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Application of Artificial Neural Network for Flow Stress Modelling of Steel
American Journal of Neural Networks and Applications
Volume 3, Issue 3, June 2017, Pages: 36-39
Received: Oct. 27, 2017; Accepted: Nov. 20, 2017; Published: Dec. 14, 2017
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Sushant Rath, Flat Rolling Group, R&D Centre for Iron & Steel, Steel Authority of India Linited, Ranchi, India
Pinaki Talukdar, Forge Technology Department, National Institute of Foundry & Forge Technology, Ranchi, India
Arujun Prasad Singh, Department of Mechanical Engg, Maharishi Markandeshwar University, Mullana, Ambala, India
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The hot rolling mills of steel plants are in the process of transformation from manual operation to artificial intelligence (AI) based automatic operations. Most of the mill input parameters required by the automation system are recorded from different sensors installed in the mill except the flow stress of rolled material. Generally a semi-empirical equation is used that correlate flow stress with strain, strain rate and temperature during rolling. The coefficients and exponents of the empirical equations are calculated from experimental data with parameter estimation techniques. This paper discusses the application of artificial neural network (ANN) for calculation of flow stress of material from experimental data. Experiments were conducted in a dynamic thermo-mechanical simulator to measure flow stress of steel at different strain, strain rate and temperature. The experimental data was used to calculate coefficients of empirical equations using multivariable optimization techniques. The data was also used to formulate an ANN model using feed forward network. The ANN model was trained with backpropagation algorithm. The ANN method is found to be more accurate than the semi-empirical equations for correlating the flow stress with strain, strain rate and temperature.
Flow Stress, Steel, Artificial Neural Network, Semi-Empirical Equations
To cite this article
Sushant Rath, Pinaki Talukdar, Arujun Prasad Singh, Application of Artificial Neural Network for Flow Stress Modelling of Steel, American Journal of Neural Networks and Applications. Vol. 3, No. 3, 2017, pp. 36-39. doi: 10.11648/j.ajnna.20170303.12
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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