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Recent Publications of Metal Pass

We assure the quality of our papers; that's why all papers we submitted have been accepted.


This page initially listed six papers submitted/published in February-March 2008. It then includes some of the later publications.
 

AISTech 2008

Metallurgical, Modeling and Software Engineering Issues in the Further Development of the Steel Mill Level 2 Models
(Presentation Slides)

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

John Nauman, Ph.D.
Vice President of Operation
Metal Pass LLC
www.metalpass.com
Pittsburgh, PA, USA

Key words: Level 2 model, force, flow stress, metallurgical, modeling, software engineering, learning, retained strain

Abstract

This paper introduces selected metallurgical, modeling and software engineering issues involved in the further development of steel mill Level 2 models. Limitation of the adaptive learning has been identified and the Guided Two-Parameter Learning is considered the quick fix for existing systems. Metallurgical issues involve the retained strain and the rolling in the two-phase region, etc. The modeling issues include rolling process models, learning logics and the intelligent learning. There are also software engineering issues such as system design with mill process models and the web-based Level 2 system. Finally, a concept on developing next-generation Level 2 system was outlined.

* Paper completion deadline (2/15/08); published. Presented in May 2008.


AISTech 2008

Level 2 Model Improvements at Evraz Oregon Steel Mills
(Presentation Slides)

Bingji (Benjamin) Li
www.bli1.com
Metal Pass LLC
www.metalpass.com
Pittsburgh, PA, USA

David Cyr
Level 2 Engineer
Department of Process Automation
Evraz Oregon Steel Mills
Portland, OR, USA

Petrus Bothma
Manager
Department of Process Automation
Evraz Oregon Steel Mills
Portland, OR, USA

Key Words: Level 2 model, metallurgical, roll force, steckle mill, adaptive learning, flow stress, resuming passes, draft schedule

Abstract

Level 2 force model was improved for OSM plate steckle mill. Learning logics and metallurgical effects were identified as the primary sources of error. Limitation of the adaptive learning was discussed. Concept of guided two-parameter learning was proposed to resolve the issues and over 6000 sets of the flow stress coefficients were designed. In addition, the problems in resuming passes and the passes with large or small strain were solved. Even with troubled grades, the testing still indicated a high accuracy with an average absolute error of 3.4%. It was intended to make minimal code change for the existing system.

* Paper was accepted by AISTech 2008. However, due to certain delay, this paper is to be published in 2009 (accepted for AISTech 2009).


Flat-Rolled Steel Processes: Advanced Technologies. By V. Ginzburg, etc. CRC Press.

Metallurgical, Modeling and Software Engineering Issues in the Further Development of the Steel Mill Level 2 Models
(Book Chapter 26)

Bingji (Benjamin) Li, Ph.D.  (Lead Contributor)
President & CEO
Metal Pass LLC
www.metalpass.com
Pittsburgh, PA, USA

John Nauman, Ph.D.
Vice President of Operation
Metal Pass LLC
www.metalpass.com
Pittsburgh, PA, USA

Content

  • Level 2 Model
  • Metallurgical Issues in Level 2
    • Retained Strain
    • Rolling in the Two-Phase Region
    • Metallurgical Aspect of the Flow Stress
    • Others
  • Modeling Issues in Level 2
    • Limitation of the Adaptive Learning
    • The Guided Two-Parameter Learning (GFIT2)
    • Flow Stress Valid Range
    • Temperature-Dependent Properties
    • Intelligent Learning
  • Software Engineering Issues in Level 2
    • System Architecture based on Interactive Relationship of Mill Process Models
    • Web-based Level 2 System
    • Others
  • Next-Generation Level 2 System
    • Next-generation Level 2 system
    • Next-generation Level 2 model

* Paper completion deadline (2/29/08). Expanded and modified from a similar writing. Accepted for publishing. Book on printing.


Flat-Rolled Steel Processes: Advanced Technologies. By V. Ginzburg, etc. CRC Press.

