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Improvement on Level 2 Model Force Prediction

Level 2 force model is one of the most significant section of the Level 2 system. See Significance of Accurate Level 2 Force Prediction.

Potential Problems You May Check

(You may refer to Mill Level 2 Model Basics for the terms and symbols used here)

Many of the following Level 2 model problems seem to be ridiculous, but before you laugh, check your own system. You cannot guarantee your system doesn't have any of those issues. At least, one of the popular Level 2 systems in the market has ALL those problems!

  1. Two of the frequently used learning fits assume that draft has no effect on rolling force, torque and power, so 10% draft and 40% draft require the same force, torque and energy. This, of course, is wrong, but check your log to see how often your system were using the FIT2 and FIT3B (refer to Mill Level 2 Model Basics for the terms used).

  2. Two of the same frequently used learning fits assume that rolling speed has no effect on rolling force, torque and power, so to roll 1m/s and 10m/s has the same force, torque and power. This, of course, is wrong, too, but check your log to see how often your system were using the FIT2 and FIT3A.

  3. How could this happen? Because, though the cases mentioned here are bad, other cases may be worse. The Level 2 model usually tests all the learning fits and selects the one with the lowest error. The learning fit FIT4 doesn't have the above-mentioned problem, but it may have still lower accuracy than FIT2, FIT3A and FIT3B in certain situation; that's why the system sometimes picks the other one (FIT2, FIT3A or FIT3B) instead of FIT4. Our research indicates that FIT4 does have weaknesses in certain situation and we can fix the problem.

  4. Sometimes with a draft of 10%, for example, in a pass, you should use e.g. 15% or even 20% as the draft to calculate force, torque and power. Otherwise your results will be inaccurate even if all others of your models are perfectly correct. Interestingly, this often happens in finishing passes and may cause your shape problems.

  5. Level 2 model force prediction formula may not apply to a draft below 10%. To make things worse, the draft about 10% is often used in the finishing pass and this may lead to product shape problem.

  6. Some passes in your two-piece rolling may, just like one of our clients, usually have a force error of 10% to 30%, and often of -20% to 60%.

  7. Though you don't want to, can you guarantee you are not, performing your rolling in the two-phase region? If you roll unexpectedly in the two-phase region, the force error would be big and your product shape could be poor (especially if it is thin).

Check whether you have set a learning parameter (such as the coefficient for strain or strain rate) to zero when you don't use it for learning. If you have, you are making the same mistakes as the problems No. 1 and 2 described above. The error from the problem No. 3 is introduced from the dependence of the learning parameters, which is a common weakness of the adaptive learning. The problem No. 4 above is due to the retained strain from previous pass, and the problem No. 5 is caused by valid range problem of certain flow stress formula. The problems No. 6 and 7 indicate that metallurgical issues often cause significant error to the Level 2 model.

Force Model Improvement

Due to the lack of understanding of the nature for roll separating force, especially in the areas such as flow stress and retained strain, many vendors of Level 2 system overly simplified process and ignored the metallurgical effects on roll separating force. Sometimes Level 2 administrators may also have difficulty in applying metallurgical principles in operating the Level 2 system. The Level 2 model may be operated with inaccurate input data (e.g. high-temperature properties of specific heat, E-Modulus, thermal expansion factor, etc. in dependence on the temperature), or with improper learning (feedback) operation. Consequently, the Level 2 system may be instable in the force prediction accuracy, with very high error for certain products, such as the hard and thin ones.

Flow Stress error

One of the toughest areas in the force prediction is the flow stress. Flow stress is tightly related to the metallurgical process such as recrystallization and correspondingly the retained strain. If a simple equation is used to describe flow stress, errors may occur in large strain and small strain passes. More complicated problems exist in, that in todayís rolling practice a great number of passes are rolled below the recrystallization temperature (technically itís a cold rolling). Due to the incomplete recrystallization, up to 80% (Tamura, Ouchi, et al.), or even more, of the retained strain from previous pass can be added to the current pass. In this aspect, we have models to calculate the retained strain, and we have experience in integrating the result into your existing Level 2 without major change of your Level 2 source code.

