基于随机森林和聚类的连铸坯纵裂纹预报方法

doi:10.13228/j.boyuan.issn1005-4006.20220059

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Abstract Longitudinal crack is a common surface defect of casting slab. Accurate prediction of longitudinal cracking on slab surface is of great significance for improving the quality of casting slab. Aiming at the temporal and spatial variation trends of the temperature of the mold thermocouple during the formation and propagation of longitudinal cracks, this paper captures and extracts the typical variation features of the thermocouple temperature in time series, and the Random Forest(RF) algorithm is used to reduce the dimension of the captured features, and the features closely related to longitudinal cracks are extracted. On this basis, a longitudinal crack detection model based on K-means(K Means) clustering was established. The results show that the proposed longitudinal crack prediction model based on temperature-time series features and clustering algorithm can correctly distinguish and identify samples with longitudinal cracks from samples under normal conditions, which provides feasible way for introducing machine learning methods into abnormal monitoring of continuous casting process.

Key words： longitudinal crack random forest K-means clustering feature dimension reduction mold continuous casting slab

Received: 28 April 2022

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	Articles by authors
	ZHANG He
	DUAN Hai-yang
	WANG Xu-dong
	YAO Man

Cite this article:

ZHANG He,DUAN Hai-yang,WANG Xu-dong等. Longitudinal crack prediction method of continuous cast slab based on random forest and clustering[J]. CONTINUOUS CASTING, 2022, 41(6): 21-28.

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http://www.chinamet.cn/Jweb_lz/EN/10.13228/j.boyuan.issn1005-4006.20220059 OR http://www.chinamet.cn/Jweb_lz/EN/Y2022/V41/I6/21

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