Xu-Yuan Zhang, Xiao-Guang Zhou, Si-Qiao Wang, Xin-Yao Zhang, Si-Wei Wu, Guang-Ming Cao, Hong-Bing Wang, Chun-Yang Shu, Zhen-Yu Liu
Accurate determination of the relationship among composition, process, and properties is crucial for predicting the yield strength of HRB400E rebar and enhancing the stability of its yield strength. Four yield strength prediction models of HRB400E rebar built using the random forest (RF) algorithm are compared, and a reverse process design is conducted based on the optimal model. The first model is an RF model driven solely by industrial big data, the second is an RF model with optimized hyperparameters (optimized RF model), the third combines physical metallurgy (PM) with industrial big data (PM-RF model), and the fourth is a dual-driven model of optimized PM and industrial big data (optimized PM-RF model). In the establishment of the optimized RF and optimized PM-RF models, a dynamic hyperparameter optimization algorithm was introduced, employing the Optuna optimization framework to optimize the curve parameters of an improved particle swarm optimization (PSO) algorithm with sigmoid-like inertial weight (Optuna-S-PSO). During the establishment of the PM-RF and optimized PM-RF models, the newly introduced input parameters, including ferrite grain size (da) and the fraction of precipitated phases in ferrite (f pa), were calculated using the PM model. The results demonstrate that the application of the Optuna-S-PSO algorithm, along with the inclusion of PM parameters, significantly improves the models’ prediction accuracy. Among these, the optimized PM-RF model exhibited the highest yield strength prediction accuracy, with the coefficient of determination, root mean square error, and mean absolute error values of 0.856, 4.00 MPa, and 3.31 MPa, respectively. Based on this model, the SHapley Additive exPlanation (SHAP) method was used to comprehensively analyze the effects of composition, rolling parameters, and microstructure on the material’s yield strength. Ultimately, the yield strength fluctuation range of multi-specification HRB400E rebar is effectively reduced by reversely designing the rolling speed based on the optimized PM-RF model.
