鉄と鋼
Online ISSN : 1883-2954
Print ISSN : 0021-1575
ISSN-L : 0021-1575
論文
機械学習を活用した連続鋳造機の浸漬ノズル形状最適化
難波 時永 岡田 信宏
著者情報
ジャーナル オープンアクセス HTML

2023 年 109 巻 6 号 p. 513-524

詳細
抄録

In continuous casting, molten steel is fed from the tundish into the mold through the immersion nozzle. In the immersion nozzle, inclusions mainly composed of alumina present in the molten steel adhere and accumulate, it causes limitation of continuous castings. To prevent the nozzle clogging, Ar gas is blown into the immersion nozzle. However, Ar bubbles flow into the mold along with the molten steel and become trapped in the solidifying shell, causing bubbling defects of the slab. To suppress bubbling defects, it is effective to keep Ar bubbles away from the solidification interface or to use molten steel to wash away Ar bubbles that have adhered to the solidification interface. The molten steel flow in the mold is greatly affected by the shape of the immersion nozzle. In this paper, we consider the optimization of the shape of the immersion nozzle to reduce Ar bubbles trapped in the solidifying shell. A numerical model of molten steel flow and heat transfer solidification in the mold is combined with an optimization method. In the optimization process, Ar bubbles trapped in the solidifying shell are evaluated by a neural network to improve the calculation speed. The application of this method to the search for immersion nozzle shape is also reported, and the effectiveness of the obtained nozzle shape in reducing Ar bubbles is discussed.

Fullsize Image
著者関連情報
© 2023 一般社団法人 日本鉄鋼協会

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs license.
https://creativecommons.org/licenses/by-nc-nd/4.0/
前の記事 次の記事
feedback
Top