Online prediction of wear on rolls of a bar rolling mill based on semianalytical. neural networks

Transcription

1 17th Online World Conference on Soft Computing in Industrial Applications (WSC17) Online prediction of wear on rolls of a bar rolling mill based on semianalytical equations and artificial neural networks Yukio Shigaki 1 and Marcos Antonio da Cunha 2 1 CEFET MG, Federal Center of Technological Education of Minas Gerais, Brazil. (yukio@des.cefetmg.br) 2 GERDAU, Divinopolis mill, Brazil(marcos.cunha@gerdau.com.br)

2 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.

3 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. 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 o C.

4 It can be seen that it is a risky operation!

5 Researchs Oike et al. proposed a wear model dependent on roll load, length of the arc of contact, strip width, thickness reduction, roll diameter, exit strip length and some empirical coefficients. Archard developed a relationship between the amount of material lost by wear and the contact length, force and hardness of the roll s material.

6 Sachs et al. showed that the rolling load is the most important factor when assessing the wear of rolls. Shinokura and Takai studied several types of cross sections for bar rolling and proposed an equation for the spread of the material of the billet inside the channel, as it has direct impact on the profile of the wear of the channels.

7 Lee et al. propose a new analytical model to predict the surface profile of the wear contour of grooved roll in the oval round (or round oval) pass rolling process computed by using a linear interpolation of the radius of curvature of an incoming billet and the radius of roll groove in the roll axis direction.

8 Kim et al. presented a study using neural network to preserve a uniform cross sectional area ofthe output bar production line, considering the wear of the rolls. To predict the profile of wear on all passes in the process of hot rolling, they proposed a modification on the Archard s wear model considering the hardness of the cylinder.

9 Byon and Lee have proposed a semi analytical model which predicts the contour of wear of the grooved rolls for oval section to round section in the hot rolling process. In this model the contours ofthewearisassumedtobea second order polynomial function that is determined by applying a linear interpolation to the radius of curvature of the workpiece at the entrance of the channel of the roll and a weighting function which takes into account the rolling load, length of contact arc with the rolled rod, the hardness of the roll and the tonnage rolled.

10 Byon et al. have shown that the prediction of wear contour is in agreement with those obtained experimentally. Based on experiments, they have proposed a model that predicts the adjustment of the gap when the groove already have a certain amount of wear. In this study the changes that occur in some process variables during bar rolling in a real situationwerenottakenintoaccount.

11 In the article by Dong and Zheng, they studied the influence of alloying materials of the bar on spread, and found values % higher for alloyed bars than in ordinary carbon steel bars. Then a greater spread, have a greater area ofcontact with the roller bar, thus causing a greater wear of the rolls.

12 Some models for determining the wear of rolls use empirical coefficients as one of variables of the process. Thus, the online application of these models with the suggested empirical coefficients by the above mentioned authors during the production process of the mill won t give precise results in the calculation of the wear, since the process parameters changes significantly with time, and these changes alter the speed of the wear of rolls.

13 This work This study aims to analyze and correlate the wear occurred with changes in process parameters such as temperature of the workpiece, roll s speed, pressure and flow rate of cooling, rolling load, the hardness of the material, the mean temperature of cooling water and rolled tonnage, by two artificial neural networks (ANN).

14 We developed two ANN Multilayer Perceptron (MLP) with two layers being the first with 8 inputs with 5 neurons in the hidden layer and one neuron in the output layer. The second ANN with 5 inputs with 5 neurons in the hidden layer and one neuron in the output layer. 12,659 billets were used for training and 5820 billets for model validation.

15 The first ANN learns the average electrical current for thousands of hot rolled billets, and is done for ideal conditions, with new rolls. The second ANN calculates empirical coefficients in order to define the spread of the workpiece and then its contour is calculated accurately.

16 The works of Shinokura and Takai, and Byon and Lee apply constant values for empirical coefficients, limiting its application for other operational data variation during the rolling process. The model presented in this work uses an ANN to adapt them to deal with this variation. More than 50,000 billets were monitored and their operational variables collected.

17 Semi analytical models for spread and wear

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19 Experimental data The wear contour was measured for 50,000 billets rolled in 2 stands tandem rolling mills. Each reading was done during the maintenance intervals with a dial indicator, and all operational parameters were collected.

20 Results Figure 5 shows a comparison between the model trained and new real data for stand number 11 and 9422 workpieces.

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22 Conclusions This work has developed ANN and semi analytical models in order to calculate the wear and its profile in the grooves for rolls of bar hot rolling mills. Preliminary results compared with the prediction of the model for new data has shown that it works very well. A comparison of this model with two other methods in Table 1 shows a better precision. In the near future this model will be prepared to automatically adjust the roll gap, and the human intervention, very risky as shown above, should not be necessary anymore.

23 References Oike, Y. Okubo, I., Hirano, H., Umeda, K.: Tetsu to Hagane 63, S222 (in Japanese) (1977). Archard, J.F., Contacts and rubbing of flat surfaces. J. Appl. Phys. 24, (1953). Shinokura, T. and Takai, K.A.: A new method for calculating spread in rod rolling [J], Journal of Applied Metalworking,2, p.94, (1982). Lee, Y., Choi, S., Kim, Y.H.: Mathematical model and experimental validation of surface profile of workpiece in round oval round pass sequence, Journal of Materials processing Technology. 108, pp , (2000). Kim, D. H., Kim, B. M., Lee,Y.: Application of ANN for the dimensional accuracy of workpiece in hot rod rolling process. Journal of Materials Processing Technology, Volumes , , (2002).

24 Kim, D. H., Kim, B. M., & Lee,Y.: Adjustment of Roll Gap for the Dimension Accuracy of Bar in Hot Bar Rolling Process. International Journal of the KSPE, 4, No. 1, 56 62, (2003). Byon, S. M. and Lee, Y.: Experimental and semi analytical study of wear contour of roll groove and its applications to rod mill. ISIJ International, Vol. 47, No 47, pp , (2007). Byon, S.M. and Lee,Y.: A study of roll gap adjustment due to roll wear in groove rolling: experiment and modeling. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture vol. 222, No. 7, (2008). Byon, S.M. and Lee,Y.: Experimental study for roll gap adjustment due to roll wear in single stand rolling and multi stand rolling test. Journal of Mechanical Science and Technology, No. 22, , (2008). Zheng, J. and Dong, Y.: Mechanic Automation and Control Engineering (MACE), International Conference on, 26 28, (2010). Sydenham, P. H.; Thorn, R.: Handbook of Measuring System Design. John Wiley & Sons, (2005). Kewalramani, M. A.; Gupta, R.: Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks. Automation in Construction , (2006).