By Topic

Neural networks to improve mathematical model adaptation in the flat steel cold rolling process

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
dos Santos Filho, A.L. ; Ind. Syst. Dept., Sao Paulo Fed. Inst. of Educ., Sci. & Technol. (IF/SP - Cubatao Campus), Cubatão, Brazil ; Ramirez-Fernandez, F.J.

In the flat steel cold rolling process, real-time controllers get their reference values (setpoints) using a mathematical model. Such a model is carried out at the process optimization level of the plant automation architecture. Since not all variables needed by the model can be effectively measured, and since a very accurate modeling would be unsuitable for real-time application or unachievable at all, the mathematical model must have adaptive capabilities, that is, its key parameters must be continuously adjusted based on real process values. This work proposes the application of Artificial Neural Networks to improve the adaptation of two hardly modeled process variables: the material yield stress and the friction coefficient between the work rolls and the strip. The text describes the theoretical foundations, the development methodology and the preliminary results achieved by implementing the proposed system in a real tandem cold mill.

Published in:

Neural Networks (IJCNN), The 2010 International Joint Conference on

Date of Conference:

18-23 July 2010