| About this Abstract |
| Meeting |
13th International Conference on the Technology of Plasticity (ICTP 2020)
|
| Symposium
|
13th International Conference on the Technology of Plasticity (ICTP 2020)
|
| Presentation Title |
Development of Numerical Model Based Deep Learning for the Roll Force Prediction at the Sendzimir Mill |
| Author(s) |
Yong Seok Cho, Jea Sook Chung |
| On-Site Speaker (Planned) |
Yong Seok Cho |
| Abstract Scope |
The Sendzmir Mill(ZRM) is the cold rolling mill to produce the stainless steels and the electrical steels. At the beginning of the rolling, the roll force set-up was performed for the desired thickness of the strip. If the set value of the roll force is not correct for the outlet thickness, the thickness deviation is high, furthermore the strip is broken during rolling. For the ZRM process, sound prediction of the roll force is vital for achieving the desired thickness because the stability of the rolling substantially affected by it.
In this presentation, mathematical model is presented for the prediction of the roll force at the beginning of the rolling. The model consists of a numerical model for the prediction of the roll force, a sub-model for the prediction of the mechanical property of the strip by the deep learning, which is the deep neural networks. |
| Proceedings Inclusion? |
Definite: At-meeting proceedings |