A comparison of envelope and statistical analyses for bearing diagnosis in hot steel rolling mill lines

2021 ◽  
Author(s):  
Kisan Sarda ◽  
Antonio Acernese ◽  
Luigi Russo ◽  
Mirko Mazzoleni
Keyword(s):  
Rolling Mill ◽  
Statistical Analyses ◽  
Steel Rolling

Experimental Investigation Into Improved Wrapper Control of Hot Strip Steel Rolling Mill Downcoilers

2009 ◽  
Author(s):  
D. Ll. Davies ◽  
J. Watton ◽  
Y. Xue ◽  
G. A. Williams
Keyword(s):  
Rolling Mill ◽  
New Technologies ◽  
Position Control ◽  
Steel Production ◽  
Rolling Process ◽  
Steel Rolling ◽  
Hot Strip ◽  
Accurate Position ◽  
Manufacturing Techniques ◽  
Coiling Process

With increasing international competition in steel production mainly from developing nations, it is important for steel plants to keep up to date with new technologies, and continuously improve on current practices and manufacturing techniques to remain competitive. This paper looks specifically at improvements to the hot rolling mill downcoilers, which is where the strip is coiled at the end of the rolling process. Hydraulic and pneumatic technology is combined to give accurate position control of guide wrappers that aid the initial coiling process. The paper presents an experimental test rig, using an actual wrapper guide, constructed to evaluate the specific design approach.

scholarly journals Non‐linear optimal control for the hot‐steel rolling mill system

2019 ◽  
Vol 1 (3) ◽  
pp. 97-107 ◽  
Author(s):  
Gerasimos Rigatos ◽  
Nikolaos Zervos ◽  
Pierluigi Siano ◽  
Masoud Abbaszadeh ◽  
Patrice Wira
Keyword(s):  
Optimal Control ◽  
Rolling Mill ◽  
Steel Rolling ◽  
Linear Optimal Control ◽  
Non Linear

Control Methods for a Hot Steel Rolling Mill: An Application of Learning Theory and Pattern Recognition

1980 ◽  
Vol 102 (2) ◽  
pp. 118-122 ◽  
Author(s):  
A. A. Desrochers ◽  
G. N. Saridis
Keyword(s):  
Pattern Recognition ◽  
Rolling Mill ◽  
Feedforward Control ◽  
Adaptive Controller ◽  
Force Model ◽  
Control Methods ◽  
Roll Force ◽  
Steel Rolling ◽  
Pattern Recognition Methods ◽  
Roll Gap

This paper presents roll force control methods to be used with the predictive force setup model of the finishing stands in a hot steel rolling mill. Current mill practices achieve a desired strip gauge by using a predictive force model to setup the roll gaps on the finishing stands. At any time before the steel enters the first finishing stand a human operator may modify the roll gap settings if it is felt that under the present conditions the force predicted by the setup model is going to be unacceptable. In this paper, the decision process of the operator is modelled by pattern recognition methods to obtain this extra degree of feedforward control. In addition, feedback control is provided from one steel run to the next by an adaptive controller which uses a linear reinforcement learning scheme to adjust its parameters. Results are presented from actual mill data.

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