Transactions of Materials and Heat Treatment

Title：Strip convexity prediction based on hybrid kernel support vector machine

Authors： Liu Wenguang1 Li Zixuan2 Xie Tianwei3 Zhou Yaluo 2 Zhang Ruicheng2

Unit： 1.Shougang Jingtang Iron and Steel United Co. Ltd. Tangshan 063200 China 2.College of Electrical Engineering North China University of Science and Technology 3.Beijing Shougang Co. Ltd.

KeyWords： hybrid kernel support vector machine strip convexity hippopotamus algorithm hot rolling prediction accuracy

ClassificationCode：TP335.5

year,vol(issue):pagenumber：2025,50(7):132-142

Abstract：

In order to solve the problems of low prediction accuracy and poor generalization ability of hot-rolled strip convexity, a support vector machine (SVM) prediction model with a mixture of Gaussian kernel and polynomial kernel was proposed. For the problem that the parameters of the hybrid kernel support vector machine were difficult to determine, an improved hippopotamus optimization algorithm (IHO) was proposed to optimize the hybrid kernel parameters by using good point sets, incomplete gamma function adaptive weights and optional reverse learning strategy. Simulation experiment results show that the IHO algorithm has a fast optimization speed and high convergence accuracy. In the convexity prediction experiment, compared with the random forest, kernel extreme learning machine, single Gaussian kernel support vector machine and polynomial kernel support vector machine prediction models, the accuracy of the hybrid kernel support vector machine prediction model is improved by 18.49%, 15.75%, 28.76% and 10.27%, respectively, which is of great significance for achieving the accurate optimization of rolling parameters and effectively improving the defects such as plate edge waves and wedges.

Funds：

河北省自然科学基金资助项目(F2018209201)；唐山市科技局科技计划资助项目（22130213G)

作者简介：刘文广（1978-），男，硕士，高级工程师 E-mail：464710757@qq.com 通信作者：李子轩（2000-），男，硕士研究生 E-mail：2941512970@qq.com

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