Hybrid Hot Strip Rolling Force Prediction using a Bayesian Trained Artificial Neural Network and Analytical Models

A new solution Karman’s equation with the Mises plasticity condition is proposed for determining contact stresses in the slip zones for hot strip rolling. Replacement of the precise plasticity condition by an approximate condition in terms of primary stress leads to a substantial decrease in the length of slip zones and to increase of the rolling force. It was shown that, even at high frictional coefficients, the length of slip zones forms a significant part of the length of deformation region. On the basis of the obtained solutions the techniques for plotting curves of the normal contact stresses, determining the length of the slip zones, the neutral position of the cross section and rolling force refined.

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Application of Mind Evolutionary Algorithm and Artificial Neural Networks for Prediction of Profile and Flatness in Hot Strip Rolling Process

Neural Processing Letters ◽

10.1007/s11063-019-10021-z ◽

2019 ◽

Vol 50 (3) ◽

pp. 2455-2479 ◽

Cited By ~ 3

Author(s):

Zhenhua Wang ◽

Gengsheng Ma ◽

Dianyao Gong ◽

Jie Sun ◽

Dianhua Zhang

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Evolutionary Algorithm ◽

Rolling Process ◽

Hot Strip Rolling ◽

Strip Rolling ◽

Hot Strip ◽

Artificial Neural

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MATHEMATICAL-ARTIFICIAL NEURAL NETWORK HYBRID MODEL TO PREDICT ROLL FORCE DURING HOT ROLLING OF STEEL

International Journal of Computational Materials Science and Engineering ◽

10.1142/s2047684113500048 ◽

2013 ◽

Vol 02 (01) ◽

pp. 1350004 ◽

Cited By ~ 1

Author(s):

S. RATH ◽

P. P. SENGUPTA ◽

A. P. SINGH ◽

A. K. MARIK ◽

P. TALUKDAR

Keyword(s):

Neural Network ◽

Mathematical Model ◽

Artificial Neural Network ◽

Flow Stress ◽

Mathematical Models ◽

Hybrid Model ◽

Data Driven ◽

Roll Force ◽

Hot Strip ◽

Artificial Neural

Accurate prediction of roll force during hot strip rolling is essential for model based operation of hot strip mills. Traditionally, mathematical models based on theory of plastic deformation have been used for prediction of roll force. In the last decade, data driven models like artificial neural network have been tried for prediction of roll force. Pure mathematical models have accuracy limitations whereas data driven models have difficulty in convergence when applied to industrial conditions. Hybrid models by integrating the traditional mathematical formulations and data driven methods are being developed in different parts of world. This paper discusses the methodology of development of an innovative hybrid mathematical-artificial neural network model. In mathematical model, the most important factor influencing accuracy is flow stress of steel. Coefficients of standard flow stress equation, calculated by parameter estimation technique, have been used in the model. The hybrid model has been trained and validated with input and output data collected from finishing stands of Hot Strip Mill, Bokaro Steel Plant, India. It has been found that the model accuracy has been improved with use of hybrid model, over the traditional mathematical model.

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