Many Level 2 system use adaptive learning. However, due to various issues, the adaptive learning has certain weaknesses and they limit the accuracy of the Level 2 force model. Through our consulting work for the steel mill Level 2 models, we have summarized a number of weaknesses of the adaptive learning. We can overcome those weaknesses, by make a very slight system change, usually only by changing several lines of the source code, or by simply modifying your input data without any source code change, to greatly increase your model accuracy. We have developed a procedure called the Guided Two-Parameter Learning (FIT2G), a special type of the adaptive learning, to overcome the shortcoming of the general adaptive learning. To achieve this, we have developed over 6,000 sets of the flow stress coefficients as the start points of the adaptive learning.

See Modeling Issues in Level 2 to understand the problems described above.

Simply letting Level 2 calculate flow stress coefficients is not an optimal way to perform learning. Certain guidelines may be of great help for better system learning. In this area our experience on flow stress modeling can be of great value - the experience to generate over 2000 flow stress models (see www.metalpass.com/flowstress, or from North America, www.flowstress.com).

If you are using the neural network, etc. to do the system learning, we could provide you with an expert system to guide your learning. If you by chance have an expert system, we could easily improve your expert system. Those would, too, significantly increase your model accuracy. If you want to make sure your neural network never fails (a black-box neural network could fail at any time) and would be continuously improved, we could improve your learning logic by applying our hybrid solution (a seamless combination of the empirical model and neural network).

Some Level 2 model may divide the rolling temperature range into the high, medium and low temperature regions. How to optimize the dividing points for the three regions, for each steel grade, and based on the flow stress tendency and metallurgical feature, is also critical for the high quality model prediction. During our consulting work we have solved the problem and are ready to apply the results to you. You don't have to reinvent the wheel to do your own research.

Other parameter errors

Depending on the design of your Level 2 system, following areas may also be the potential sources of error:

  1. Roll flattening calculation. Roll flattening should be calculated. Some systems may overly simplify the calculation process; others may even ignore the roll flattening for hot rolling.
  2. Shape factor. Shape factor covers all the contributions to the roll separating force beyond the mean flow stress and the contact area. It is affected by both roll gap geometry and friction. We have studied over a dozen of different formulas for shape factor, and are capable of improving it in case your Level 2 model has problem in this aspect.
  3. Temperature during rolling, interpass cooling and controlled cooling. Many Level 2 systems only use the primary models, which consider a heat transfer coefficient as a constant, to perform temperature learning. We can apply our heat transfer models to improve your temperature learning logics, in which a heat transfer coefficient is described as a function of various rolling/cooling parameters. This would greatly improve your temperature prediction accuracy. See Temperature Model Improvement.

What we can do for you

We have made a great progress on the rolling process modeling since 20 years throughout our work in various countries (Germany, USA, China, etc.). During the work we have identified a long list of potential sources of force errors and have established the solution in each aspect. We can help you, by making a minimal change of your level 2 system, or by simply modifying your Level 2 input data, to achieve much higher model accuracy. For reversing/steckle mill, your Level 2 is expected to have an error of about 5% for force prediction; for continuous strip rolling, the accuracy should be still higher because the rolling condition is more consistent than in reversing/steckle mill. After the force improvement the system accuracy should be stable for all products. Our current result for a reversing/steckle plate mill, with test data for previously troubled grades (hard and thin ones with shape problems), is averagely 3.4% (average of the absolute force errors)! See the paper we published, some jointly with the client.

For this purpose we may work in following areas:

  1. To examine your Level 2 system to check potential design problems.
  2. To analyze your history data by accessing your log files or database to identify weaknesses. We have a list of programs used to analyze your data.
  3. To establish stable, optimized learning for your Level 2 model. If you analyze your log files/tables for your flow stress coefficients, you would be surprised by their large scattering range. Certain guidelines should be applied to achieve proper learning.
  4. To help you with other model problems besides the force prediction. Dozens of rolling process models we have created in the past 20 years can be used.
  5. If you plan to upgrade your current Level 2 system or design a new one, one or more of our consultants can join your team. We have excellent steel mill modeling and software engineering expertise (10 years of software development experience in steel mills).
     

Metal Pass 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 admin@metalpass.com or by phone (001) 503 516 9625 for top quality consulting services.

 

 
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