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10th International Conference on Steel Rolling

(ICSR'10)
 

Plate/Coil Mill Level 2 Model Upgrade with Metallurgical Modeling and Advanced Learning

Bingji (Benjamin) Li
www.metalpass.com/bli
Metal Pass LLC
www.metalpass.com
Pittsburgh, PA, USA
 

Key words: Level 2 Model, Metallurgical processes, Metallurgical models, Intelligent learning, Hybrid system
 

ABSTRACT

Mill Level 2 model, with major manufacture logics and mill intelligence in it, is responsible for creating draft schedule and achieving parameter targets for production optimization. Level 2 model errors cause equal deformation targets, metallurgical temperature targets and maximal productivity targets, etc. to be missed. This would lead to product shape problems (e.g. center/edge waves) and low mechanical properties. A large AGC movement due to initial roll gap error caused by inaccurate force prediction, for example, could result in plate head-end geometry problem. Traditional Level 2 model does not contain metallurgical principle such as those for thermomechanical rolling and microalloying strengthening. Due to the recovery, recrystallization and grain growth, etc., flow stress is very dynamic. The retained strain from incomplete recrystallization, and the microstructural changes during intermediate hold, etc., cause significant parameter prediction errors. In addition, there are problems in learning logics, such as scatter of adapted coefficients due to their potential dependence on each other. Blind learning only reaches limited accuracy.

This paper first provides various fixes on metallurgical issues and learning limitations mentioned above, and discusses application of a full set of rolling mill models in Level 2. Then it primarily introduces a simple but very effective learning mechanism, a so-called guided two-parameter fitting (FIT2G), as the solution to all those problems. The FIT2G uses carefully designed strain coefficients and strain rate coefficients, and performs adaptation by adjusting temperature coefficients and material coefficients. It can not only remove limitations of adaptive learning, but also include the metallurgical effects into the Level 2 model. The large number of flow stress coefficients, usually about 6,000 to 12,000 sets for a mill, is the integration of all the solutions for the learning logics and metallurgical effects. In addition, it only requires very limited modifications to the Level 2 source code and needs a very small temperature range to perform regression. Therefore, it is the right choice in improving existing Level 2 model, with very little concern on introducing potential bugs to the existing system. Past results in USA and current project warrantees in China all assure a low force prediction error below 5% for the plate/coil steckle mills. Economic value of the upgrade is usually millions of US dollars per year for each of the mills.
 

SUMMARY

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