Predicting Strip Tearing in Cold Rolling Tandem Mill using Neural Network
الموضوعات :
A. Haghani
1
(Department of Mechanics, Faculty of Engineering,
Shahrekord Branch, Islamic Azad University, Shahrekord, Iran)
A. R. Khoogar
2
(Department of Mechanical Engineering,
Maleke-Ashtar University of Technology, Lavizan, Tehran, Iran)
F. Kumarci
3
(Department of Computers, Faculty of Engineering,
Shahrekord Branch, Islamic Azad University, Shahrekord, Iran)
الکلمات المفتاحية: Neural Network, Strip Tearing, Cold Rolling Tandem Mill, Multi-Layer Perceptron,
ملخص المقالة :
Strip tearing during cold rolling process has always been considered among the main concerns for steel companies. Several works have been done so far regarding the examination of the issue. In this paper, experimental data from cold rolling tandem mill is used for detecting strip tearing. Sensors are placed across the cold rolling tandem mill. They receive information on parameters (such as angular velocity of the rolls, voltage and the electrical current of electrical motors driving rolls, roll gap, and strip tension force between rolls) directly from the cold rolling tandem mill and save as files. The information included two modes: perfect rolling and ruptured rolling. A neural network was designed by means of MATLAB software and, then, trained using the information from files. Finally, the neural network was examined by new data. It was concluded that neural network has high accuracy in distinguishing between perfect and defected rolling.
[1] Roberts, W., “Cold Rolling of Steel”, 1st ed., Marcel Dekker, New York, 1978, Chap. 2.
[2] Bagheripoor, M., Bisadi, H., “Application of artificial neural networks for the prediction of roll force and roll torque in hot strip rolling process”, Journal of Applied Mathematical Modelling, Vol. 37, 2013, pp. 4593-4607.
[3] Ghaisari, J., Jannesari, H., and Vatani, M., “Artificial neural network predictors for mechanical properties of cold rolling products”, Journal of Advances in Engineering Software, Vol. 45, 2012, pp. 91-99.
[4] Chen, J., Chandrashekhara, K., Mahimkar, C., Lekakh, S. N., and Richards, V., “Void closure prediction in cold rolling using finite element analysis and neural network”, Journal of Materials Processing Technology, Vol. 211, 2011, pp. 245-255.
[5] Rath, S., Singh, A., Bhaskar, U., Krishna, B., Santra, B., Rai, D., and Neogi, N., “Artificial neural network modeling for prediction of roll force during plate rolling process”, Journal of Materials and Manufacturing Processes, Vol. 25, 2010, pp. 149-153.
[6] Mohanty, I., Datta, S., and Bhattacharjee, D., “Composition–processing–property correlation of cold-rolled IF steel sheets using neural network”, Journal of Materials and Manufacturing Processes, Vol. 24, 2008, pp. 100-105.
[7] Shahani, A., Setayeshi, S., Nodamaie, S., Asadi, M., and Rezaie, S., “Prediction of influence parameters on the hot rolling process using finite element method and neural network”, Journal of Materials Processing Technology, Vol. 209, 2009, pp. 1920-1935.
[8] Peng, Y., Liu, H., and Du, R., “A neural network-based shape control system for cold rolling operations”, Journal of Materials Processing Technology, Vol. 202, 2008, pp. 54-60.
[9] Gudur, P., Dixit, U., “A neural network-assisted finite element analysis of cold flat rolling”, Journal of Engineering Applications of Artificial Intelligence, Vol. 21, 2008, pp. 43-52.
[10] Kim, H., Mahfouf, M., and Yang, Y., “Modelling of hot strip rolling process using a hybrid neural network approach”, Journal of Materials Processing Technology, Vol. 201, 2008, pp. 101-105.
[11] Xie, H., Jiang, Z., Tieu, A., Liu, X., and Wang, G., “Prediction of rolling force using an adaptive neural network model during cold rolling of thin strip”, International Journal of Modern Physics B, Vol. 22, 2008, pp. 5723-5727.
[12] Liang, X.-G., Jia, T., Jiao, Z.-J., Wang, G.-D., and Liu, X.-H, “Application of Neural Network Based on Bayesian Method to Rolling Force Prediction in Cold Rolling Process”, Journal of Iron and Steel Research, Vol. 10, 2008, pp. 1-16.
[13] Zhang, L., Zhang, L., y., Wang, J., and Ma, F., T., “Prediction of rolling load in hot strip mill by innovations feedback neural networks”, Journal of Iron and Steel Research, Vol. 14, 2007, pp. 42-51.
[14] Yang, J., Che, H., Xu, Y., and Dou, F., “Application of adaptable neural networks for rolling force set-up in optimization of rolling schedules”, Journal of Advances in Neural Networks, Vol. 3973, 2006, pp. 864-869.
[15] Kang, G., W., Liu, H., B., “Surface defects inspection of cold rolled strips based on neural network”, IEEE Machine Learning and Cybernetics Conference, Vol. 8, IEEE, Guangzhou, China, 2005, pp. 5034-5037.
[16] Son, J., S., Lee, D., M., Kim, I., S., and Choi, S., G., “A study on on-line learning neural network for prediction for rolling force in hot-rolling mill”, Journal of Materials Processing Technology, Vol. 164, 2005, pp. 1612-1617.
[17] Lee, D., Lee, Y., “Application of neural-network for improving accuracy of roll-force model in hot-rolling mill”, Journal of Control Engineering Practice, Vol. 10, 2002, pp. 473-478.
[18] Frayman, Y., Wang, L., and Wan, C., “Cold rolling mill thickness control using the cascade-correlation neural network”, Journal of Control and Cybernetics, Vol. 31, 2002, pp. 327-342.
[19] Lin, J., “Prediction of rolling force and deformation in three-dimensional cold rolling by using the finite-element method and a neural network”, International Journal of Advanced Manufacturing Technology, Vol. 20, 2002, pp. 799-806.
[20] Quanfeng, S., Jiangang, Z., “Prediction on five-stand cold rolling mill of rolling force by neural network”, Journal of Iron and Steel, Vol. 2, 2002, pp. 1-8.
[21] Gunasekera, J., Jia, Z., Malas, J., Rabelo, L., “Development of a neural network model for a cold rolling process”, Journal of Engineering Applications of Artificial Intelligence, Vol. 11, 1998, pp. 597-603.
[22] Larkiola, J., Myllykoski, P., Korhonen, A., and Cser, L., “The role of neural networks in the optimisation of rolling processes”, Journal of Materials Processing Technology, Vol. 80, 1998, pp. 16-23.
[23] Korczak, P., Dyja, H., and Łabuda, E., “Using neural network models for predicting mechanical properties after hot plate rolling processes”, Journal of Materials Processing Technology, Vol. 80, 1998, pp. 481-486.
[24] Cho, S., Cho, Y., and Yoon, S., “Reliable roll force prediction in cold mill using multiple neural networks”, Journal of IEEE Transactions on Neural Networks, Vol. 8, 1997, pp. 874-882.
[25] Aistleitner, K., Mattersdorfer, L., Haas, W., and Kugi, A., “Neural network for identification of roll eccentricity in rolling mills”, Journal of materials processing technology, Vol. 60, 1996, pp. 387-392.
[26] Haghani, A., “A Study of Damage Evolution in Cold Rolling Tandem Mill”, MSc. Dissertation, Faculty of Mechanics, Azad University, Khomeinishahr Branch, Isfahan, Iran. 2009.
[27] Cichocki, A., Unbehauen, R., Neural Networks for Optimization and Signal Processing, 2nd ed., JOHN WILEY & SONS, New York, 1995, Chap. 3.
