Real-time Quality Prediction of Casting Billet Based on Random Forest Algorithm

摘要

Real-time quality prediction in continuous casting process is of great significance to the increase of production and the improvement of casting billet quality. The process parameters have a great influence on the quality of the billet in the continuous casting process, and the quantity distribution of the superior and inferior products in the casting billet is extremely unbalanced. Therefore, this paper proposes an intelligent prediction method for casting billet quality based on multi-process parameters. Based on the analysis of the relationship between multiple process parameters and casting billet quality, a casting quality prediction model based on weighted random forest (WRF) algorithm was established. This algorithm solves the sample imbalance problem by weighting the decision tree results effectively, and can correctly identify negative samples. Based on real-time casting billet data in the production process, results of the case prove the effectiveness of the proposed method.