Level 2 Model Improvement
Case Study: Oregon Steel
This paper outlines the work completed and
suggestions to further improve Evraz Oregon Steel’s Level 2 models, along with
the presentation of the trial results to validate the theory. The sources of the
model errors were identified to be initiated from the adaptation function,
ignorance of the metallurgical effects, and the valid range of the formula, etc.
The Level 2 model improvements were
carried out in the following two primary areas:
First area of improvement (for learning
and metallurgical issues)
Learning logic - applied the Guided
Two-Parameter Learning (FIT2G)
Metallurgical interaction - considered
effect of retained strain, etc., into the FIT2G.
Second area of improvement (defining the
valid range of the formula and further metallurgical issues e.g. in resume
Flow stress formula - reduced errors in
passes with draft below 10% or over 30%
Resume pass force - reduced errors caused
by microstructure evolution
Ranges of the temperature regions and
rolling in the two-phase region, etc.
One of the problems in the adaptation
function was allowing the use of zeros instead of medium values for the C3
and C4 parameters. Other learning issues were such as, the scatter of
the C3 and C4 due to their dependence on each other. This
dependence indicated that the blind adaptive learning, even the Four-Parameter
Learning (FIT4), could only reach limited accuracy. Including the C3-C4
dependence in the learning logics would greatly increase the prediction
accuracy. This project demonstrated that it is very critical for a Level 2 model
to consider metallurgical effects. The project calls for an expansion of Level 2
models from current mechanical system to a combined metallurgical and mechanical
system, in order to satisfy the new production practices. Current Level 2
systems in the market were usually designed as a mechanical system without
sufficient metallurgical principles taken into account. With increasing
applications of metallurgical processes such as controlled rolling in the steel
mills, traditional Level 2 models without metallurgical consideration could only
reach a limited level of accuracy.
The Guided Two-Parameter Learning (FIT2G),
by using carefully designed medium values for C3 and C4,
and performing the adaptation by adjusting C1 and C2,
would be a simple but very effective solution, especially for improving existing
Level 2 models. This procedure can, not only, remove the limitation of the
adaptive learning, but also include the metallurgical effects into a Level 2
model. The large number of flow stress coefficients, over 6000 sets of C1,
C2, C3 and C4, is the result of all the
solutions for the learning logic and metallurgical effects. In addition, it only
requires very limited modifications to the Level 2 source code and needs a very
small temperature range (number of points) to perform the regression. Even for
the troubled grades that experienced phase transformation, the first
improvement, by applying the Guided Two-Parameter Learning, still led to over
80% passes below 5% error, over 90 passes below 10% and over 99% passes below
15% errors. This result, confirmed by the trials, fully demonstrated the
effectiveness of this learning procedure.
With the issues and sources of error
revealed in the mill trials, particularly those 1% of passes that were still
with error of 15% or higher, the second improvement was conducted and
theoretically showed to further reduce the model prediction error. However this
solution as yet to be implemented. The temperature regions were divided based on
the metallurgical, dividing points and the range of the low-temperature region
was narrowed. Modification to the strain was done to extend the valid range of
the flow stress formula for small strain (below 0.1) and large strain. Predicted
flow stress for the resume pass was scaled down (or up) using the
temperature-dependent empirical factor, in order to compensate for the error
caused by microstructure evolution during holds. In addition, issues on entry
into the two-phase region was identified and a possible solution was suggested.
 B. Li, D. Cyr and P. Bothma: Level 2 Model
Improvements at Evraz Oregon Steel. AISTech 2009. St. Louis, MO. USA. May
4-7, 2009.metalpass.com/consulting/Level2ModelDefects.htm. Metal
Pass LLC, Pittsburgh, PA, USA. Accessed in January 2009.
 B. Li: Product Defects and Level 2 Model Error. Online
 I. Tamura, et al: Thermomechanical Processing of
High-strength Low-alloy Steels. Butterworths & Co. 1988. ISBN 0-408-11034-1.
 B. Li: Compared Experimental and Theoretical
Investigations of Forming Technical Parameters in Shape Rolling with Example of
the Hot Rolling of Angle Steels. TU Bergakademie Freiberg, Germany, 1996.
 B. Li: Flow Stress. Online at www.metalpass.com/flowstress.
Metal Pass LLC, Pittsburgh, PA, USA. Accessed in January 2009.
 A. Hensel & T. Spittel: Kraft- und Arbeitsbedarf
bildsameer Formgebungsverfahren. VEB Deutscher Verlag für
Grundstoffindustrie, Leipzig, Germany. 1977.
 Y. Saito, et al: The mathemarical model of hot
deformation resisitance with reference to microstructural changes during rolling
in plate mill. Transaction ISIJ, 1985, 25(11).
 B. Li & J. Nauman: Metallurgical, modeling and
software engineering issues in the further development of the steel mill Level 2
models. AISTech 2008. Pittsburgh, PA, USA. May 5–8, 2008.
Level 2 Model Improvement
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