The State-of-the-art of Infrared, Laser and Microwave based sensors and systems
(Book Chapter 22)

Francois Reizine
Lead Contributor, President of American Sensors Corp.
Pittsburgh, PA, USA

Xiaoqing Zhang
Operations Manger of American Sensors Corp.
Pittsburgh, PA, USA

Bingji (Benjamin) Li
President of Metal Pass LLC
Pittsburgh, PA, USA

John Nauman
Lead Contributor, Vice President of Metal Pass LLC
Pittsburgh, PA, USA

Content

  • Current Sensor Technologies
  • Principles of Selected Applications
    • Continuous Caster Optimization of Cut
    • Width Measurement of Slab
    • Strip Centering/Camber and Width Measurement
  • Sensor Systems
    • Systems Developments
    • Systems Techniques
    • System Examples in Slab Casting
    • System Examples in Hot Rolling
    • System Examples in Finishing

* Paper completion deadline (2/29/08). Accepted for publishing. Book on printing.


Materials Science & Technology 2008 (MS&T'08)

Significance and Development of a Next-Generation Level 2 Model as a Metallurgical System
(Presentation Slides)

Abstract

Level 2 model improvement projects have revealed various metallurgical issues that negatively affect the current Level 2 model. The considerable retained strains due to uncompleted recrystallization, and the metallurgical phenomena during hold and two-phase region rolling, etc., cause significant model errors which cannot be removed by adaptive learning. Wide application of metallurgical processes in today’s steel rolling calls for a Level 2 model to fully consider metallurgical principles. The next-generation Level 2 model should include a hybrid system by combining a full-range of metallurgical models with intelligent learning such as neural network, together with an expert system to guide the learning. The new model would also improve the pass schedule in controlled rolling principle and provide assistance for the Level 3 scheduling. The revealed metallurgical issues, the general concepts of the next-generation Level 2 system and the related metallurgical models, etc., will be introduced.

  • Level 2 model as a metallurgical system
    • Incomplete recrystallization and retained strain
    • Softening during the hold
    • Two-phase region
    • Metallurgical nature of the flow stress
    • Property variations
  • Benefits of metallurgical Level 2
    • High Accuracy of the Force Prediction
    • Improved pass schedule and slab selection
  • Development of Next-Generation Level 2 Model as a Metallurgical System
    • Level 2 System
    • Rolling mill Level 2 model
    • Reheating furnace Level 2 model
    • Controlled cooling Level 2 model

Submitted by

Bingji (Benjamin) Li, Ph.D.
www.bli1.com
Metal Pass LLC
www.metalpass.com
Pittsburgh, PA, USA

John Nauman, Ph.D.
Metal Pass LLC
Pittsburgh, PA, USA
www.metalpass.com/jnauman

* Paper abstract submitted (3/4/08); accepted for publishing. To be presented on Oct., 2008.


Materials Science & Technology 2008 (MS&T'08)

Career Development to be a Multi-National and Multi-Disciplinary Engineer
(Presentation Slides)

Abstract

Experiences are shared on how to perform self-training to become one of the most dynamical engineers, for integrating German engineering, US IT and Chinese market. With over 30 years of training, the author has gained three countries' working experiences, four languages and skills on material engineering, mechanical engineering, software engineering and industry automation. After receiving Ph.D., working on rolling process modeling and publishing a book, the author spent recent 10 years to be a mill-automation software engineer and to do mill application development. To be a highly qualified software engineer, the author completed 30 computer classes. Critical factors for success are to plan ahead and to brew interest in the things to be done. The paper also outlines author's results on the mill process models, web-based applications, general design on the next-generation Level 2 systems and a book in writing on steel mill process modeling and computer application, etc.

Submitted by

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

* Abstract submitted (3/4/08). Accepted; presented in Oct. 2008.



