Prediction of mechanical properties of cold rolled strip based on improved extreme random tree

doi:10.1007/s42243-022-00815-2

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Abstract Taking the 2130 cold rolling production line of a steel mill as the research object, feature dimensionality reduction and decoupling processing were realized by fusing random forest and factor analysis, which reduced the generation of weak decision trees while ensured its diversity. The base learner used a weighted voting mechanism to replace the traditional average method, which improved the prediction accuracy. Finally, the analysis method of the correlation between steel grades was proposed to solve the problem of unstable prediction accuracy of multiple steel grades. The experimental results show that the improved prediction model of mechanical properties has high accuracy: the prediction accuracy of yield strength and tensile strength within the error of ± 20 MPa reaches 93.20% and 97.62%, respectively, and that of the elongation rate under the error of ± 5% has reached 96.60%.

Key words： Cold strip rolling Mechanical property prediction Extreme random tree Factor analysis Random forest Correlation analysis Steel grade

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	Articles by authors
	Yun-bao Zhao
	Yong Song
	Fei-fei Li
	Xian-le Yan

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Yun-bao Zhao,Yong Song,Fei-fei Li, et al. Prediction of mechanical properties of cold rolled strip based on improved extreme random tree[J]. Journal of Iron and Steel Research International, 2023, 30(02): 293-304.