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.

Investigative Analysis of Automatic Mode Detection for a Lubricant Base Oil Production Plant Using PCA and Machine-Learning Models

“…21 XGBoost has been used in anomaly detection to identify an unusual behavior in a range of systems, including computer networks, production lines, and transportation systems, 22 as well as for quality prediction method a variety of manufacturing processes, including the production of semiconductors and steel. 23 2.2.2. Random Forest Algorithm.…”

“…XGBoost has been used in fault diagnosis to find and identify faults in mechanical systems, such as bearings and turbines . XGBoost has been used in anomaly detection to identify an unusual behavior in a range of systems, including computer networks, production lines, and transportation systems, as well as for quality prediction method a variety of manufacturing processes, including the production of semiconductors and steel …”

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

“…Fang [16], Xin [17] 2014-2023 Prediction of molten steel temperature Zhou [18], Wang [19], Zang [20] 2022-2023 Prediction of oxygen demand Wang [21] 2017 Prediction of ladle furnace temperature Takalo-Mattila [22], Chen [23], Li [24], Wu [25], Zhao [26], Xie [27], He [28], Boto [29], Chen [30], Xu [31], Orta [32],…”

Survey on AI Applications for Product Quality Control and Predictive Maintenance in Industry 4.0

“…XGBoost successfully predicts manufacturing defects in [16,19]. In [60], a gradient boosting model is suggested for predicting steel product quality. In predictive maintenance, random forest models are used to predict the RUL of machines in [27,61].…”