Prediction of mechanical properties of hot-rolled strip steel based on PCA-GBDT method

Abstract

Improving the quality of rolled steel products is the primary task of the entire steel industry. As the main step of rolled steel production, hot rolling has received extensive attention. As far as the hot rolling process is concerned, the chemical composition of steel and related process parameters are the most direct factors affecting the quality of hot rolled steel sheets. Based on Principal Component Analysis (PCA) and Gradient Lifting Decision Tree (GBDT) methods, this paper takes the tensile strength as the research object and constructs a prediction model for the mechanical properties of hot rolled strip steel. Through principal component analysis of characteristic data, 28 variables were reduced into 8 new indicators to be used for GBDT regression analysis. 2489 pieces of data were divided into a training set and a test set at a ratio of 7:3, to be used in the training set to build a regression model, and get a root mean square error of 16.7393. The data in the test set was used to predict the tensile strength value, with the root mean square error reaching 18.2650.