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The Iron & Steel Technology Conference and Exposition 2011 (AISTech 2011)
May 2-5, 2011, Indianapolis, IN., USA
 

NISCO Plate/Coil Mill Level 2 Force Model improvements

南钢板/卷厂二级模型之轧制力模型的改进

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

Pengju Zhu
Chief Engineer
Nanjing Iron & Steel Co. (NISCO)
Nanjing, Jiangsu, PR China

Daoyuan Wang, Ph.D.
Executive Vice Plant Manager
Nanjing Iron & Steel Co. (NISCO)
Nanjing, Jiangsu, PR China
 

Key words: Plate coil mill, Level 2, Force prediction, Flow stress learning, Metallurgical effect

关键字: 板卷厂, 二级系统, 轧制力预报, 流变应力自学, 金属学效果, 轧钢厂咨询, 二级系统开发, 过程模拟, 工业自动化, 钢厂, 中板二级系统
 

ABSTRACT

Level 2 [二级系统] force [轧制力] prediction [轧制力预报] serves as basis for Draft [压下量] schedule [道次规程] generation and initial [初始] roll [轧辊] gap [辊缝] setup before AGC adjustment. This paper covers Level 2 [二级系统] force [轧制力] model [轧制力模型] improvements [模型改进] in NISCO [南钢] plate/coil [板卷] steckle mill [炉卷轧机], especially for continuously increasing steel grade [钢种] [钢种], and for automatic design of flow stress coefficient [流变应力系数] based on chemical composition [化学成分] [化学成分], slab [板坯] and product mix, and rolling [轧制] process [轧制过程] including thermomechanical [热机械] rolling [轧制]. The guided two-Parameter [参数] learning [指导下的双变量自学], by designing flow stress [流变应力] coefficient [流变应力系数] with metallurgical [金属学] effect [金属学效果] integrated and learning [自学] logic [自学逻辑] issues resolved, proves to be an easy-to-apply and very accurate solution. force [轧制力] prediction [轧制力预报] accuracy [准确度] is thus significantly increased. Further Development [开发] fields are also listed.

南钢板卷厂炉卷轧机二级系统轧制力模型改进,尤其是针对不断增加的钢种,以及对流变应力系数的自动设计。指导下的双变量自学证明是一种简单且准确的解决方案。也列举了进一步开发的领域。
 

SUMMARY

Steckle mill Level 2 model from former TIPPINS has some learning logics issues in the force prediction. In addition, adaptive learning also has some limitations such as the dependence between the strain coefficient and the strain rate coefficient. Beyond this, in today’s plate mill production, metallurgical aspect such as retained strain, metallurgical transformation during hold, etc., has significant impact in the force prediction accuracy. Force model improvement to handle those common problems is of great importance to every Level 2, especially the Tippins package.

This project was initially created to provide some quick improvement to the Tippins Level 2 force prediction logics for the NISCO Plate/Coil Mill. During the development it has been evolved into a full scale system infrastructure upgrade including redesign of grade family, consolidation for the steel grades, and regeneration of Level 2 model grade list, automatic design for large number of flow stress coefficients, as well as handling in continuously increased steel grades and model grades, and so on. Total number of model grades, and so the number of flow stress models, has reached three times of the initial plan. Since this mill is relatively new compared with similar US mills, many technical issues, such as some draft scheduling logic problems which were already solved in the US mills, still remain to be resolved (in a different, ongoing project). As extension to the draft schedule, shape control and controlled rolling are on the way. Metal Pass, with its three specialized areas, the force prediction, the metal deformation and the microstructure modeling, is considered to be the right fit.

In this paper, development background, process, results, experience and lessons, together with the further development, etc., in the NISCO Level 2 force model improvement, are introduced.

In order to fix the learning logic problem and to remove the limitation of adaptive learning, and in order to include metallurgical principle in the Level 2, the Guided Two-Parameter Fitting (FIT2G) is applied. FIT2G uses only material coefficient and temperature coefficient for learning, while strain coefficient and strain rate coefficient are carefully designed and remain unchanged during the learning. Related work includes, at first, redesigning grade family list based on chemical composition, and consolidation for steel grades with similar composition. Then, a large number of model grades have been created based on product thickness, production process, rolling stage and slab thickness, that are used in NISCO production practice. Core development exists in the automatic design and development for the large number of flow stress coefficients for every model grades in three temperature regions. Grade files for each model grade were also created programmatically, accompanied with the redesign of all the related model data. Solution to the new grade and new model grade were also provided. Further improvements include modification for the model in the resume pass and extension to flow stress formula valid range, etc. In the model development, great attention was paid in applying controlled rolling in the mill operation.

High accuracy has been achieved for the force prediction, with the error mere one half of that in the old model. During the test, other weaknesses of the system were identified, e.g., draft scheduling logics, which has to be improved before applying the new model. Though the project of improving draft scheduling logics is ongoing and has made good progress, it is beyond the scope of the force prediction and is not covered in this paper. A list of further developments have been planned. In addition to the draft scheduling logics, it also includes the shape improvement, improvement of rolled steel properties, and software engineering advancement, etc.
 

PRESENTATION

Session: Advances in Plate Rolling Technology (Plate Rolling)
Presentation Time:  3 May 2011 at 3 pm (Room 104)

(Paper: 11 pages, pdf)
 


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