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%.

以某钢厂2130冷轧生产线为研究对象，通过融合随机森林和因子分析实现特征降维和解耦处理，在保证弱决策树多样性的同时减少了弱决策树的生成。基础学习器采用加权投票机制代替了传统的平均方法，提高了预测精度。最后，针对多个钢种预测精度不稳定的问题，提出了钢种间相关性分析方法。实验结果表明，改进后的力学性能预测模型精度较高：在±20 MPa误差范围内，屈服强度和抗拉强度的预测精度分别达到93.20%和97.62%，