钢管2026，Vol.55Issue(2)：15-22,8.DOI:10.19938/j.steelpipe.1001-2311.2026.2.15.22
粒子群算法优化随机森林模型的无缝钢管穿孔关键参数预测
Prediction of Key Parameters for Seamless Steel Tube Piercing with Particle Swarm Optimization-based Random Forest Model
王俊东 1王晓晨 2周锦波 1杨荃 2李京栋2
作者信息
- 1. 北京科技大学工程技术研究院,北京 100083
- 2. 北京科技大学工程技术研究院,北京 100083||北京科技大学国家板带生产先进装备工程技术研究中心,北京 100083
- 折叠
摘要
Abstract
A prediction method using a random forest(RF)model optimized by particle swarm optimization(PSO)is proposed to achieve accurate prediction of key parameters in the seamless steel tube piercing process.Firstly,the produ-ction characteristic data are preprocessed,including missing value processing,standardization and one-hot encoding for categorical features.Then,random forest model is used to model the multi-output variables,and the random forest hyper-parameters are optimized by PSO so as to improve the prediction accuracy of the model.The experimental results show that the PSO-optimized random forest model is superior to other models in the prediction of roll gap,guide plate gap and plug advance,resulting in a significant reduction of mean squared error and mean absolute error,and improvement in coefficient of determination R2.The proposed method can provide a scientific decision-making basis for the seamless steel tube piercing process.关键词
无缝钢管/穿孔/参数预测/随机森林/粒子群优化Key words
seamless steel tube/piercing/parameter prediction/random forest/particle swarm optimization引用本文复制引用
王俊东,王晓晨,周锦波,杨荃,李京栋..粒子群算法优化随机森林模型的无缝钢管穿孔关键参数预测[J].钢管,2026,55(2):15-22,8.
