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Improvement on Level 2 Model Temperature Prediction

Significance of Accurate Temperature Prediction

After a product order is received in a mill, the metallurgist will schedule a steel grade (if the order didn't specify it) and a set of production stages (reheating, rolling, controlled cooling, etc.) to fulfill the order. The temperatures in the start and end of each stage are among the most critical parameters for the metallurgist to schedule, because the metallurgical transformations and finish steel properties are tightly related to the transaction temperatures. If the Level 2 model is not sufficiently accurate, the actual temperatures in the production would be off the schedule and the final product properties would be off the targets. In addition, due to the different temperatures between the actual and predicted (planned) ones, the actual deformation could be under higher force and torque, and this could even damage the equipment. Selected examples for the problem are discussed below.

Examples of the problems caused by a wrong temperature

  • Wrong mechanical properties, often poorer ones. Wrong rolling temperature leads to the rolling process to be performed in a wrong metallurgical region and this may produce different microstructures from planned. During the production scheduling, the metallurgist would fully utilize metallurgical transaction (controlled rolling, precipitation, etc.) to produce preferred microstructure and finish properties. Due to the system error, a wrong rolling temperature in each rolling stage, and consequently maybe also a wrong initial temperature for controlled cooling, lead to different microstructure and poor mechanical properties. The rolled steel may not achieve the target properties.
  • Wrong force. Wrong temperature leads to a wrong force and may damage the rolls and other equipment. This is particularly the case in the roughing rolling stage. For finishing passes, accurate force prediction is critical to a good product shape. See Significance of Accurate Force Prediction and Product Defects and Level 2 Model Error.
  • Other unexpectations. For example, due to temperature error, the rolling in the finish pass may actually be performed in the two-phase region, as in a case of a Metal Data client (Fig. 1). In the last two passes, with a decreasing temperature the flow stress went lower. This created large force error (sometimes over 40%) and caused very poor product geometry.

          Fig. 1: Measured flow stress at strain 0.3 and strain rate 10/s

Temperature Model Improvement

Many Level 2 systems only use the primary model to calculate temperature. The primary model takes into account the heat transfer in conduction, convection, radiation and heat generation during rolling and controlled cooling. During the calculation the coefficients for those heat transfer types are used as constants rather than variables depending on operational parameters (temperature, speed, etc.).

However, it is the secondary model that contributes greatly to high accuracy of temperature prediction. We have a long list of the secondary models to predict heat transfer coefficients in conduction, convection and radiation, and the ratio of thermal energy to deformation energy. As examples, the convection heat transfer coefficient can be calculated with the relative speed of air/water and the rolling stock, and the heat transfer coefficient between work roll and stock also depends on many parameters and often varies from 2500W/m2K to 10,000W/m2K. Many systems treat the heat transfer coefficient between work roll and stock as a constant in doing the temperature learning. If the influence factors are integrated into the learning, it will greatly increase the temperature prediction accuracy. In addition, the specific heat is strongly temperature dependent. If austenite - ferrite phase transformation occurs, the specific heat will sharply increase to more than twice as high and then sharply drop.

Most Level 2 systems also ignores thermal energy involved in the phase transformation or other metallurgical processes.

Most thermal properties are temperature dependent though many Level 2 system treat them as constant values. Please see http://www.Meta4-0.com/hit for background knowledge and data.

What we can do for you

For your Level 2 temperature model improvement we may help in following areas:

  1. To analyze your history data by accessing your log files or database data to identify operational problems. We have a list of programs that can be used to analyze your data. To save your money, we may provide our web-based Remote Diagnosis system for you to find your problems yourselves.
  2. To establish optimized system learning for your Level 2 model. For example, many Level 2 systems use the heat transfer coefficient between the roll and steel as learning parameter. It would be more efficient to redesign this learning logic by using our secondary models because the heat transfer coefficient depends on rolling process parameters.
  3. To apply accurate high-temperature property data and our secondary thermal models in your Level 2 system. Most modifications may be made only in the input log files or input databases, so we may not need to make so many modifications to your Level 2 source code.

Metal Data Resources on Level 2 Model Improvement

See the profile for the primary consultant Dr. Benjamin Li. You may also view our company and personnel profiles. Please contact us via email bli68@qq.com or by phone (0086) 13400064848 for top quality consulting services.



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