘领域中聚类分析方法,提出基于网格和密度的板形特征聚类方法,并以此方法对几种典型带钢宽度的大量板形实测数据进行分析,得到不同宽度带钢的板形特征.以分段函数对板形特征进行多项式表达,得到不同宽度带钢的板形特征参数化分析结果.提出的基于网格和密度的板形特征聚类与分析方法,能够快速准确地对大量板形实测数据进行分析,提取出长期生产过程中板形缺陷特征并得到参数化表达,从而为冷连轧机,特别是超宽带钢冷连轧机的辊形改进和控制策略优化提供数据基础.

In order to master the flatness characteristics of strips with different widths for ultra-wide tandem cold rolling mills, taking the sufficient flatness detection data of 1900 mm strips from 2180 mm cold rolling mills as a research object and considering the idea of big data and the cluster analysis method of data mining, this article proposed a cluster algorithm based on density and grid, ap- plied this cluster algorithm to the analysis of flatness detection data under several typical strip widths, and then obtained the flatness characteristics of strips with different widths. A piecewise polynomial function was introduced to describe the strip flatness characteris- tics, and the analysis results of polynomial coefficients for strips with different widths were gotten. The proposed cluster algorithm based on density and grid and the piecewise function analysis method can be applied to analyze plenty of flatness detection data quickly and accurately, and the flatness defect characteristics and parameterized expression can be obtained, which will be a data basis of roll contour improvement and strip flatness control strategy optimization for cold rolling mills, especially ultra-wide cold rolling mills.