E Oznergiz, C Ozsoy I Delice, and A Kural
Jed Goodell
September 9th,2009
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A fast, reliable, and accurate mathematical
model is needed to predict the rolling force,
torque and exit temperature in the rolling
process.
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Function of Paper: To propose an adaptable
neural network model for a rolling mill
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Why important?
An Artificial Neural Network is a computer model designed to simulate the
behavior of biological neural networks, as in pattern recognition, language
processing, and problem solving, with the goal of self-directed information
processing.
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The paper shows an effective way to
compute the needed rolling force, torque and
temperature needed for hot rolling
1.
2.
3.
Empirical Model
Lookup tables
Neural Network
Empirical vs NN
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Outputs:
 Rolling force
 Torque
 Exit Temperature
MISO System– Multi Input Single Output
Back Propagation Algorithm
To find Force and Torque:
Inputs: Roll radius, number of revolutions, entry slab temperature,
entry and exit thickness.
Output: Force and Torque
To find Exit Temperature
Inputs: Energy required, exit thickness, radius, number of revolutions,
entry slab temperature, slab width, slab volume.
Output: Exit Temperature
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Hot rolling mill at Eregli Iron and Steel Factory in
Turkey.
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The equipment:
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Slab furnace
Pre-rolling mill
Reversible mill
Seven strip rolling stands
Cooling system
Shearing System
Data Acquisition and Computer control system
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Dimensions monitored during each pass by
an X-ray
Temperature monitored with pyrometer
Roll force and torque monitored using four
load cells placed along the mill
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NN model was 22 % better predictor for
force, 24% better for torque, and 14 % better
for exit temperature
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Errors decreased by 85% for force, 97% for
torque, and 92% for temperature
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Practical use – faster rolling, reduction in
energy , more flatness control
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Simple learning method vs Adaptable NN
Offline vs Online – weight update
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Industries most impacted – any industry
using sheet metal