Online Prediction of Wear on Rolls of a Bar Rolling Mill Based on Semi-Analytical Equations and Artificial Neural Networ
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
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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 c and j (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 c and j 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.
Y. Shigaki (&) CEFET-MG, Federal Center of Technological Education of Minas Gerais, Belo Horizonte, Brazil e-mail: [email protected] M. A. Cunha GERDAU, Divinopolis mill, Brazil e-mail: [email protected]
V. Snášel et al. (eds.), Soft Computing in Industrial Applications, Advances in Intelligent Systems and Computing 223, DOI: 10.1007/978-3-319-00930-8_22, Springer International Publishing Switzerland 2014
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Y. Shigaki and M. A. Cunha
1 Introduction A hot rod rolling process has several rolling mills in tandem, and each mill has grooved rolls that changes and reduces the cross sectional area of the billet into rod until its final form. A round-oval-round pass rolling means that the billet is formed into a round cross section, and next is rolled to an oval form, and then into a round cross section, and so on. Tons of rods are produced by this process and the surface and dimensional quality are deteriorated with the wear of the grooves of the rolls (Fig. 1). It is not a very easy task to determine exactly when the amount of roll’s wear seems to affect adversely on the rod’s quality, and today its verification is done manually on the rod being rolled at high temperature, around 1000 C. It can be seen that it is a risky operation. When this problem is detected, the process is stopped an
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