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L. E. Zarate and F. R. Bittencout, “Representation and Control of the Cold Rolling Process through Artificial Neural Networks via Sensitivity Factors,” Elsevier Journal of Materials Processing Technology, 2007, pp. 344362.

has been cited by the following article:

• TITLE: Strip Thickness Control of Cold Rolling Mill with Roll Eccentricity Compensation by Using Fuzzy Neural Network

  AUTHORS: Waleed I. Hameed, Khearia A. Mohamad

  KEYWORDS: Cold Rolling Mill; Thickness Control; Roll Eccentricity; Fuzzy Neural Network; Eccentricity Compensation

  JOURNAL NAME: Engineering, Vol.6 No.1, January 13, 2014

  ABSTRACT: In rolling mill, the accuracy and quality of the strip exit thickness are very important factors. To realize high accuracy in the strip exit thickness, the Automatic Gauge Control (AGC) system is used. Because of roll eccentricity in backup rolls, the exit thickness deviates periodically. In this paper, we design PI controller in outer loop for the strip exit thickness while PD controller is used in inner loop for the work roll actuator position. Also, in order to reduce the periodic thickness deviation, we propose roll eccentricity compensation by using Fuzzy Neural Network with online tuning. Simulink model for the overall system has been implemented using MATLAB/SIMULINK software. The simulation results show the effectiveness of the proposed control.