AISTech 2009

Development of Model-Intensive Web-based Rolling Mill Applications
(Presentation Slides)

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

Abstract

Model-intensive and web-based steel rolling mill applications have been developed in metalpass.com. They include pass design suites AutoForm and FreeForm, mill force/torque prediction suite, temperature profile program with finite-differential method for rolling and water/air cooling, and microstructure prediction application, etc. Coupled with tension models, the FreeForm is particularly useful for high-speed rolling blocks, and for both designing new passes and examining existing ones. Multiple algorithms are applied to ensure both speed and accuracy. Issues in developing each of the applications, such as process modeling, data modeling, model verification, object-oriented programming, and data management, etc., are discussed.

* Abstract submitted (July 2008), accepted for AISTech 2009. Paper submitted on 2/13/09.

 


AISTech 2009

Level 2 Model Improvements at Evraz Oregon Steel
(Presentation Slides)

Bingji (Benjamin) Li
www.metalpass.com/bli (www.bli1.com)
Metal Pass LLC
Pittsburgh, PA, USA
www.metalpass.com

David Cyr
Level 2 Engineer
Department of Process Automation
Evraz Oregon Steel Mills
Portland, OR, USA

Petrus Bothma
Manager
Department of Process Automation
Evraz Oregon Steel Mills
Portland, OR, USA

Key Words: Level 2 model, metallurgical, roll force, steckle mill, adaptive learning, flow stress, resuming passes, draft schedule

Abstract

Level 2 force model was improved for OSM plate steckle mill. Learning logics and metallurgical effects were identified as the primary sources of error. Limitation of the adaptive learning was discussed. Concept of guided two-parameter learning was proposed to resolve the issues and over 6000 sets of the flow stress coefficients were designed. In addition, the problems in resuming passes and the passes with large or small strain were solved. Even with troubled grades, the testing still indicated a high accuracy with an average absolute error of 3.4%. It was intended to make minimal code change for the existing system.

* Accepted for publication in AISTech 2009. Paper submitted on 2/13/09.

 


Materials Science & Technology 2009 (MS&T'09)
SPECIAL TOPICS
- Developments in Web-based Material Property Databases, Knowledge Management of Materials Information, and Materials Informatics

Development of Web-based Metal Property and Metal Information Databases

Abstract

A list of web-based metal property and metal information databases have been developed and made accessible through metalpass.com. The property databases include Flow Stress, High-Temperature Property and General Property, etc. The information databases consist of Metal Dictionaries (both Tech Terms and Translation), Metal Software, Metal Patents, and Metal Directory, etc. Number of entries in each database usually ranges from several thousand to over fifty thousand. Flow stresses in dependence of strain, strain rate and temperature, etc. are provided in the form of both data and models, while high-temperature properties are available in temperature dependence. As extensions to the databases, over a thousand pages of short papers describing technical details and dozens of applications for predicting process/product parameters are provided. Besides data development, data management and data application, etc., technical significance of the data such as temperature dependence of metal properties in hot forming modeling is also covered.

Submitted by

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

* Abstract submitted (3/6/09); Accepted; Paper completed and accepted; To be presented in Oct. 2009.

 


AISTech 2010

Mill Level 2 Model in Improvement of Product Quality and Productivity

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

Key Words: Level 2 Model, draft schedule, product property, shape defect, productivity, rolling mill models

Abstract

Mill Level 2 model, with major manufacture logics and mill intelligence in it, is responsible for creating draft schedule and stage plan. Level 2 model errors would cause equal deformation target, metallurgical temperature target and maximal productivity target, etc. to be missed. This paper first discusses association of Level 2 model quality with product property and shape and mill productivity. Then it covers application of a full range of rolling mill models in Level 2, and integration of models on product defects (camber, center/edge wave, etc.) with Level 2 and AGC. Examples are from author’s past and ongoing consulting projects.

* Paper presented by Douglas Stalheim, a mill consultant associated with Metal Pass (Benjamin Li was on onsite in China on mill Level 2 project).

