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 Li
John Nauman
Metal Pass LLC.
www.metalpass.com
301 1/2 S. Winebiddle St, Pittsburgh, PA 15224
bli@metalpass.com, jnauman@metalpass.com
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 Li
www.bli1.com
bli@metalpass.com
Metal Pass LLC
David Cyr
Petrus Bothma
cyrd@osm.com, bothmap@osm.com
Department of Process Automation
Evraz Oregon Steel Mills
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 Li (Lead Contributor)
President & CEO, Metal Pass LLC
412 621 3836
John Nauman
Vice President of Operation, Metal Pass LLC
412 620 6066
www.metalpass.com
301 1/2 S. Winebiddle St, Pittsburgh, PA 15224
bli@metalpass.com, jnauman@metalpass.com
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.
Address: 557 Long Road, Pittsburgh, PA 15235
Email: francois@americansensors.com
Telephone: 412-242-5903
Fax: 412-242-5908
Xiaoqing Zhang
Operations Manger of American Sensors Corp.
Address: 557 Long Road, Pittsburgh, PA 15235
Email: sunnyxzhang@gmail.com
Telephone: 412-680-6415
Fax: 412-242-5903
Bingji Li
President of Metal Pass LLC
301 2/1 S. Winebiddle St. Pittsburgh, PA 15224
Email: bli@metapass.com
Telephone: 412-621-3836
Web: www.metalpass.com/bli
John Nauman
Lead Contributor, Vice President of Metal Pass LLC
301 2/1 S. Winebiddle St. Pittsburgh, PA 15224
Email: jnauman@metapass.com
Telephone: 412-620-6066
Web: www.metalpass.com/jnauman
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 Li, Ph.D.
Metal Pass LLC
bli@metalpass.com
(412) 621 3836
www.bli1.com
John Nauman, Ph.D.
Metal Pass LLC
jnauman@metalpass.com
(412) 620 6066
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 Li, Ph.D.
Metal Pass LLC
bli@metalpass.com
(412) 621 3836
www.bli1.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 Li
Metal Pass LLC.
www.metalpass.com
301 1/2 S. Winebiddle St, Pittsburgh, PA 15224
bli@metalpass.com
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 Li
www.bli1.com
bli@metalpass.com
Metal Pass LLC
David Cyr
Petrus Bothma
cyrd@osm.com, bothmap@osm.com
Department of Process Automation
Evraz Oregon Steel Mills
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)
