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Online Prediction of Wear on Rolls of a Bar Rolling Mill Based on Semi-Analytical Equations and Artificial Neural Networks

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 223)

Abstract

This paper presents a computer model for online prediction of the wear contour of grooved rolls in the round-oval-round pass rolling process based on semi-analytical equations and artificial neural networks (ANN). This wear may adversely affect the shape quality of final product and is a result of complex interactions of many variables in the rolling process. The temperature of the material, amount of rolled material, water cooling system efficiency, diameters of the rolls, rolling speed and rolling load are some of these factors that play important role when assessing the wear of the rolls. A first ANN learns the average electrical current for thousands of hot rolled billets, and is done for ideal conditions, with new rolls. A second ANN calculates empirical coefficients in order to define the spread of the workpiece and then its contour is calculated accurately. This second ANN has inputs of differences on ideal and real electrical currents (and, thus, the rolling load variation) generated from the first ANN, temperature, water cooling pressure, speed of the rolls, diameters of the rolls, etc. Then the coefficients \({\varvec{\gamma }}\) and \({\varvec{\kappa }}\) (for wear profile) are calculated and input in semi-analytical equations to define the wear and its contour, as an online prediction. The works of Shinokura and Takai (1984) and Byon and Lee (2007) apply constant values for these two coefficients, limiting its application for other operational data variation during the rolling process. The model presented in this work uses an ANN to adapt \({\varvec{\gamma }}\) and \({\varvec{\kappa }}\) to cope with this variation. More than 50,000 billets were monitored and their operational data collected. The model was tested and the results agree well in real operational situations.

Keywords

Bar rolling Artificial neural networks Wear 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.CEFET-MGFederal Center of Technological Education of Minas GeraisBelo HorizonteBrazil
  2. 2.GERDAUDivinopolis millBrazil

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