Prediction and analysis of mechanical properties of hot-rolled strip steel based on an interpretable machine learning

Abstract

Establishing a low-cost, high-precision predictive model for mechanical properties is crucial for enhancing the properties of steel. Due to the complexity of the steel production process, traditional mechanistic models struggle to efficiently and accurately describe the relationships among alloy elements, processes, and properties. In response to this, the paper introduces a high-precision framework for predicting and optimizing mechanical properties through feature engineering and machine learning models. Firstly, an effective dimension reduction of features is achieved through a weighted fusion heterogeneous feature selection strategy, thereby identifying the optimal model input features. Subsequently, an improved seagull optimization algorithm is utilized to optimize the hyperparameters of Extreme Gradient Boosting, further enhancing the predictive accuracy of the model. Moreover, based on the Shapley Additive Explanation method, a quantitative analysis of the model's predictive outcomes is conducted, elucidating the impacts of alloy elements and processes on mechanical properties, consistent with established principles of physical metallurgy. The proposed framework not only enables precise prediction of mechanical properties in steel but also provides theoretical guidance and technical support for process optimization design and the development of new steel grades.