Physical Metallurgy Guided Industrial Big Data Analysis System with Data Classification and Property Prediction

Various computational analysis systems based on machine learning (ML) methods have been established for the analysis of steel industrial data. However, limited by the extensibility of one regression strategy, it is difficult to obtain a generic property prediction model for multiple types of steels. To solve this problem, this study proposes a novel industrial big data analysis system that combines ML classification and regression models with key physical metallurgy (PM) variables. First, the database is obtained from an industrial production line and carefully preprocessed. Then, multiple types of steels are categorized into five classes using a K-nearest neighbor (KNN) algorithm, and suitable ML algorithms are selected for each category to maximize the performance. Considering the role of PM variables in improving the model accuracy, some relevant parameters (the Ac1 temperature, Ac3 temperature, and flow stress) are introduced to guide the further optimization of the ML process. The proposed industrial analysis system has more accurate prediction and higher flexibility than the model that directly uses the original dataset. With a rational combination of different regression strategies, the present results clearly demonstrate that the extensibility of the proposed property prediction model is significantly improved for industrial big data.