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Level 2 Model Optimization and New Product Development

Benjamin Li, Metal Data LLC
www.meta4-0.com (Industry 4.0 Metaverse)


3. A Simple and Efficient Way to Integrate New Models into Level 2

(1) General Learning
We have developed a simple, easy but effective way to integrate large number of new models into Level 2 System, thus to upgrade conventional Level 2 System into the New-Generation one! In this way, only a very few source code modifications is required.

Usually machine learning can be carried out in following ways:

(1) . With chemical composition and temperature
(2) . (1) plus strain
(3) . (1) plus strain rate
(4) . (1) plus strain and strain rate

In real operation usually all 4 cases are considered, and the best one was selected. Of those selected, there occur often the bigger strain factor plus small strain rate factor, or the small strain factor plus bigger strain rate factor. Therefore, both the strain factor and strain rate factor can be any value from large to small. There would be other problems. Those problems reduced the accuracy of the model prediction.

(2) Guided Two-Parameter Learning
To solve this problem, we classify the all possible chemical composition into material grades/Steel grades (most plants have about 100-200 grades, average as 150), Thickness (considered as 5 cases), Product types (4 cases), Rolling stages (3 cases), Slab thickness (3 cases), and 3 temperature zones. Under this classification, there are 54000-108000 cases (average 81000 cases. Each case has a data file containing over 100 data and for a production process. Material data for E modulus and specific heat, etc. are pretty strong temperature dependent; in particular, flow stress coefficients as material factor, temperature factor, etc. can be determined through offline design, wile strain factor and strain rate factor can be determined through machine learning. These learning procedure is called guided Two-Parameter Learning. So-called guided is because of the offline design into above mentioned cases, and Two-Parameter is referred to strain factor and strain rate factor.
(3) Others
In some situation there may not be all cases. For example, for thin product, the high thickness of the slab may not be used.

In each production only one file is used for learning, and the learnt factor (strain and strain rate) is updated in the data file (only small portion of the data is changed). So in the source code, only the source code regarding the learning need modification; others, such as use cases/user scenarios, do not need modification. Therefore, source code modification is very limited.

Due to large number of data files, and due to complicated data process, software is developed to do the offline design. In the design software, metallurgic models and manufacturing knowledge is integrated, such as those related to equipment, process, entry material, automation, etc.

This type of software is beyond the reach of pure software engineers. It would also be hard for non-software engineer to integrate such data and learning in the large software such as Level 2 system.



<To Be Continued>

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