基于特征选择的NGO-RF热轧H型钢水平辊轧制力预测  

Prediction on rolling force of horizontal roller of NGO-RF hot-rolled H-beams based on feature selection

作　　者：臧德宇 吴龙[2] 林太阳 潘建洲 Zang Deyu;Wu Long;Lin Taiyang;Pan Jianzhou(School of Mechanical and Electronic Engineering,Fujian Agriculture and Forestry University,Fuzhou 350002,China;School of Mechanical and Electronic Engineering,Sanming University,Sanming 365004,China;School of Material Science and Engineering,University of Science and Technology Beijing,Beijing 100083,China;Fujian Sansteel(Group)Co.,Ltd.,Sanming 365000,China)

机构地区：[1]福建农林大学机电工程学院,福建福州350002 [2]三明学院机电工程学院,福建三明365004 [3]北京科技大学材料科学与工程学院,北京100083 [4]福建三钢(集团)有限公司,福建三明365000

出　　处：《锻压技术》2025年第1期122-133,共12页Forging & Stamping Technology

基　　金：福建省科技厅“揭榜挂帅”成果转化项目(2023T5001);福建省科技重大专项(2022HZ026025)。

摘　　要：为了得到较为精确的水平辊轧制力,收集福建罗源闽光钢铁轧钢厂的实际轧制参数,并进行相关参数计算与预处理,构建包含多输入特征及多规格的H型钢水平辊轧制力数据集。为有效预测H型钢的水平辊轧制力,首先,运用孤立森林算法和树模型进行离群点检测与特征选择;其次,划分数据集并采用随机森林模型作为基础模型进行训练与验证;再次,应用北方苍鹰优化算法优化随机森林模型;最后,输入处理后的H型钢水平辊轧制力测试集数据,输出轧制力预测值。将所建模型(NGO-RF)与未经优化的随机森林模型、支持向量机模型、多层感知神经网络模型、卷积神经网络模型,以及经过北方苍鹰优化算法优化的支持向量机模型和多层感知神经网络模型对比,结果显示,所建模型在预测性能上优于上述所有模型,具有较高的准确性与适用性。此外,利用所建模型对H型钢588 mm×300 mm×12 mm×20 mm新规格产品的轧制力进行预测,对比模型预测值与实测值,平均误差仅为6.05%,进一步证实了所建模型能够较好地实现对H型钢水平辊轧制力的预测。In order to obtain more accurate rolling force of horizontal roller,the actual rolling parameters of Fujian Luoyuan Minguang Iron and Steel Rolling Mill were collected,and the calculation and preprocessing of relevant parameter were performed to construct a rolling force dataset of horizontal roller for H-beam containing multi-input features and multiple specifications.To effectively predict the rolling force of horizontal roller for H-beam,firstly,the outlier detection and feature selection were conducted by isolation forest algorithm and tree model,and the dataset was divided,using random forest model as the base model for training and validation.Next,the random forest model was optimized by northern goshawk optimization algorithm.Furthermore,the processed test set data of rolling force for H-beam horizontal roller was inputted,and the predicted rolling force values were output.In addition,the constructed model(NGO-RF)was compared with the unoptimized random forest model,support vector machine model,multi-layer perceptron model,convolutional neural network model,as well as support vector machine model and multi-layer perceptron neural network model optimized by northerm goshawk optimization algorithm.The results show that the constructed model outperforms all models mentioned above in terms of performance prediction,and it has high accuracy and adaptation.Additionally,the rolling force of new H-beam 588 mm×300 mm×12 mm×20 mm specification products are predicted by using the constructed model.Comparing the predicted values of the model with the actual measured values,the average error is only 6.05%,further confirming that the constructed model can effectively predict the rolling force of H-beams horizontal roller.

关 键 词：H型钢 水平辊轧制力 随机森林 北方苍鹰优化算法 特征选择 

分 类 号：TG335.42[金属学及工艺—金属压力加工]