Neural network for identification of roll eccentricity in rolling mills

被引：14

作者：

Aistleitner, K ^{[1
]}

Mattersdorfer, LG ^{[1
]}

Haas, W ^{[1
]}

Kugi, A ^{[1
]}

机构：

[1] JOHANNES KEPLER UNIV,INST AUTOMAT CONTROL,A-4040 LINZ,AUHOF,AUSTRIA

来源：

JOURNAL OF MATERIALS PROCESSING TECHNOLOGY | 1996年 / 60卷 / 1-4期

关键词：

rolling mills; roll eccentricity; thickness control;

D O I：

10.1016/0924-0136(96)02359-X

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The roll eccentricity in a rolling mill may define the limit of achievable thickness tolerances and thus is subject of interest for the automation equipment in hot rolling mills as well as in cold rolling mills. Today's demand on thickness tolerances less than 0.8% require efficient methods for roll eccentricity identification and compensation. This paper should present a solution for identifying roll eccentricity by using a neural network with a comparison to other methods in order to show the advantages and disadvantages for further use in a roll eccentricity compensation. The solution is verified on measured data sets of a cold rolling mill.

引用

页码：387 / 392

页数：6