 


AISTech 2010

Developments on a Web-based Metal Technology and Metal Information Network

Key Words: Metal Technology, Metal Information, Web-Based, Tech Resources, Tech Directories, Categorization, Data Population

Abstract

Developments on metal technology and metal information network metalpass.com are summarized and experiences shared. The website includes metal properties (flow stress and high-temperature properties, etc.), tech resources (e.g. Metal Dictionaries for Tech Terms and Translation, Metal Patents, Software Database and Tech Papers), and business showcase applications (Metal Directory, Product Profile and Tech Profile, etc.). Development processes such as data collection, data categorization and data population are discussed, together with implementations for automatic upload of technical paper, expandable submission of listing in every page of the showcase applications, and web-based mill software suites, etc.

Submitted by

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

* Paper presented by Douglas Stalheim, a mill consultant associated with Metal Pass (Benjamin Li was on onsite in China on mill Level 2 project).

 


10th International Conference on Steel Rolling

Plate/Coil Mill Level 2 Model Upgrade with Metallurgical Modeling and Advanced Learning
(Presentation Slides)

Bingji (Benjamin) Li, Ph.D.
Metal Pass LLC
www.metalpass.com
Pittsburgh, PA, USA
(001) 412-621-3836

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.

* Presented in Sept. 14-17, 2010, Beijing, China.

 


AISTech 2011

NISCO Plate/Coil Mill Level 2 Force Model improvements

Abstract

Level 2 force prediction serves as basis for draft schedule generation and initial roll gap setup before AGC adjustment. This paper covers Level 2 force model improvements in NISCO plate/coil steckle mill, especially for continuously increasing steel grades, and for automatic design of flow stress coefficients based on chemical composition, slab and product mix, and rolling process including thermomechanical rolling. The guided two-parameter learning, by designing flow stress coefficients with metallurgical effects integrated and learning logic issues resolved, proves to be an easy-to-apply and very accurate solution. Force prediction accuracy is thus significantly increased. Further development fields are also listed.

Submitted by

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

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

Daoyuan Wang
Nanjing Iron & Steel Co. (NISCO)
Nanjing, Jiangsu, PR China

 


AISTech 2011

Development of Next-Generation Level 2 Model as Metallurgical System

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

Daoyuan Wang
Nanjing Iron & Steel Co. (NISCO)
Nanjing, Jiangsu, PR China

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

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

Abstract

Various metallurgical issues negatively affect current Level 2 model and cause significant model errors which cannot be removed by adaptive learning. Wide application of metallurgical processes (e.g. controlled rolling) calls for metallurgical principles in Level 2 model. The next-generation Level 2 should include a hybrid system by combining a full-range of metallurgical models with intelligent learning. The new model would also improve the pass schedule in controlled rolling. The revealed metallurgical issues, the general concepts of the next-generation Level 2 system, and the related metallurgical models in rolling, reheating and accelerated-cooling will be particularly discussed.

 


International Symposium on the Recent Developments in Plate Steels
19-22 June 2011 – Winter Park, Colorado

Improved Level 2 Draft Scheduling for Good Plate Shape and Properties

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

Key words: Level 2 Model, draft scheduling, plate shape, product property

Abstract

Primary purpose for plate mill Level 2 model is to create quality draft schedule for good plate shape and product properties. Three key issues should be well addressed in the Level 2 model. Firstly, the logic in creating draft schedule based on predicted parameters should be good; secondly, parameter prediction as basis for the draft scheduling should be accurate; and thirdly, metallurgical issues such as retained strain, recrystallization, etc., for model accuracy and rolled steel properties, should be well considered. Level 2 model should achieve equal deformation target, metallurgical temperature target and maximal productivity target. Draft scheduling is also aimed at grain refinement for the finish product. This paper summarizes work results in several plate mills on Level 2 model logic improvement, Level 2 parameter prediction improvement and metallurgical integration into Level 2 model. Features for the next-generation Level 2 model as metallurgical system are also introduced.


 

 
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