Interpretable Prediction of Mechanical Properties for Hot‐Rolled Seamless Steel Pipes: Combining Machine Learning with Shapley Additive Explanation

In order to improve the efficiency of mechanical performance testing of seamless steel pipes, enhance product quality, and reduce the cost of new product development, a machine learning‐based prediction model for the mechanical properties of hot continuous rolling seamless steel pipes is proposed. Specifically, a Bayesian optimization optimized categorical boosting (CatBoost) algorithm is employed to train on production data of 723 seamless steel pipes from a 460 mm steel plant. Its predictive performance is compared with other machine learning models, including support vector regression, random forest, and extreme gradient boosting. Furthermore, the Shapley additive explanations method is used for post hoc interpretability analysis of the prediction model. Results demonstrate that the BO‐CatBoost model achieves superior performance, offering higher prediction accuracy. Interpretability analysis further reveals that elongation is primarily influenced by diameter and the contents of C, V; yield strength is mainly affected by rolling temperature and the contents of MN, V; while rolling temperature, sizing temperature, and thickness are the key factors affecting tensile strength. Finally, a prediction system based on the proposed method is developed and is applied in industrial production.