Explainable Steel Quality Prediction System Based on Gradient Boosting Decision Trees

Abstract

The steelmaking industry is one of the most energy-intensive industries and is responsible for 4% of the world's total greenhouse gas emissions. Solutions to improve operational efficiency can therefore bring major improvements to the overall environmental performance of the entire industry. This article proposes a novel steel quality prediction system based on gradient boosting trees that can be used to predict the quality of steel products during manufacturing. The prediction system enables the detection of possible surface defects in the early phase of the manufacturing process, thus avoiding costly and time-consuming manufacturing efforts to address defective products. In this study, we trained a prediction model with data collected from an SSAB Europe steelmaking plant in Raahe, Finland. From the 296 process parameters measured in the liquid steel stage of steelmaking, we selected 89 input features to train and test the prediction model. The model was then integrated into a quality monitoring tool (QMT) to utilize real-time manufacturing data in its predictions. The validation process showed that the prediction model can find more than 50% of defective steel products by marking only about 10% of the steel products as potentially at risk of surface defects in plate rolling. This can potentially save time in the quality control phase and improve process efficiency. To gain more insights into the model predictions, we used SHAP (SHapley Additive exPlanations) to find a potential connection between the process input parameters and surface defects.