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OA

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.