Deep learning-based rolling mill inlet anomaly recognition method

Abstract

The invention provides a rolling mill inlet steel biting abnormity recognition method based on deep learning. The method comprises the steps that an abnormity recognition model is acquired according to a billet sample image of a rolling mill inlet; inputting the real-time billet image of the inlet of the rolling mill into the anomaly recognition model, obtaining the moving state of the billet at the inlet of the rolling mill, and obtaining anomaly information according to the moving state; the method can effectively avoid a series of problems of manual participation, accurately identify the abnormality and effectively guarantee the quality of the steel billet.

Description

technical field [0001] The invention relates to the field of iron and steel metallurgy, in particular to a deep learning-based method for identifying abnormality at the entrance of a rolling mill. Background technique [0002] In the smelting process in the field of iron and steel metallurgy, it is necessary to use a rolling mill to perform hot rolling and cold rolling on steel, and a rolling operator is required to operate the input and output roller tables of the rolling mill. In the process of hot rolling and cold rolling, the steel billet will be stuck and stagnant, that is, the steel bite at the entrance of the rolling mill is abnormal. Once the abnormal steel bite at the entrance of the rolling mill occurs, it must be dealt with in time to avoid affecting the overall work efficiency. However, at present, most of the abnormal monitoring of the entrance of the rolling mill relies on manual work, which is not only inefficient, but also easily leads to some abnormalities t...

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Problems solved by technology

[0003] In view of the above existing problems in the prior art, the present invention proposes a deep learning-based identification method for steel bite abnormality at the entrance of the rolling mill, which mainly solves the problem that the identification of abnormality at the entrance of the rolling mill relies on manual work and the identification efficiency is low

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