Metal Pass Consulting

Metal Pass Work Area

Level 2 model improvement

• Rolling mill Level 2 force model improvement. Many Level 2 force model use simple formula and thus only reach limited accuracy. Some system, such as Tippins Level 2, are even with design error in adaptive learning. A summary of the work for a client: Improvement on Level 2 Model Force Prediction is available as example. A 10-page stage report for a company ("Company A") with a reversing/steckle mill to roll plates and coils is also available. See stage 1 report   www.metalpass.com/consulting/FceModelRpt.doc and the report for stage 2 www.metalpass.com/consulting/FceModelRpt2.doc. 
• Rolling mill Level 2 temperature model improvement. We provide accurate model for heat transfer coefficients for every heat transfer stage. For example, the heat transfer during rolling in the roll gap depends on roll and stock materials, roll RPM, pressure, descaling procedure and roll cooling, etc. The heat transfer coefficients in the inter-pass or during traveling on the run-out table depends on relative speed between stock and air, etc. We also integrate those models into your temperature learning procedure.
• Controlled cooling Level 2 system for water cooling. After evaluation of nearly 10 site testing reports we have developed accurate model on heat transfer coefficients during controlled water cooling. The model can greatly increase prediction accuracy for the controlled cooling system. We also have developed a finite differential method (FDM) model which has great advantage over the traditional slab element method.

Model-based mill software development

• Metal pass has developed a long list of steel mill process model based applications. A summary of the applications is available: www.metalpass.com/consulting/metalpass.doc.
• We develop model-based mill applications for flat rolling and shape rolling, either hot or cold forming. We also develop programs for controlled water cooling, reheating, EAF/LMF steelmaking, and Casting, etc.
• We are developing a program package to predict rolled product properties. Process models and system learning will be combined. For example, the grain size at the entrance of the controlled water cooling in the run-out table can be used as learning parameter. A proposal for the package development is available: Prediction of Rolled Steel Properties.
• We are also looking for industrial partner(s) to develop Enterprise Roll Pass Software. See Proposal Part 1, Part 2, Part 3, and  Part 4.
• Microstructure prediction during steel rolling and cooling

Mill automation system development

Mill Improvement through Modeling

• Rolling Mill, Metal flow: Spread prediction (critical for roll pass design), Forward slip, Roll work diameter, Roll effective diameter, stock speed, etc.
• Rolling Mill, Load and power requirements: Flow stress, Roll separating force, Torque, Power, etc.
• Rolling Mill, Microstructure: Dynamic and static recrystallization, Grain size, Grain growth, etc.
• EAF/LMF Steel making: Temperature prediction, Alloy addition, etc.
• See the list of available process models for steel mill.